diff --git a/.gitattributes b/.gitattributes index c80c1d244965d5f7772a08bd32ff0d8905074963..d5259367f2e9a5ea39399ee6b74897b4133a25f5 100644 --- a/.gitattributes +++ b/.gitattributes @@ -234,3 +234,4 @@ data_all_eng_slimpj/shuffled/split/split_finalad/part-14.finalad filter=lfs diff data_all_eng_slimpj/shuffled/split/split_finalaa/part-13.finalaa filter=lfs diff=lfs merge=lfs -text data_all_eng_slimpj/shuffled/split/split_finalaa/part-16.finalaa filter=lfs diff=lfs merge=lfs -text data_all_eng_slimpj/shuffled/split/split_finalaa/part-12.finalaa filter=lfs diff=lfs merge=lfs -text +data_all_eng_slimpj/shuffled/split/split_finalaa/part-03.finalaa filter=lfs diff=lfs merge=lfs -text diff --git a/data_all_eng_slimpj/shuffled/split/split_finalaa/part-03.finalaa b/data_all_eng_slimpj/shuffled/split/split_finalaa/part-03.finalaa new file mode 100644 index 0000000000000000000000000000000000000000..658e037cc086695c9f2515f3705eece458410d02 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split/split_finalaa/part-03.finalaa @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:08e4ebfaeddc9e9660b90d1952cdcdcd03017307f0263e2fb79eaa0affed371e +size 12576630116 diff --git a/data_all_eng_slimpj/shuffled/split2/finalzkku b/data_all_eng_slimpj/shuffled/split2/finalzkku new file mode 100644 index 0000000000000000000000000000000000000000..c6c6ba0b7996135436cf34ab5dd3e7efc139f528 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzkku @@ -0,0 +1,5 @@ +{"text":"\\section{The Mu2e Experiment}\n\\label{sec:intro}\n\nThe Mu2e experiment \\cite{TDR} at Fermilab will search for the\ncharged-lepton flavor violating neutrino-less coherent conversion\nof a negatively charged muon into an electron in the field of an\naluminum nucleus. The process produces a mono-energetic electron\nwith an energy slightly below the muon rest mass (104.967 MeV). If\nno events are observed, Mu2e will set a\nlimit on the ratio between the conversion rate and the muon capture\nrate of $R_{\\mu e}$~$\\leq 8.4\\ \\times\\ 10^{-17}$ (@ 90$\\%$ C.L.).\nThis will improve the current limit \\cite{Sindrum-II} by four\norders of magnitude.\nOn the other hand, an observation of Charge Lepton Flavour Violation\n(CLFV) events will provide a clear indication of New Physics (NP)\nbeyond the Standard Model up to mass scales of nearly $10^4$ TeV,\nfar beyond the direct search reach at colliders, complementing and\nextending other CLFV searches on a wide range of NP scenarios\n\\cite{CLFV-theory}.\n\nThe Mu2e design is based on the MELC concept \\cite{MELC}. An\nintense pulsed muon beam ($\\sim 10^{10} \\mu\/$sec) is produced by\n8 GeV, 8 kW protons hitting a tungsten target and it is stopped\non an aluminum target after travelling inside a very long, curved\nseries of solenoids (Fig.~\\ref{Fig:Mu2e}).\nThe strong negative gradient of the Production Solenoid, from 4 to\n2.5 T, confines soft pions and increases the yield through magnetic\nreflection. The S-shaped Transport Solenoid efficiently transfers\nlow energy, negatively charged particles while allowing a large\nfraction of pions to decay into muons.\nThe Detector Solenoid has a graded field from 2 to 1 Tesla in\nthe upstream region of the stopping target to increase acceptance\nfor Conversion Electron (CE) events. \n\n\\begin{figure}[ht]\n \\begin{center}\n \\includegraphics[width=1.0\\textwidth]{mu2e_layout}\n \\end{center}\n \\caption{The Mu2e experiment. Cosmic Ray Veto and Stopping\n Target Monitor are not shown.}\n \\label{Fig:Mu2e}\n\\end{figure}\n\nThe Mu2e detector, just downstream of the aluminum target inside\na 1T solenoid, is composed of a tracker and an electromagnetic\ncalorimeter. The Mu2e tracker measures the momentum of the conversion\nelectron and separates it from the background. The crystal\ncalorimeter plays an important role in providing particle\nidentification capabilities and a fast online trigger filter, while\nalso aiding the track reconstruction capabilities. The detector\nsolenoid is in vacuum, at $10^{-4}$ Torr, and in a high radiation\nenvironment. The entire detector region and part of the transport\nsolenoid are surrounded by a Cosmic Ray Veto (CRV) that reduces the\ncosmic ray background.\nA High Purity Germanium Detector and a Lanthanum Bromide crystal\nconstitute the Stopping Target Monitor, placed $\\sim 35$ m after\nthe stopping target, which provides normalization to CLFV events by\ndetecting $\\gamma$-rays emitted from muon capture in the aluminum\ntarget.\n\nIn order to reach the required sensitivity, control of the background\nto the level of less than 0.5 expected events is required.\nThe background coming from the\nbeam is reduced by means of a pulsed beam structure with a proton\nextinction lower than 10$^{-10}$: a delay in the start of the live\nwindow of $\\sim$ 700 ns after the bunch arrival time removes the\nprompt background from the acquired data.\nThe extinction level is monitored by detecting scattered protons from\nthe production target to evaluate the fraction of out-of-time beam.\n\n\\section{The tracking system}\n\\label{sec:tracker}\n\nThe Mu2e tracker system \\cite{Tracker} is designed to maximize\nacceptance for conversion electrons while minimizing the contamination\nfrom the muon Decay-In-Orbit (DIO) background, where nuclear\nmodifications push the DIO spectrum towards the CE signal\n(Fig.~\\ref{Fig:tracker} left). Energy loss and detector resolution\nproduce an overlap of the two processes. The selected design is based\non nearly 20,000 low mass straw drift tubes of 5 mm in diameter,\nwith a 15 $\\mu$m Mylar wall and 25 $\\mu$m sense wire.\nStraws of lengths ranging from 430 to 1220 mm are oriented transversely\nto the solenoid axis and arranged in 18 stations (Fig.~\\ref{Fig:tracker}\nright), for a total length of 3.2 metres along the solenoid axis. A\ncentral hole, 38 cm in diameter, makes the device blind to low momentum\nbackground particles ($p<55$ MeV\/c) which are constrained to low radius\nby the solenoidal field.\n\n\\begin{figure}[!th]\n \\centering\n \\begin{tabular}{cc}\n \\includegraphics[width=0.5\\textwidth]{dio_nores} &\n \\includegraphics[width=0.5\\textwidth]{trk-station.png} \\\\\n \\end{tabular}\n \\caption{Left: energy spectrum for electrons produced from\n free muon decays (blue), muon decays in orbit (red) and\n conversion electrons (purple). Right: Sketch of the Mu2e\n straw tracker system. The basic element is the panel, where\n straws are organized in two staggered layers. Six panels\n arranged as shown in in the middle figure above form a plane;\n two planes rotated by $30^\\circ$ constitute a station, right.\n The tracker, containing 18 stations, is 3.2 meters long.}\n \\label{Fig:tracker}\n\\end{figure}\n\nAn eight channel tracker prototype was built and tested with\ncosmics rays to measure performances and tune detector simulations.\nIn Fig.~\\ref{Fig:trk-proto}, the position resolution and straw\nefficiency are compared with Monte Carlo expectations.\nGood reproducibility of data is observed.\n\n\\begin{figure}[!th]\n \\vspace{0.3cm}\n \\centering\n \\includegraphics[width=1.0\\textwidth]{prototypedata3.png}\n \\caption{Longitudinal (left), transverse (center) position\n resolution and efficiency (right) for an eight channel\n prototype of the tracker.\n Data from minimum ionizing particles (blue triangles) are\n compared with Monte Carlo simulation (red crosses). \n Resolution is extracted with Gaussian fits to the spectra.}\n \\label{Fig:trk-proto}\n\\end{figure}\n\nThe tracker performance is studied with Monte Carlo using the\nfull Mu2e simulation. Results are reported in Fig.~\\ref{Fig:trk-reso}.\nThe core momentum resolution of 159 keV\/c is well within physics\nrequirements and stable when increasing accidental hit rate.\nThe total track efficiency of $\\sim 9\\%$ is fully dominated by\ngeometric acceptance.\n\n\\begin{figure}[!th]\n \\vspace{0.3cm}\n \\centering\n \\includegraphics[width=0.5\\textwidth,height=6cm]{trk-reso}\n \\caption{Momentum resolution evaluated with the fully tuned\n Mu2e simulation.}\n \\label{Fig:trk-reso}\n\\end{figure}\n\nAt the moment of writing, twelve pre-production panels are under\nconstruction and testing. In Fig.~\\ref{Fig:trk-plane}, three\npanels are assembled to form a tracking plane.\nA vertical slice test on fully instrumented panels with the entire\nFront-End Electronics chain will be performed.\n\n\\begin{figure}[!th]\n \\centering\n \\includegraphics[width=\\textwidth]{3panels.png}\n \\caption{Tracking plane being assembled with pre-production\n panels.}\n \\label{Fig:trk-plane}\n\\end{figure}\n\n\\section{The calorimeter system}\n\\label{sec:calo}\n\nThe Mu2e calorimeter \\cite{TDR-EMC} has to provide confirmation for\nCE signal events, a powerful $e\/\\mu$ separation - with a muon\nrejection factor of $\\sim 200$, a standalone trigger and seeding for\ntrack reconstruction. An energy resolution of $O(10\\%$) and a time\nresolution of $500$ ps for 100 MeV electrons are sufficient to fulfil\nthese requirements.\nThe calorimeter design consists of two disks made from 674 undoped\nCsI scintillating crystals with ($34\\times 34\\times 200$) mm$^3$\ndimension. Each crystal is read-out by two custom array large area\n($2\\times 3$ of $6\\times 6$ mm$^2$ cells) UV-extended Silicon\nPhoto-Multipliers (SiPMs). Each SiPM is connected to a Front-End\nElectronics (FEE) board providing amplification and shaping of the\nsignal. Groups of 20 signals are sent to a custom digitizer module\n(DIRAC, DIgitizer and ReAdout Controller) where they are sampled at\n200 Mega samples per second and transferred to the Mu2e data acquisition\nsystem. A radioactive source and a laser system allow setting the energy\nscale and monitor the fast changes of response and resolution.\nThe crystals will receive an ionizing dose of 90 krad and a fluence\nof $3\\times10^{12}$ n\/cm$^2$.\nThe photosensors,\nbeing shielded by the crystals, will get a three times smaller dose.\nThe layout of the calorimeter system and pictures of crystals and a\nreadout channel are shown in Fig.~\\ref{Fig:calo}.\n\n\\begin{figure}[!th]\n \\begin{center}\n \\includegraphics[width=1.0\\textwidth]{mu2e_calo.png}\n \\end{center}\n \\caption{Left: sketch of the calorimeter system. The cooling\n pipes and the on board racks containing the DIRAC boards\n are visible. Right: pure CsI calorimeter crystals (top) and\n a readout channel, composed by two UV-extended SiPMs and the\n corresponding analog FEE boards (bottom).}\n\\label{Fig:calo} \n\\end{figure}\n\nA long R\\&D phase with small prototypes demonstrates that the calorimeter\ndesign easily satisfies the requirements\n\\cite{NIM-LYSO1,NIM-LYSO2,NIM-BaF2,Proto-EMC}.\nPre-production components have been used to build a large size calorimeter \nprototype, Module-0 (Fig.~\\ref{Fig:emc-mod0}), with 51 crystals and 102\nSiPMs and front end boards \\cite{Module-0}. It represents a portion of the\nfinal disk and has been used to test the integration and assembly procedures\nand to evaluate the operations of running in vacuum and at low temperatures.\nModule-0 performance was tested with an electron beam of 60-120 MeV at the\nINFN Beam Test Facility in Frascati \\cite{BTF}. The energy distribution for\n100 MeV electrons is well reproduced by the calorimeter simulation,\nFig.~\\ref{Fig:calo-testbeam} left. Energy and time resolution are evaluated\nwith particles impinging on the calorimeter surface both at 0 and 50 degrees.\nThe latter is the expected incidence angle for conversion electrons in Mu2e.\nAn energy resolution of 5\\% (7\\%) and a time resolution of 120 ps (150 ps)\nare obtained for 100 MeV particles impinging at $0^{\\circ}$ ($50^{\\circ}$),\nFig.~\\ref{Fig:calo-testbeam} center and right. Results satisfy\nphysics requirements and are well reproduced by simulation.\n\n\\begin{figure}[!th]\n \\vspace{0.3cm}\n \\centering\n \\begin{tabular}{cc}\n \\includegraphics[width=0.5\\textwidth]{mod0-front} &\n \\includegraphics[width=0.5\\textwidth]{mod0-testbeam} \\\\\n \\end{tabular}\n \\caption{Module-0, a large size prototype of the Mu2e calorimeter.\n Left: front view before mounting the source panel, where the\n staggered crystal structure is visible. Right: rear side, with\n readout channels and cooling circuit.}\n \\label{Fig:emc-mod0}\n\\end{figure}\n\n\\begin{figure}[!t]\n \\centering\n \\begin{tabular}{ccc}\n \\includegraphics[width=0.33\\textwidth]{calo_ene} &\n \\includegraphics[width=0.33\\textwidth]{calo_eres} &\n \\includegraphics[width=0.33\\textwidth]{calo_tres} \\\\\n \\end{tabular}\n \\caption{Calorimeter performance evaluated with a large-scale prototype,\n Module-0, using 60-120 MeV electron beam. Left: data-MC comparison of\n the energy distribution for 100 MeV beam. Energy (center) and time\n (right) resolution for orthogonal and 50$^\\circ$ impinging electrons.\n The energy resolution is compared with results expected from simulated\n data.}\n \\label{Fig:calo-testbeam}\n\\end{figure}\n\nThe complete production components for SiPMs and 85\\% of production\ncrystals have been received and characterized. For all of the 4000\nsensors, the breakdown voltage and the dark current are measured\nat different temperatures. The spread of these quantities over\nthe six cells of each sensor is used as quality control parameter\n(Fig.~\\ref{Fig:calo-qc} bottom). The overall rejection factor is\n1.2\\%, dominated by those sensors whose dark current RMS is too large.\nThe Quality Control of CsI crystals foresees a dimensional control,\nwith 0.1 mm tolerance with respect to nominal values, and a\nmeasurement of the optical properties \\cite{QAcrystals}.\nIn Fig.~\\ref{Fig:calo-qc} (top) the number of photoelectrons and\nthe uniformity response along the crystals are reported for both\nof the CsI producers. About 10\\% of the crystals have been rejected,\nmostly due to problems with mechanical tolerances.\nIrradiation tests have been carried out for small CsI and SiPM\nproduction subsamples. Results show that the calorimeter will be\nable to operate at the end of the Mu2e lifetime at a temperature\nbelow $0^\\circ$ C.\nMean Time To Failure tests on photosensors demonstrate an MTTF\nvalue 10 times larger than the experiment needs.\n\n\\begin{figure}[!t]\n \\centering\n \\includegraphics[width=\\textwidth]{calo-qa}\n \\caption{Summary of Quality Control measurements for production\n CsI crystals and Silicon Photo-Multipliers: CsI light yield\n (top left) and longitudinal response uniformity (top right);\n RMS of the breakdown voltage (bottom left) and of the dark\n current over the six cells of SiPMs (bottom right).\n Vertical lines represent the Quality Control acceptance cuts.}\n \\label{Fig:calo-qc}\n\\end{figure}\n\nThe prototypes of FEE and DIRAC have been exposed to a large ionization\ndose and neutron fluence to qualify rad-hard components. A slice test\nwith the whole calorimeter electronic chain provides results comparable\nto those achieved using a commercial digitizer. A DIRAC prototype is\ncurrently used to read 16 channels of Module-0.\n\n\\section{Cosmic Ray Veto}\n\\label{sec:crv}\n\nIn absence of the vetoing system, cosmic ray muons interacting with\nthe detector materials produce false signal CE candidates at a rate\nof approximately one\/day. In order to maintain the background under\nthe required level, the CRV has to provide a vetoing efficiency of at\nleast 99.99\\% for cosmic ray tracks while withstanding an intense\nradiation environment.\nThe Cosmic Ray Veto system \\cite{CRV} is made by four staggered layers\nof extruded plastic scintillation counters with two embedded 1.4 mm\ndiameter Wavelength Shifting Fibers\/counter, alternated with absorber\nslabs (Fig.~\\ref{Fig:crv-design}).\nEach fiber is readout by means of 2$\\times$2 mm$^2$ SiPMs. To achieve\nthe required coverage, a total of 5,504 counters are needed, organized\nin 86 modules of six different lengths for a total surface coverage of\n327 m$^2$.\n\n\\begin{figure}[!th]\n \\vspace{0.5cm}\n \\centering\n \\begin{tabular}{cc}\n \\includegraphics[width=0.5\\textwidth]{crvnew.png} &\n \\includegraphics[width=0.5\\textwidth]{crv_layout} \\\\\n \\end{tabular}\n \\caption{Left: the Mu2e Cosmic Ray Veto system, covering\n the detector solenoid and part of the transport solenoid.\n Right: layout of a CRV module.}\n \\label{Fig:crv-design}\n\\end{figure}\n\nMeasurements on a full size prototype with 120 GeV protons in the\nFermilab test beam area was carried out (Fig.~\\ref{Fig:crv-testbeam})\ndemonstrating that the needed light yield can be reached: the number\nof photo-eletrons obtained at 1 meter from the readout end provides\na safety factor of $\\sim 40\\%$ with respect to the requirements\n\\cite{CRVnpe}.\nIn Fig.~\\ref{Fig:crv-pe} test beam results are compared with the\nresults obtained from the CRV counter simulation, which includes\nscintillation and Cerenkov photon production\/transport, SiPM and\nelectronics responses. Good agreement is obtained after tuning\nthe Monte Carlo parameters.\nIrradiation of CRV SiPMs with neutrons was also tested to\nunderstand the maximum level of fluence acceptable for operations\n\\cite{CRVradhard}: neutrons could deteriorate the sensors response\nand increase the detector occupancy and dead-time so that shielding\nis mandatory.\n\n\\begin{figure}[!t]\n \\centering\n \\includegraphics[width=\\textwidth]{crvtestbeam.png}\n \\caption{Set-up of the CRV test beam. Protons are tracked with\n multi-wire proportional chambers. Front-End Boards (FEB) are\n visible on the top of the counter.}\n \\label{Fig:crv-testbeam}\n\\end{figure}\n\n\\begin{figure}[!t]\n \\vspace{0.5cm}\n \\centering\n \\begin{tabular}{cc}\n \\includegraphics[width=0.5\\textwidth]{crvtestbeampe} &\n \\includegraphics[width=0.5\\textwidth]{crv_pe_y} \\\\\n \\end{tabular}\n \\caption{Comparison of number of photo-electrons between simulated\n and test beam data for 120 GeV protons normally incident at\n different locations along (left) and across (right) a CRV\n counter.}\n \\label{Fig:crv-pe}\n\\end{figure}\n\nThe assembly of CRV di-counters started in June 2018 and about\nhalf of them have been produced. Production of photosensors and\nelectronics are also underway and 6\\% of the modules have been\nassembled.\nA test stand with cosmic rays is used to control the modules after\nproduction. An example of a cosmic ray event, as recorded by the\ntest stand and by the CRV module under test, is shown in\nFig.~\\ref{Fig:crv-display}.\n\n\n\\begin{figure}[!thb]\n \\begin{center}\n \\includegraphics[width=\\textwidth]{CRVscreen.png} \n \\end{center}\n \\caption{Example of event display at the cosmic ray test stand\n used to qualify CRV modules.} \n \\label{Fig:crv-display} \n \\vspace{1cm}\n\\end{figure}\n\n\\section{Conclusions and perspectives}\n\\label{sec:theend}\n\nThe Mu2e experiment will exploit the world's highest intensity muon\nbeams of the Fermilab Muon Campus to search for CLFV, improving current\nsensitivity by a factor $10^4$ and with a discovery capability over a\nwide range of New Physics models.\nA low mass straw tube tracker, a pure CsI crystal calorimeter with\nSiPM readout and a high efficiency cosmic ray veto have been selected\nto satisfy the demanding requirements.\nTests on prototypes and pre-production modules meet the experimental\nneeds.\nDetector construction is in progress and is expected to be completed\nby the end of 2020.\nInstallation will begin in 2021, followed by commissioning, with data\nbeginning in late 2023.\n\n\n\n\\acknowledgments\n\nWe are grateful for the vital contributions of the Fermilab staff\nand the technical staff of the participating institutions.\nThis work was supported by the US Department of Energy; \nthe Istituto Nazionale di Fisica Nucleare, Italy;\nthe Science and Technology Facilities Council, UK;\nthe Ministry of Education and Science, Russian Federation;\nthe National Science Foundation, USA; \nthe Thousand Talents Plan, China;\nthe Helmholtz Association, Germany;\nand the EU Horizon 2020 Research and Innovation Program under the\nMarie Sklodowska-Curie Grant Agreement No.~690835 and 734303. \nThis document was prepared by members of the Mu2e Collaboration\nusing the resources of the Fermi National Accelerator Laboratory\n(Fermilab), a U.S. Department of Energy, Office of Science, HEP\nUser Facility. Fermilab is managed by Fermi Research Alliance, LLC\n(FRA), acting under Contract No. DE-AC02-07CH11359.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nThe study of time dependent classical as well as quantum harmonic oscillators \nhas appealed to theoretical physicists since time immemorial. In the literature \nthe work by Lewis {\\it et al.}~\\cite{Lewis} has lead to an upsurge of analysis \nof the Hamiltonian for the time dependent quantum harmonic oscillator using a \nclass of exact invariants designed for such systems~\\cite{Lewis2,Lewis3}. The problem \nbecomes even more fascinating when one has a system of two such oscillators in \ntwo-dimensional space. Now, in order to address practical situations one needs \nto include damping in the system. Although there are several studies on the \none-dimensional damped quantum harmonic oscillator in the past~~\\cite{Sebawe}-\\cite{Pedrosa3}, it's two-dimensional equivalent \nis a less explored system~\\cite{Gouba}. The work by Lawson {\\it et.al.}~\\cite{Gouba} is \none of the very few which analyses a two-dimensional damped quantum harmonic oscillator \nsystem. The solutions obtained by them for the mentioned system provides a \nplatform to explore the construction of various coherent states with \nintriguing properties. \n\nIn the present work we extend the study by Lawson \n{\\it et.al.}~\\cite{Gouba} and consider the two-dimensional damped quantum harmonic \noscillator in noncommutative (NC) space. It has been argued that study of quantum mechanical systems in NC space is essential to ensure the attainment of \ngravitational stability~\\cite{Doplicher} in the present theories of quantum \ngravity, namely, string theory \\cite{amati, sw} and loop quantum gravity \\cite{rov}. \nThe simplest quantum mechanical setting in two dimensional NC space\nconsists of replacing the standard set of commutation relations between the\ncanonical coordinates by NC commutation relations $[X, Y]=i\\theta$, where $\\theta$ is a positive real constant. Quantum mechanical systems in such spaces have been studied extensively in the literature \\cite{suss}-\\cite{fgs}.\nThe study of a two-dimensional quantum harmonic oscillator in NC space with time dependent NC parameters was done in \\cite{Dey}. However, their system was an undamped oscillator. The parametrized form of solutions obtained there offered an interesting possibility for study of generalized version of Heisenberg's \nuncertainty relations. Quantum damped harmonic oscillator \non noncommuting two-dimensional space was studied in \\cite{anto} where the exact propagator of the system was obtained and the thermodynamic properties of the system was investigated using the standard canonical density matrix.\n\nIn this work, a two-dimensional damped quantum harmonic oscillator in NC space\nis considered once again. However, our focus of study is different than the work carried out in \\cite{anto}. We first construct the Hamiltonian and then express it in terms of standard commutative variables. This is done in Section 2. Then we solve the Hamiltonian using the method of invariants~\\cite{Lewis} and obtain the corresponding eigenfunction in \nSection 3. In doing so, although we start with the Hamiltonian and corresponding \ninvariant in Cartesian coordinates, eventually we transform our operators to \npolar coordinates (following closely the procedure suggested in \\cite{Dey}) for ease of solution. The form of the Lewis invariant in Cartesian coordinates with a Zeeman term in the Hamiltonian is an interesting result in itself and it also makes it easier to make a transition to it's polar form. It is to be noted that the eigenfunction \nof the Hamiltonian is a product of the eigenfunction of the invariant and a \nphase factor. Both the eigenfunction and phase factor are expressed in terms \nof time dependent parameters which obey the non-linear differential equation known as Ermakov-Pinney (EP) equation~\\cite{Ermakov,Pinney}. \nNext, in Section 4 we judiciously choose the parameters of the damped system \nsuch that they satisfy all the equations representing the system as well as \nprovide us with an exact closed form solution of the Hamiltonian. The \nsolutions of the NC parameters obtained in our analysis turns out to be such that the phase factor in an integral form given in \\cite{Dey} is exactly integrable for various kinds of dissipation. Then \nin Section 5 we device a procedure to calculate the matrix element of a finite arbitrary power of the position operator with respect to the exact solutions \nfor Hamiltonian eigenstates. Using these expressions we proceed to calculate \nthe expectation value of energy and study the \nevolution of the energy expectation value of the system with time \nfor various types of damping. In Section 6 we summarize our results.\n\n\n\n\\section{Model of the two-dimensional harmonic oscillator}\nThe system we consider is a combination of two non-interacting damped harmonic oscillators in two dimensional NC space. The oscillators have equal time dependent \nfrequencies, time dependent coefficients of friction and equal mass in \nNC space. Such a model of damped harmonic oscillator was \nconsidered in an earlier communication~\\cite{Gouba} in commutative space. In this work, we extend the model by considering the system in NC space\\footnote{We shall be considering NC phase space in our work. However, we shall generically refer this as NC space.}.\n\n\\noindent The Hamiltonian of the system has the following form,\n\\begin{equation}\nH(t)=\\dfrac{f(t)}{2M}({P_1}^2+{P_2}^2)+\\dfrac{M\\omega^2(t)}{2f(t)}({X_1}^2+{X_2}^2)\\label{1}\n\\end{equation}\nwhere the damping factor $f(t)$ is given by,\n\\begin{equation}\nf(t)=e^{-\\int_{0}^t\\eta(s)ds} \n\\label{1x}\n\\end{equation}\nwith $\\eta(s)$ being the coefficient of friction. Here $\\omega(t)$ is the \ntime dependent angular frequency of the oscillators and $M$ is their mass.\nIt should be noted that in commutative space, the model with $f(t)=e^{-\\Gamma t}$ and $\\omega(t)=\\omega_0$, with $\\Gamma$ and $\\omega_0$\nbeing positive constants, is said to be the two-dimensional Caldirola and Kanai Hamiltonian \\cite{caldi, kanai}.\nThe position and momentum \ncoordinates $(X_i,P_i)$ are noncommuting variables in NC space, that is, \ntheir commutators are $[X_1,X_2]~\\neq~0$ and $[P_1,P_2]~\\neq~0$. The \ncorresponding canonical variables $(x_i,p_i)$ in commutative space are such \nthat the commutator $[x_i,p_j]=i\\hbar\\delta_{i,j}$, $[x_i,x_j]=0=[p_i,p_j]$; ($i,j=1,2$).\n\nIn order to express the NC Hamiltonian in terms of the standard commutative variables explicitly, we apply the standard Bopp-shift relations \\cite{mez} ($\\hbar=1$): \n\\begin{eqnarray}\n& X_1=x_1-\\dfrac{\\theta(t)}{2}p_2\\,\\,\\,;\\,\\,\\,X_2=x_2+\\dfrac{\\theta(t)}{2}p_1\\\\\n& P_1=p_1+\\dfrac{\\Omega(t)}{2}x_2\\,\\,\\,;\\,\\,\\,P_2=p_2-\\dfrac{\\Omega(t)}{2}x_1 \\,\\,.\n\\label{eqn1}\n\\end{eqnarray}\nHere $\\theta(t)$ and $\\Omega(t)$ are the NC parameters for space \nand momentum respectively, such that $[X_1,X_2]~=i\\theta(t)$, \n$[P_1,P_2]~=i\\Omega(t)$ and $[X_1,P_1]=i[1+\\frac{\\theta(t)\\Omega(t)}{4}]=[X_2,P_2]$; ($X_1\\equiv X$, $X_2 \\equiv Y$, $P_1\\equiv P_x$, $P_2 \\equiv P_y$). \n\n\\noindent The Hamiltonian in terms of $(x_i,p_i)$ coordinates is therefore given by the following \nrelation,\n\\begin{equation}\nH=\\dfrac{a(t)}{2}({p_1}^2+{p_2}^2)+\\dfrac{b(t)}{2}({x_1}^2+{x_2}^2)+c(t)({p_1}{x_2}-{p_2}{x_1})\\,\\,\\,.\\label{eqn2}\n\\end{equation}\nThe time dependent coefficients in the above Hamiltonian are given as,\n\\begin{eqnarray}\na(t)&=&\\dfrac{f(t)}{M}+\\dfrac{M{\\omega^2(t)}\\theta^2(t)}{4f(t)}\\label{3} \\\\\n b(t)&=&\\dfrac{f(t){\\Omega^2(t)}}{4M}+\\dfrac{M{\\omega^2(t)}}{f(t)}\\label{4} \\\\\n c(t)&=&\\dfrac{1}{2}\\left[\\dfrac{f(t)\\Omega(t)}{M}+\\dfrac{M\\omega^2(t)\\theta(t)}{f(t)} \\right]. \n\\label{eqn3}\n\\end{eqnarray}\nHere it must be noted that although our Hamiltonian given by Eqn.(\\ref{eqn2}) has the same form as that in \n\\cite{Dey} to study a system of a two dimensional harmonic oscillator in NC space, the time dependent \nHamiltonian coefficients (given by Eqn(s).(\\ref{eqn3})) have very different form. This is because our system is that of \na damped harmonic oscillator in two-dimensional NC space. Thus, the damping factor $f(t)$ modulates and alters \nthe Hamiltonian coefficients from the form considered in earlier study \\cite{Dey}.\n\n\n\n\\section{Solution of the model Hamiltonian}\nIn order to find the solutions of the model Hamiltonian $H(t)$ (Eqn.(\\ref{eqn2}))\nrepresenting the two-dimensional damped harmonic oscillator in \nNC{ space, we follow the route suggested by Lewis {\\it et.al.}~\\cite{Lewis} in their work. First we \nconstruct the time-dependent Hermitian invariant operator $I(t)$ corresponding to our Hamiltonian operator $H(t)$ \n(given by Eqn.(\\ref{eqn2})). This is because if one can solve for the eigenfunctions of $I(t)$, $\\phi(x_1,x_2)$, such \nthat,\n\\begin{equation}\nI(t)\\phi(x_1,x_2)=\\epsilon \\phi(x_1,x_2)\n\\label{eqnegn}\n\\end{equation}\nwhere $\\epsilon$ is an eigenvalue of $I(t)$ corresponding to eigenstate $\\phi(x_1,x_2)$, one can obtain the \neigenstates of $H(t)$, $\\psi(x_1,x_2,t)$, using the relation given by Lewis {\\it et. al.}~\\cite{Lewis} which is as \nfollows, \n\\begin{equation}\n\\psi(x_1,x_2,t)=e^{i\\Theta(t)}\\phi(x_1,x_2)\n\\label{eqnpsi}\n\\end{equation}\nwhere the real function $\\Theta(t)$ which acts as the phase factor will be discussed in details later. \n\n\n\n\\subsection{The Time Dependent Invariant}\nNext, following the approach taken by Lewis {\\it et.al.}~\\cite{Lewis}, we need to construct the operator $I(t)$ which \nis an invariant with respect to time, corresponding to the Hamiltonian $H(t)$, as mentioned earlier, such \nthat $I(t)$ satisfies the condition,\n\\begin{equation}\n\\dfrac{dI}{dt}=\\partial_t{I}+\\dfrac{1}{i}[I,H]=0.\n\\label{eqn4}\n\\end{equation}\nThe procedure is to choose the Hermitian invariant $I(t)$ to be of the same homogeneous quadratic form defined by Lewis \n{\\it et. al.}~\\cite{Lewis} for time-dependent harmonic oscillators. However, since we are dealing with a \ntwo-dimensional system in the present study, $I(t)$ takes on the following form,\n\\begin{equation}\nI(t)=\\alpha(t)({p_1}^2+{p_2}^2)+\\beta(t)({x_1}^2+{x_2}^2)+\\gamma(t)(x_1{p_1}+p_2{x_2}).\n\\label{eqn5}\n\\end{equation}\nHere we will consider $\\hbar=1$ since we choose to work in natural units. Now, using the form of $I(t)$ defined by \nEqn.(\\ref{eqn5}) in Eqn.(\\ref{eqn4}) and equating the coefficients of the canonical variables, we get the \nfollowing relations,\n\\begin{eqnarray}\n\\dot{\\alpha}(t)&=&-a(t)\\gamma(t)\\label{eqn6}\\\\\n\\dot{\\beta}(t)&=&b(t)\\gamma(t)\\label{eqn7}\\\\\n\\dot{\\gamma}(t)&=&2\\left[\\,b(t)\\alpha(t)-\\beta(t)a(t)\\,\\right]\n\\label{eqn8}\n\\end{eqnarray}\nwhere dot denotes derivative with respect to time $t$.\n\n\\noindent To express the above three time dependent parameters $\\alpha$,$\\beta$ and $\\gamma$ in terms of a single time \ndependent parameter, we parametrize $\\alpha(t)=\\rho^{2}(t)$. Substituting this in Eqn(s).(\\ref{eqn6}, \\ref{eqn8}), we \nget the other two parameters in terms of $\\rho(t)$ as, \n\\begin{eqnarray}\n\\gamma(t)&=&-\\dfrac{2\\rho\\dot{\\rho}}{a(t)}\\label{eqn9}\\\\\n\\beta(t)&=&\\dfrac{1}{a(t)}\\left[\\dfrac{{\\dot{\\rho}^2}}{a(t)}+{{\\rho}^2}b+\\dfrac{\\rho\\ddot{\\rho}}{a(t)}-\\dfrac{\\rho\\dot{\\rho}\\dot{a}}{a^2} \\right].\\label{eqn10}\n\\end{eqnarray}\nNow, substituting the value of $\\beta$ in Eqn.(\\ref{eqn7}), we get a non-linear equation in \n$\\rho(t)$ which has the form of the non-linear Ermakov-Pinney (EP) equation with a dissipative \nterm~\\cite{Dey, Ermakov, Pinney}. The form of the non-linear equation is as follows, \n\\begin{equation}\n\\ddot{\\rho}-\\dfrac{\\dot{a}}{a}\\dot{\\rho}+ab\\rho={\\xi^2}\\dfrac{a^2}{\\rho^3}~.\n\\label{eqn11}\n\\end{equation} \nwhere ${\\xi^2}$ is a constant of integration. This equation has similar form to the EP equation obtained in \\cite{Dey}, which is expected since \nour $H(t)$ has the same form as theirs. However, once again we should recall the fact that the explicit form of the time-dependent \ncoefficients are different due to the presence of damping. \n\n\n\\noindent Now, using the EP equation we get a simpler form of $\\beta$ as,\n\\begin{eqnarray}\n\\beta(t)&=&\\dfrac{1}{a(t)}\\left[\\dfrac{{\\dot{\\rho}^2}}{a(t)}+\\dfrac{{\\xi^2}{a(t)}}{\\rho^2} \\right].\n\\label{eqnew}\n\\end{eqnarray} \n\\noindent Next, substituting the expressions of $\\alpha$, $\\beta$ and $\\gamma$ in \nEqn.(\\ref{eqn5}), we get the following \nexpression for $I(t)$,\n\\begin{equation}\nI(t)=\\rho^2({p_1}^2+{p_2}^2)+\\left(\\dfrac{\\dot{\\rho}^2}{a^2}+\\dfrac{{\\xi^2}}{\\rho^2}\\right)({x_1}^2+{x_2}^2)-\\dfrac{2\\rho\\dot{\\rho}}{a}(x_1{p_1}+p_2{x_2}).\n\\label{eqn12}\n\\end{equation}\nThe form of the Lewis invariant in Cartesian coordinates will be used later to go over to it's polar coordinate form.\nThe solution of the EP equation under various physically significant conditions shall be discussed later.\n\n\n\n \n\\subsection{Construction of Ladder operators} \nNow that we have the required Hermitian invariant $I(t)$, we proceed to calculate it's eigenstates using the operator approach. For \nthis purpose we need to first construct some ladder operators. To do this, we first need to transform the form of $I(t)$ \n(given by Eqn.(\\ref{eqn12})) to a more manageable form. For this we invoke a unitary transformation using a suitable unitary \noperator $\\hat{U}$ having the following form, \n\\begin{eqnarray}\n\\hat{U}=exp\\left[-\\dfrac{i\\dot{\\rho}}{2a(t)\\rho}({x_1}^2+{x_2}^2)\\right],\\,\\,\\,\n\\hat{U^{\\dagger}}\\hat{U}=\\hat{U}\\hat{U^{\\dagger}}=\\textbf{I}.\n\\label{eqn13}\n\\end{eqnarray}\nDefining, \n\\begin{eqnarray}\n\\phi^{'}(x_1,x_2)=\\hat{U}\\phi(x_1,x_2) \\,\\,\\,,\\,\\,\\,\nI^{'}(t)&=\\hat{U}I\\hat{U^\\dagger}\\,\\,\\;\n\\label{eqn14}\n\\end{eqnarray}\nwhere $\\phi(x_1,x_2)$ is an eigenfunction of $I(t)$ as introduced in Eqn.(\\ref{eqnegn}), \nthen, using Eqn(s).(\\ref{eqnegn},\\ref{eqn14}), we get, \n\\begin{align}\nI^{'}\\phi^{'}=\\hat{U}I\\hat{U^\\dagger}\\hat{U}\\phi=\\hat{U}I\\phi=\\hat{U}\\epsilon\\phi=\\epsilon\\phi^{'}.\n\\end{align}\nThe transformed expression of the invariant, $I^{'}(t)$, using Eqn.(\\ref{eqn14}), has the following form, \n\\begin{align}\nI^{'}(t)=\\rho^2({p_1}^2+{p_2}^2)+\\dfrac{{\\xi^2}}{\\rho^2}({x_1}^2+{x_2}^2)\\,\\,.\n\\label{eqn15} \n\\end{align}\nThis transformed form of the invariant, $I^{'}(t)$, has exactly the same form as that of the Hamiltonian for a time dependent \ntwo-dimensional simple harmonic oscillator. So, we can introduce the corresponding ladder operators for $\\hat{I^{'}}(t)$ to be \ngiven by,\n\\begin{eqnarray}\n{\\hat{a}_j}^{'}=\\dfrac{1}{\\sqrt{2\\xi}}\\left(\\dfrac{\\xi}{\\rho}{\\hat{x}}_j+i\\rho{\\hat{p}}_j\\right)\\,\\,\\,,\\,\\,\\,{{\\hat{a}_j}^{'\\dagger}}=\\dfrac{1}{\\sqrt{ 2\\xi}}\\left(\\dfrac{\\xi}{\\rho}{\\hat{x}}_j-i\\rho{\\hat{p}}_j\\right)\n\\label{eqn16}\n\\end{eqnarray}\nwhere $j=1,2$ and the operators satisfy the commutation relation $[{{\\hat{a}_i}^{'}},{{\\hat{a}_j}^{'\\dagger}}]=\\delta_{ij}$.\n\n\\noindent Now we make the reverse transformation to get the expression of the unprimed ladder operators:\n\\begin{eqnarray}\n\\hat{a_j}(t)&=&{\\hat{U}}^\\dagger{\\hat{a}_j}^{'}\\hat{U}=\\dfrac{1}{\\sqrt{2\\xi}}\\left[\\dfrac{\\xi}{\\rho}x_j+i\\rho{p_j}-\\dfrac{i\\dot{\\rho}}{a(t)}x_j\\right]\\\\\n\\label{eqn17}\n\\hat{{a_j}^{\\dagger}}(t)&=&{\\hat{U}}^\\dagger{{\\hat{a}_j}^{'\\dagger}}\\hat{U}=\\dfrac{1}{\\sqrt{2\\xi}}\\left[\\dfrac{\\xi}{\\rho}x_j-i\\rho{p_j}+\\dfrac{i\\dot{\\rho}}{a(t)}x_j\\right].\n\\label{eqn18} \n\\end{eqnarray}\nIt can be easily checked using the algebra of the primed ladder operators that $[{{\\hat{a}_i}},{{\\hat{a}_j}^{\\dagger}}]=\\delta_{ij}$.\n\n\\noindent We now set $\\xi=1$ and consider two linear combinations of the above two operators such that,\n\\begin{eqnarray}\n\\hat{a}(t)=-\\dfrac{i}{\\sqrt{2}}(\\hat{a}_1-i\\hat{a}_2)\n=\\dfrac{1}{2}\\left[\\rho(\\hat{p_1}-i\\hat{p_2})-\\left(\\dfrac{i}{\\rho}+\\dfrac{\\dot{\\rho}}{a(t)} \\right)(\\hat{x_1}-i\\hat{x_2})\\right]\n\\label{eqn19}\n\\end{eqnarray}\nand\n\\begin{eqnarray}\n{\\hat{a}}^\\dagger(t)=\\dfrac{i}{\\sqrt{2}}({\\hat{a}_1}^\\dagger+i{\\hat{a}_2}^\\dagger)\n=\\dfrac{1}{2}\\left[\\rho(\\hat{p_1}+i\\hat{p_2})+\\left(\\dfrac{i}{\\rho}-\\dfrac{\\dot{\\rho}}{a(t)} \\right)(\\hat{x_1}+i\\hat{x_2})\\right].\n\\label{eqn20}\n\\end{eqnarray}\nThese also satisfy the commutation relation $[\\hat{a},\\hat{a}^\\dagger]=1$.\n\n\n\n\n\\subsection{Transformation to polar coordinates}\n\nWith the above results in place, we now transform the invariant $I(t)$ and the corresponding ladder operators to polar \ncoordinates for calculational convenience. For this we invoke the transformation of coordinates of the form,\n\\begin{equation}\nx=rcos\\theta\\,\\,\\,;\\,\\,\\,y=rsin\\theta~.\n\\label{eqn21}\n\\end{equation}\nThe canonical coordinates in polar representation takes the following form, \n\\begin{eqnarray}\np_r&=&\\dfrac{1}{2}\\left(\\dfrac{x_1}{r}{p_1}+{p_1}\\dfrac{x_1}{r}+\\dfrac{x_2}{r}{p_2}+p_2\\dfrac{x_2}{r}\\right)\\nonumber\\\\\n&=&\\dfrac{x_1{p_1}+x_2{p_2}}{r}-\\dfrac{i}{2r}\\nonumber\\\\&=&-i\\left({\\partial}_r+\\dfrac{1}{2r} \\right)\\\\\np_{\\theta}&=&(x_1{p_2}-x_2{p_1})=-i{\\partial_{\\theta}}.\n\\label{eqn22}\n\\end{eqnarray} \nThe commutation relations between ($r$, $p_r$) and \n($\\theta$, $p_\\theta$) have the form\n\\begin{equation}\n[r,p_r]=[\\theta,p_{\\theta}]=[x_1,p_1]=[x_2,p_2]=i.\n\\label{eqn23}\n\\end{equation}\nThe corresponding anticommutation relation can be found to be,\n\\begin{equation}\n[r, p_r]_{+}=[x_1, p_1]_{+}+[x_2, p_2]_{+}\n=2(x_1 p_1+p_2 x_2)\n\\label{eqn24}\n\\end{equation}\nwhere $[A, B]_{+}=AB+BA$ represents anticommutator between operators $A$, $B$.\n\n\\noindent In order to transform the invariant $I(t)$ in polar coordinates, we need to have few other relations which are,\n\\begin{eqnarray}\n({p_1}^2+{p_2}^2)&=&\\left({p_r}^2+\\dfrac{{p_{\\theta}}^2}{r^2}-\\dfrac{1}{4r^2}\\right)\\\\\n(p_1+i{p_2})&=&e^{i\\theta}\\left[p_r+\\dfrac{i}{r}p_{\\theta}+\\dfrac{i}{2r} \\right] \\\\\n(p_1-i{p_2})&=&e^{-i\\theta}\\left[p_r-\\dfrac{i}{r}p_{\\theta}+\\dfrac{i}{2r} \\right].\n\\label{eqn25}\n\\end{eqnarray}\nHence the invariant in polar coordinate system is given by,\n\\begin{eqnarray}\nI(t)=\\dfrac{\\xi^2}{\\rho^2}r^2+\\left(\\rho{p_r}-\\dfrac{\\dot{\\rho}}{a}r\\right)^2+\\left({\\dfrac{\\rho{p_\\theta}}{r}}\\right)^2-\\left({\\dfrac{\\rho\\hbar}{2r}}\\right)^2\n\\label{eqn26}\n\\end{eqnarray}\nand the ladder operators in polar coordinate system have the following form,\n\\begin{eqnarray}\n\\hat{a}(t)&=&\\dfrac{1}{2}\\left[\\left(\\rho{p_r}-\\dfrac{\\dot{\\rho}}{a(t)}r \\right)-i\\left(\\dfrac{r}{\\rho}+\\dfrac{\\rho{p_\\theta}}{r}+\\dfrac{\\rho}{2r} \\right) \\right]e^{-i\\theta}\\nonumber\\\\\n{\\hat{a}}^{\\dagger}(t)&=&\\dfrac{1}{2}e^{i\\theta}\\left[\\left(\\rho{p_r}-\\dfrac{\\dot{\\rho}}{a(t)}r \\right)+i\\left(\\dfrac{r}{\\rho}+\\dfrac{\\rho{p_\\theta}}{r}+\\dfrac{\\rho}{2r} \\right) \\right].\n\\label{eqn27}\n\\end{eqnarray}\nNow we note from Eqn(s).(\\ref{eqn26}, \\ref{eqn27}) that both the invariant $I(t)$ \nand the ladder operators have the same form as those used in \\cite{Dey} to study the undamped harmonic oscillator in \nNC space. The time-dependent coefficients involved in the \npresent study however differ due to the damping present in our system. Thus, we \ncan just borrow the expression of eigenfunction and the phase factors \nfrom \\cite{Dey} for our present system.\n\n\n\n\\subsection{Eigenfunction and phase factor}\nWe depict the set of eigenstates of the invariant operator $I(t)$ as $\\ket{n,l} $, following the convention in \n\\cite{Dey}. Here, $n$ and $l$ are integers such that $n+l\\geqslant0$. So we have the condition $l\\geqslant-n$. \nThus, if $l=-n+m$, then $m$ is a positive integer; and the corresponding eigenfunction in polar coordinate system has the following form (restoring $\\hbar$), \n\\begin{eqnarray}\n\\phi_{n,m-n}(r,\\theta)&=&\\braket{r,\\theta|n,m-n}\\\\\n&=&\\lambda_{n}\\dfrac{{(i\\sqrt{\\hbar}\\rho)}^m}{\\sqrt{m!}}r^{n-m}e^{i\\theta(m-n)-\\dfrac{a(t)-i\\rho\\dot{\\rho}}{2a(t)\n\\hbar{\\rho}^2}r^2}U\\left(-m,1-m+n,\\dfrac{r^2}{\\hbar\\rho^2} \\right)\n\\label{eqn28}\n\\end{eqnarray}\nwhere $\\lambda_n$ is given by\n\\begin{eqnarray}\n\\lambda_n^2=\\dfrac{1}{\\pi{n!}{(\\hbar\\rho^2)}^{1+n}}~. \n\\label{eqn28lam}\n\\end{eqnarray}\nHere, $U\\left(-m,1-m+n,\\dfrac{r^2}{\\hbar\\rho^2} \\right)$ is \nTricomi's confluent hypergeometric function \\cite{Arfken, uva} and the eigenfunction $\\phi_{n,m-n}(r,\\theta)$ satisfies the following \northonormality relation,\n\\begin{equation}\n\\int_0^{2\\pi}d\\theta\\int_0^{\\infty}rdr\\phi^{*}_{n,m-n}(r,\\theta)\\phi_{n^{'},m^{'}-n^{'}}(r,\\theta)=\\delta_{nn^{'}}\\delta_{mm^{'}}.\n\\label{eqn29}\n\\end{equation}\nAgain following \\cite{Dey}, the expression of the phase factor $\\Theta(t)$ is given by, \n\\begin{equation}\n\\Theta_{\\,n\\,,\\,l}(t)\\,=\\,(\\,n\\,+\\,l\\,)\\,\\int_0^t \\left(c(T)-\\dfrac{a(T)}{\\rho^2(T)} \\right)dT~.\n\\label{eqn30}\n\\end{equation}\nFor a given value of $l=-n+m$, it would be given by \\cite{Dey},\n\\begin{equation}\n\\Theta_{\\,n\\,,\\,m\\,-\\,n\\,}(t)=m\\int_0^t \\left(c(T)-\\dfrac{a(T)}{\\rho^2(T)} \\right)dT~.\n\\label{eqn31}\n\\end{equation}\nWe shall use this expression to compute the phase explicitly as a function of time for various physical cases in the subsequent discussion.\n\n\\noindent The eigenfunction of the Hamiltonian therefore reads (using Eqn(s).(\\ref{eqnpsi}, \\ref{eqn28}, \\ref{eqn31}))\n\\begin{eqnarray}\n\\psi_{n,m-n}(r,\\theta,t)&=&e^{i\\Theta_{n, m-n}(t)}\\phi_{n, m-n}(r,\\theta)\\nonumber\\\\\n&=&\\lambda_{n}\\dfrac{{(i\\sqrt{\\hbar}\\rho)}^m}{\\sqrt{m!}}\\exp{\\left[im\\int_0^t \\left(c(T)-\\dfrac{a(T)}{\\rho^2(T)} \\right)dT \\right]}\n\\nonumber\\\\\n&&\\times~r^{n-m}e^{i\\theta(m-n)-\\dfrac{a(t)-i\\rho\\dot{\\rho}}{2a(t)\\hbar{\\rho}^2}r^2}U\\left(-m,1-m+n,\\dfrac{r^2}{\\hbar\\rho^2} \\right).\n\\label{eqn32}\n\\end{eqnarray}\n\n\n\n\n\n\n\n\\section{Solutions for the noncommutative damped oscillator}\nIn this paper we are primarily interested in damped oscillators in \nNC space. For this purpose we want to find the eigenfunctions of \nthe corresponding Hamiltonian under various types of damping. The various \nkinds of damping are represented by various forms of the time dependent \ncoefficients of the Hamiltonian, namely, $a(t)$, $b(t)$ and $c(t)$. \nHowever, the various forms must be constructed in such a way that they satisfy \nthe non-linear EP equation given by Eqn.(\\ref{eqn11}). \nThe procedure of this construction of exact analytical solutions is based on the Chiellini integrability condition \\cite{chill} and this formalism was followed in \\cite{Dey}. We shall do the same in this paper.\nSo, for various forms of $a(t)$ and $b(t)$, we get the corresponding form of $\\rho(t)$ \nusing the EP equation together with the Chiellini integrability condition. In other words, the set of values of $a(t)$, $b(t)$ and $\\rho(t)$ that we use must be a solution set of the EP equation consistent with the Chiellini integrability condition. In the subsequent discussion we shall proceed to obtain solutions of the EP equation for the damped NC oscillator.\n\n\n\\subsection{Solution Set-I for Ermakov-Pinney equation : Exponentially \\\\ decaying solutions } \n\\subsubsection{The Solution Set}\nThe simplest kind of solution set of EP equation under damping is the \nexponentially decaying set used in \\cite{Dey}. The solution set is given by the following\nrelations, \n\\begin{eqnarray}\na(t)=\\sigma e^{-\\vartheta{t}}\\,\\,\\,,\\,\\,\\,b(t)=\\Delta e^{\\vartheta{t}}\\,\\,\\,,\\,\\,\\rho(t)={\\mu}e^{-\\vartheta{t\/2}}\\,\\,\\,\\,\\,\n\\label{EPsoln1}\n\\end{eqnarray}\nwhere $\\sigma,\\Delta$ and $\\mu$ are constants. Here, $\\vartheta$ is any \npositive real number. Substituting the expression of $a(t),b(t) \\,$and$\\, \\rho(t)$ in the EP equation, we can easily verify the relation between these constants to be as follows, \n\\begin{equation}\n\\mu^4=\\dfrac{\\xi^2{\\sigma^2}}{\\sigma\\Delta-\\dfrac{1}{4}\\vartheta^2}~.\n\\label{EPreln1}\n\\end{equation}\n\n\n\n\n\\subsubsection{Study of the corresponding eigenfunctions}\nWe now write down the eigenfunctions of the Hamiltonian for the choosen set of \ntime-dependent coefficients. For this endeavour we need to choose explicit \nforms of the damping factor $f(t)$ and angular frequency of the oscillator \n$\\omega(t)$. The eigenfunction of the invariant $I(t)$ (which is given by \nEqn.(\\ref{eqn28})) takes on the following form for the solution set-I:\n\\begin{eqnarray}\n\\phi_{n,m-n}(r,\\theta)=\\lambda_{n}\\dfrac{{(i{\\mu}e^{-\\vartheta{t\/2}})}^m}{\\sqrt{m!}} r^{n-m}e^{i\\theta(m-n)-\\dfrac{2\\sigma+i\\mu^2\\vartheta}{4\\sigma\\mu^2{e^{-\\vartheta{t}}}}r^2}U\\left(-m,1-m+n,\\dfrac{r^2{e^{\\vartheta{t}}}}{\\mu^2} \\right)\n\\label{eqn33}\n\\end{eqnarray}\nwhere $\\lambda_n$ is given by\n\\begin{eqnarray}\n\\lambda_n^{\\,2}\\,=\\,\\dfrac{1}{\\pi\\,n!\\,[\\mu^2\\,exp\\,(-\\vartheta{t})]^{1+n}}~.\n\\label{eqn33lam}\n\\end{eqnarray}\nIn order to obtain explicit expressions of the phase factors for various cases of the damping factor, we choose both the \nfunctions $\\omega(t)$ and $\\eta(t)$ as follows.\\\\\n\n\n\n\\noindent{\\bf $\\langle A\\rangle$ Solution Set-Ia}\n\n\\noindent Firstly, we choose the damping factor $f(t)=1$. Thus, in this case the damping in the system is due to the exponentially \ndecaying frequency $\\omega(t)$. For this purpose we set, \n\\begin{eqnarray}\n\\eta(t)=0\\,\\,\\Rightarrow\\,\\,f(t)=1\\\\\n\\omega(t)={\\omega_0}\\,exp(-\\Gamma{t}\/2)\\,\\,\\,.\n\\label{eqn34}\n\\end{eqnarray}\nSubstituting the expressions for $a(t)$, $b(t)$, $\\omega(t)$ and $f(t)$ in the \nEqn(s).(\\ref{3}, \\ref{4}), we get the time dependent NC parameters as,\n\\begin{eqnarray}\n\\theta(t)=\\dfrac{2}{M\\omega_0}\\,exp\\,[\\Gamma{t}\/2] \\sqrt{M\\sigma\\,exp(-\\vartheta{t})-1}\\label{eqn35} \\\\\n\\Omega(t)=2\\sqrt{\\,M[\\Delta\\,exp\\,(\\vartheta{t})-M\\omega_0^2\\,exp\\,(-\\Gamma{t})]}. \\label{eqn36}\n\\end{eqnarray}\nIt can be checked that in the limit $\\Gamma\\rightarrow0$, that is, for \nconstant frequency, the expressions for $\\theta(t)$ and $\\Omega(t)$ reduce \nto those in \\cite{Dey}. When $\\vartheta=\\Gamma$, then the solutions take the \nform,\n\\begin{eqnarray}\n\\theta(t)&=&\\dfrac{2}{M\\omega_0}\\, \\sqrt{M\\sigma\\,-e^{\\Gamma t}}\\label{eqn35b} \\\\\n\\Omega(t)&=&2\\sqrt{\\,M[\\Delta\\,exp\\,(\\Gamma{t})-M\\omega_0^2\\,exp\\,(-\\Gamma{t})]}. \\label{eqn36b}\n\\end{eqnarray}\nSubstituting these relations in the expression for $c(t)$ in Eqn.(\\ref{eqn3}), \nwe get an expression for the phase in a closed form as, \n\\begin{equation}\nc(t)=\\sqrt{\\dfrac{ \\Delta\\,exp\\,(\\Gamma{t})-M{\\omega_0}^2\\,exp\\,(-\\Gamma{t}) }{M}} \n\\,+\\,\\omega_0\\,exp\\,(-\\Gamma{t}\/2)\\sqrt{\\,M\\sigma\\,exp\\,(-\\Gamma{t})-1 \\,}.\\label{eqn38}\n\\end{equation}\nSubstituting the expressions of $a(t)$, $\\rho(t)$ and $c(t)$ in Eqn.(\\ref{eqn31}), we get,\n\\begin{eqnarray}\n\\Theta_{\\,n\\,,\\,l}(t)&=&\\,(\\,n\\,+\\,l\\,)\\,\\dfrac{\\omega_0}{2\\sqrt{M\\sigma}\\Gamma}\\ \\left[log_{e}\\dfrac{e^{{\\Gamma}t}-2M\\sigma-2\\sqrt{M\\sigma(M\\sigma-e^{{\\Gamma}t})}}{1-2M\\sigma-2\\sqrt{M\\sigma(M\\sigma-1)}}\\right.\\nonumber\\\\ \n&&\\left. -{\\Gamma}t-2\\sqrt{M\\sigma(M{\\sigma}e^{-{2\\Gamma}t}-e^{-{\\Gamma}t})}+2\\sqrt{M\\sigma(M\\sigma-1)}\\ \\right]\\nonumber\\\\\n&&+\\dfrac{2(n+l)}{\\Gamma}\\,\\left[\\sqrt{\\frac{\\Delta}{M}e^{{\\Gamma}t}-{\\omega_0^2}e^{-{\\Gamma}t}}-\\sqrt{\\dfrac{\\Delta}{M}-{\\omega_0^2}}\\right.\\nonumber\\\\\n&&\\left.+2i{\\omega_0}\\left\\{e^{-{\\Gamma}t\/2} {_{2}F_{1}}\\left(-\\frac{1}{4},\\frac{1}{2},\\frac{3}{4},\\frac{{\\Delta}e^{{2\\Gamma}t}}{M\\omega_0^2}\\right)- {_{2}F_{1}}\\left(-\\frac{1}{4},\\frac{1}{2},\\frac{3}{4},\\frac{\\Delta}{M\\omega_0^2}\\right)\\right\\}\\right]\n-\\frac{\\sigma}{\\mu^2}(n+l)t \\nonumber\\\\\n\\label{eqn39} \n\\end{eqnarray}\nwhere $_{2}F_{1}(a,b,c;t)$ is said to be the Gauss hypergeometric function. It is interesting to note that the solutions of the time dependent NC parameters enable us to get an exact analytic expression for the phase factor.\nIt is further interesting to observe that the phase has a complex part which indicates that the wave function decays with time.\\\\\n\n\n\n\\noindent {\\bf $\\langle B\\rangle$ Solution Set-Ib}\n\n\\noindent Here the oscillator is damped due to the damping factor $f(t)$ and the frequency $\\omega(t)$ \nis a constant. This situation can be depicted by the following relations, \n\\begin{eqnarray}\nf(t)= exp\\,(-\\Gamma{t})~;~\\omega(t)={\\omega_0}.\n\\label{10x}\n\\end{eqnarray}\nSubstituting these relations in Eqn(s).(\\ref{3}, \\ref{4}), we get the time dependent NC parameters \nas,\n\\begin{eqnarray}\n\\theta(t)&=&\\dfrac{2}{M\\omega_0}\\sqrt{M{\\sigma}\\,exp\\,(-\\vartheta{t})-exp\\,(-\\Gamma{t})}\\,\\,e^{-\\Gamma t\/2}\\label{eqn40}\\\\\n\\Omega(t)&=&2e^{\\Gamma{t}}\\sqrt{M\\,[\\Delta\\,exp\\,(\\vartheta-\\Gamma)t-M{\\omega_0}^2]}.\n\\label{eqn41}\n\\end{eqnarray}\nIt can be checked that in the limit $\\Gamma\\rightarrow0$, that is, for \nconstant frequency, the expressions for $\\theta(t)$ and $\\Omega(t)$ reduce \nto those in \\cite{Dey}. When $\\vartheta=\\Gamma$, then the solutions take the \nform,\n\\begin{eqnarray}\n\\theta(t)&=&\\dfrac{2}{M\\omega_0}\\sqrt{M{\\sigma}\\,- 1}\\,\\,e^{-\\Gamma t}\\label{eqn40b}\\\\\n\\Omega(t)&=&2e^{\\Gamma{t}}\\sqrt{M\\,[\\Delta\\,-M{\\omega_0}^2]}.\n\\label{eqn41b}\n\\end{eqnarray}\nSubstituting these relations in the expression for $c(t)$ in Eqn.(\\ref{eqn3}), we \nget,\n\\begin{equation}\nc(t)= \\sqrt{\\dfrac{ \\Delta\\,-M{\\omega_0}^2\\, }{M}} \\,+\\,\\omega_0\\sqrt{M\\sigma-1}\\,=\\,constant ~. \\label{eqn43}\n\\end{equation}\nSubstituting the expressions of $a(t)\\,,\\rho(t)$\\, and $c(t)$\\, in Eqn.(\\ref{eqn31})\\,,\\,we get an expression for the phase in a closed form as,\n\\begin{align}\n\\Theta_{\\,n\\,,\\,l}(t)\\,=&(\\,n\\,+\\,l\\,)\\left[-\\frac{\\sigma}{\\mu^2}\\,+\\,\\sqrt{\\frac{ \\Delta\\,-M{\\omega_0}^2\\,}{M} } \\,+\\omega_0\\sqrt{M\\sigma-1} \\right]\\,t ~.\\label{eqn44}\n\\end{align}\nOnce again we are able to obtain an exact expression for the phase, in this case varying linearly with time.\nIt is important to note that the reality of the phase in this case depends crucially on the parameters $\\Delta$, $M$, $\\sigma$, $\\omega_0$. The phase $\\Theta_{n,l}$ is real if $\\Delta-M\\omega_{0}^2 \\geq 0$ and $M\\sigma \\geq 1$, else it is complex.\\\\\n\n\n\n\\noindent {\\bf $\\langle C\\rangle$ Solution Set-Ic}\n\n\n\\noindent Here the oscillator is damped due to the damping factor $f(t)$ and the time-dependent \nfrequency $\\omega(t)$; both of which are exponentially decaying. Thus, we set, \n\\begin{eqnarray}\nf(t)=exp\\,(-\\Gamma{t})~;~\\omega(t)={\\omega_0}\\,exp\\,(-\\Gamma{t}\/2).\n\\label{eqn45}\n\\end{eqnarray}\nSubstituting these relations in Eqn.(s)(\\ref{3}, \\ref{4}), we get the time dependent NC \nparameters to be, \n\\begin{eqnarray}\n\\theta(t)&=&\\dfrac{2}{M\\omega_0}\\sqrt{(M{\\sigma}e^{-(\\vartheta-\\Gamma)t}\\,- 1)}e^{-\\Gamma{t}\/2}\\label{eqn46}\\\\\n\\Omega(t)&=&2\\sqrt{M\\,[\\Delta\\,exp(\\vartheta{t})-M{\\omega_0}^2\\, \\,]}\\,\\,e^{\\Gamma t\/2}.\\label{eqn47}\n\\end{eqnarray}\nIt can be checked that in the limit $\\Gamma\\rightarrow0$, that is, for \nconstant frequency, the expressions for $\\theta(t)$ and $\\Omega(t)$ reduce \nto those in \\cite{Dey}. When $\\vartheta=\\Gamma$, then the solutions take the \nform,\n\\begin{eqnarray}\n\\theta(t)&=&\\dfrac{2}{M\\omega_0}\\sqrt{(M{\\sigma}\\,- 1)}e^{-\\Gamma{t}\/2}\\,\\,\\label{eqn46}\\\\\n\\Omega(t)&=&2\\sqrt{M\\,[\\Delta\\,exp(\\Gamma{t})-M{\\omega_0}^2\\, \\,]}\\,\\,e^{\\Gamma t\/2}.\\label{eqn47}\n\\end{eqnarray}\nSubstituting these relations in the expression for $c(t)$ in Eqn.(\\ref{eqn3}), we \nget,\n\\begin{align}\nc(t)= \\sqrt{\\dfrac{ \\Delta\\,-M{\\omega_0}^2\\,\\exp\\left[-\\Gamma{t}\\right] }{M}} \n + {\\omega_0}\\,e^{-\\Gamma t\/2}\\sqrt{M{\\sigma}\\,-1}~.\\label{eqn49}\n\\end{align}\nSubstituting the expressions of $a(t)$, $\\rho(t)$ and $c(t)$\\, in Eqn.(\\ref{eqn31}), we obtain an expression for the phase in a closed form as,\n\\begin{eqnarray}\n\\Theta_{\\,n\\,,\\,l}(t)\\,&=&\\,\\dfrac{(\\,n+l\\,)}{\\Gamma\\,\\sqrt{M}}\\,\\left[\\,\\sqrt{\\Delta}\\,\\Gamma\\,t\\,+\\,2\\sqrt{\\Delta-M\\omega_0^2}\\,-2\\sqrt{\\Delta-M\\omega_0^2{\\exp\\,(-\\Gamma{t})}}\\right.\\nonumber\\\\\n&&\\left.+\\,2\\sqrt{\\Delta}\\,log\\,\\left(\\frac{\\Delta+\\sqrt{\\Delta[\\Delta-M\\omega_0^2\\,\\exp\\,(-\\Gamma{t}) ]}}{\\Delta+\\sqrt{\\Delta[\\Delta-M\\omega_0^2\\,]}}\\right) \\right]\\nonumber \\\\\n&&-\\,(\\,n+l\\,)\\left[\\dfrac{\\sigma\\,t}{\\mu^2}\\,+\\,\\dfrac{2}{\\Gamma}\\,\\omega_0\\,\\left(e^{-\\Gamma t\/2}-1\\right)\\sqrt{\\,M\\sigma-1} \\right].\\label{eqn50}\n\\end{eqnarray}\n\n\n\n\n\n\n\\subsection{Solution Set-II for Ermakov-Pinney equation: Rationally decaying solutions}\n\\subsubsection{The Solution Set}\nWe now consider rationally decaying solutions of the EP equation similar to that used in~\\cite{Dey} which is of the form,\n\\begin{eqnarray}\n&a(t)=\\dfrac{\\sigma\\,\\left(1+\\dfrac{2}{k}\\right)^{\\,(k+2)\/k}}{(\\Gamma{t}+\\chi)^{\\,(k+2)\/k}}\\nonumber \\\\ \\nonumber\\\\\n&b(t)=\\dfrac{\\Delta\\,\\left(\\dfrac{k}{k+2} \\right)^{(2-k)\/k} }{(\\Gamma{t}+\\chi)^{\\,(k-2)\/k}} \\,\\,\\,\\Rightarrow\\, \\,\\,\\,\\dfrac{\\Delta\\,\\left(1+\\dfrac{2}{k}\\right)^{\\,(k-2)\/k} }{(\\Gamma{t}+\\chi)^{\\,(k-2)\/k}}\\nonumber \\\\ \\nonumber\\\\\n&\\rho(t)=\\dfrac{\\mu\\left(1+\\dfrac{2}{k}\\right)^{1\/k} }{(\\Gamma{t}+\\chi)^{1\/k}}\n\\label{EPsoln2}\n\\end{eqnarray}\nwhere $\\sigma$, $\\Delta$, $\\mu$, $\\Gamma$ and $\\chi$ are constants such that $(\\Gamma{t}+\\chi)~\\neq~0$, and $k$ is an integer. Substituting the expressions of $a(t)$, $b(t)$, and $\\rho(t)$ in the EP equation, we can easily verify the relation between these constants to be as follows, \n\\begin{equation}\n\\Gamma^2\\mu=(k+2)^2\\,(\\sigma\\Delta\\mu-\\frac{\\xi^2\\sigma^2}{\\mu^3}).\n\\label{EPreln2}\n\\end{equation}\n\n\n\n\\subsubsection{Study of the corresponding eigenfunctions}\nThe eigenfunction of the invariant operator $I(t)$ (given by \nEqn.(\\ref{eqn28})) for this solution Set-II is given by,\n\\begin{eqnarray}\n\\phi_{n\\,,\\,m-n}(r,\\theta)=\\lambda_{n}\\,\\dfrac{{(i\\mu)}^{\\,m}}{\\sqrt{m!}}\\left[\\dfrac{k+2}{k(\\Gamma{t}+\\chi)}\\right]^{m\/k} r^{n-m}e^{i\\theta(m-n)-\\dfrac{[\\sigma\\,(k+2)\\,+\\,i\\mu^2\\Gamma]\\,\\,(\\Gamma{t}+\\chi)^{2\/k}\\,\\,\\,k^{2\/k} }{2\\sigma\\,(k+2)^{\\,(k+2)\/k}\\mu^2}r^2}\\nonumber \\\\\n\\times\\,\\,\\,U\\left(-m,1-m+n,\\,\\dfrac{r^2[k(\\Gamma{t}+\\chi)]^{2\/k}}{\\mu^2\\left(k+2\\right)^{2\/k}}\\,\\right)\n\\label{eqn51}\n\\end{eqnarray}\nwhere $\\lambda_n$ is given by \n\\begin{eqnarray}\n\\lambda_n^{\\,2}=\\dfrac{1}{\\pi\\,n!\\mu^{2n+2}}\\left[\\dfrac{k(\\Gamma{t}+\\chi)}{k+2}\\right]^{2(1+n)\/k}.\n\\label{eqn51lam}\n\\end{eqnarray}\nIn order to get the eigenfunction of the Hamiltonian $H(t)$, we need to calculate the associated phase factor. Once again for this we need to fix up the forms of the damping factor $f(t)$ and angular frequency $\\omega(t)$ of the oscillator. In order to \nexplore the solution of $H(t)$ for rationally decaying coefficients, we choose a rationally decaying form for $\\omega(t)$ \nand set $f(t)=1$. Thus, we have the following relations, \n\\begin{eqnarray}\n\\eta(t)=0\\,\\,\\Rightarrow\\,\\,f(t)=1\\\\\n\\omega(t)=\\dfrac{\\omega_0}{(\\Gamma\\,t+\\chi)}~.\n\\end{eqnarray}\nSubstituting these relations in Eqns.(\\ref{3}, \\ref{4}), we get the time dependent NC parameters as, \n\\begin{eqnarray}\n\\theta(t)&=& \\dfrac{2\\,(\\Gamma\\,t+\\chi)}{M\\,\\omega_0}\\,\\sqrt{M\\sigma\\,\\left[\\dfrac{(k+2)}{k\\,(\\Gamma{t}\\,+\\,\\chi)}\\right]^{(k+2)\/k}\\,-\\,1} \\label{eqn52} \\\\ \\nonumber \\\\\n\\Omega(t)&=& \\,2\\,\\sqrt{M\\Delta\\,\\left[\\dfrac{k+2}{k(\\Gamma{t}+\\chi)}\\right]^{\\,(k-2)\/k}\\,-\\,\\dfrac{M^{\\,2}\\omega_0^{\\,2}}{(\\Gamma\\,t+\\chi)^2}}~.\\label{eqn53}\n\\end{eqnarray}\nWe now consider $k=2$. This enables us to integrate the expression for the phase factor (given by Eqn.(\\ref{eqn31})).\nThe simplified forms of $a(t)$, $b(t)$ and $\\rho(t)$ for $k=2$ read,\n\\begin{eqnarray}\na(t)=\\dfrac{4\\sigma}{(\\Gamma{t}+\\chi)^{\\,2}}\\,\\,,\\,\\,b(t)\\,=\\,\\Delta\\,\\,,\\,\\,\\rho(t)=\\left[\\dfrac{2\\mu^{\\,2}}{\\Gamma{t}+\\chi}\\right]^{1\/2}.\n\\end{eqnarray}\nSubstituting these relations in the expression for $c(t)$ in Eqn.(\\ref{eqn3}) gives, \n\\begin{equation}\nc(t)\\,=\\,\\dfrac{\\omega_0}{(\\Gamma\\,t+\\chi)}\\,\\sqrt{\\dfrac{4\\sigma\\,M}{(\\Gamma{t}+\\chi)^2}\\,-\\,1}\\,+\\,\\sqrt{\\dfrac{\\Delta}{M}\\,-\\,\\dfrac{\\omega_0^{\\,2}}{(\\Gamma\\,t+\\chi)^{\\,2}}}\\,\\,\\,\\,.\n\\end{equation} \nSubstituting these expressions for $a(t)$, $\\rho(t)$ and $c(t)$ \nfor $k=2$ in Eqn.(\\ref{eqn31}), we get the following expression \nfor the phase factor in a closed form as,\n\\begin{eqnarray}\n\\Theta_{\\,n, l\\,}(t)&=&\\dfrac{(n+l)}{\\Gamma}\\,\\left[\\omega_0\\,\\,tan^{\\,-1}\\left(\\dfrac{\\omega_0}{\\sqrt{\\frac{\\Delta}{M}{(\\Gamma\\,t+\\chi)^2}-\\omega_0^2}}\\right)+\\sqrt{\\dfrac{\\Delta\\,{(\\Gamma\\,t+\\chi)^2}}{M}-\\omega_0^2}\\,-\\,\\frac{2\\sigma}{\\mu^2}\\,log_{e}\\,\\frac{(\\chi+\\Gamma\\,t)}{\\chi}\\right.\\nonumber\\\\\n&&\\left.-{\\sqrt{\\frac{\\Delta}{M}{\\chi^2}-\\omega_0^2}}-\\omega_0\\,\\,tan^{\\,-1}\\left(\\dfrac{\\omega_0}{\\sqrt{\\frac{\\Delta}{M}{\\chi^2}-\\omega_0^2}}\\right)\\right] \\nonumber\\\\\n&& +\\dfrac{\\omega_0(n+l)}{\\Gamma}\\left[\\dfrac{\\sqrt{4\\,\\sigma\\,M-\\chi^2}}{\\chi}-\\dfrac{\\sqrt{4\\,\\sigma\\,M-(\\chi+\\Gamma\\,t)^2}}{(\\chi+\\Gamma\\,t)}\n\\right.\\nonumber\\\\\n&&\\left. +ilog_{e}\\dfrac{(\\chi+\\Gamma\\,t)+{\\sqrt{(\\chi+\\Gamma\\,t)^2-4\\,\\sigma\\,M}}}{\\chi+\\sqrt{\\chi^2-4\\,\\sigma\\,M}} \\right].\n\\label{eqn55}\n\\end{eqnarray}\nWe can now get the eigenfunction of this rationally decaying damped system using \nEqn.(\\ref{eqnpsi}).\n\n\n\n\n\n\n\n\\subsection{Solution Set-III for Ermakov-Pinney equation: Elementary Solution}\n\\subsubsection{The Solution Set}\nWe now propose a simple method of obtaining a solution of the EP equation.\nThe method is as follows. Choosing $\\rho(t)$ to be any arbitrary time dependent function and taking it's time derivative as proportional to $a(t)$, that is,\n$a(t)=constant \\times\\dot{\\rho}$ and setting \n$b(t)=constant \\times \\dfrac{a}{\\rho^4}$, we observe that these would always satisfy the EP equation along with a certain constraint relation among the constants.\n\n\\noindent Here we consider a simple solution which is a special case of the above solution for the EP equation. We call this the elementary solution which reads,\n\\begin{eqnarray}\na(t)={\\sigma}\\,\\,\\,\\,,\\,\\,\\,b(t)=\\dfrac{{\\Delta}}{{(\\Gamma\\,t\\,+\\,\\chi)^4}}\\,\\,\\,,\\,\\,\\rho(t)=\\mu(\\Gamma{t}\\,+\\,\\chi)\n\\label{eqn57}\n\\end{eqnarray}\nwhere $\\Gamma$, $\\chi$, $\\mu$, $\\sigma$ and $\\Delta$ are constants. The above solution set satisfy the EP equation with the following constraint relation,\n\\begin{equation}\n\\Delta\\mu^4=\\xi^2\\sigma\\,\\,.\n\\end{equation}\n\n\n\n\\subsubsection{Study of the corresponding eigenfunctions}\nThe eigenfunctions of the invariant operator $I(t)$ for this solution set is given by,\n\\begin{eqnarray}\n\\phi_{n,m-n}(r,\\theta)&=&\\lambda_{n}\\dfrac{{[i\\mu(\\Gamma\\,t+\\chi)]}^m}{\\sqrt{m!}}r^{n-m}e^{i\\theta(m-n)-\\dfrac{\\sigma-i\\mu^2\\Gamma(\\Gamma\\,t+\\chi)}{2\\sigma\\mu^2(\\Gamma\\,t+\\chi)^2}r^2}\\nonumber \\\\\n&&\\times\\,\\,U\\left(-m,1-m+n,\\dfrac{r^2}{\\mu^2(\\Gamma\\,t+\\chi)^2} \\right)\n\\label{eqn58}\n\\end{eqnarray}\nwhere $\\lambda_n$ is given by\n\\begin{eqnarray}\n\\lambda_n^2=\\dfrac{1}{\\pi{n!}{\\left[\\mu(\\Gamma\\,t+\\chi) \\right]}^{2+2n}}~. \n\\label{eqn58lam}\n\\end{eqnarray}\nIn order to get an eigenfunction of the Hamiltonian, we calculate the phase factor for a particular case of the damped harmonic oscillator where the angular frequency $\\omega(t)$ is rationally decaying and the damping factor $f(t)$=1. Thus, we set, \n\\begin{eqnarray}\n\\eta(t)=0\\,\\,\\Rightarrow\\,\\,f(t)=1\\\\\n\\omega(t)=\\dfrac{\\omega_0}{(\\Gamma\\,t+\\chi)}\n\\end{eqnarray}\nwhere $\\Gamma$ and $\\chi$ are real constants.\nSubstituting these relations in Eqns.(\\ref{3}, \\ref{4}), we get the time dependent NC parameters as, \n\\begin{eqnarray}\n\\theta(t)=\\dfrac{2\\,(\\Gamma\\,t+\\chi)}{\\omega_0\\,M}\\,\\sqrt{M\\,\\sigma-1}\\label{eqn59}\\\\\n\\Omega(t)=2\\sqrt{\\dfrac{M\\Delta}{(\\Gamma\\,t+\\chi)^4}-\\dfrac{M^2\\,\\omega_0^2}{(\\Gamma\\,t+\\chi)^{2}}}~.\\label{eqn60}\n\\end{eqnarray}\nSubstituting these relations \nin the expression for $c(t)$ in Eqn.(\\ref{eqn3}), we get, \n\\begin{align}\nc(t)=\\sqrt{\\dfrac{\\Delta}{M(\\Gamma\\,t+\\chi)^4}-\\dfrac{\\omega_0^2}{(\\Gamma\\,t+\\chi)^2}}\\,+\\,\\dfrac{\\omega_0}{(\\Gamma\\,t+\\chi)}\\sqrt{M\\,\\sigma-1}.\\label{eqn61}\n\\end{align}\nSubstituting these expressions of $a(t)$, $\\rho(t)$ and $c(t)$ in Eqn.(\\ref{eqn31}), we obtain an expression for the phase factor in a closed form as, \n\\begin{align}\n\\Theta_{\\,n\\,,\\,l}(t)&=\\,(n+l\\,)\\left[\\omega_0\\dfrac{\\sqrt{M\\,\\sigma-1}}{\\Gamma}\\log\\,\\dfrac{(\\Gamma\\,t+\\chi)}{\\chi}-\\frac{{\\sigma}t}{\\mu^2\\chi(\\Gamma\\,t+\\chi)} \\right] +\\,\\dfrac{(\\,n+l\\,)}{\\Gamma}\\left[\\sqrt{\\dfrac{\\Delta}{M\\chi^2}-\\omega_0^2}\\right.\\nonumber\\\\\n&\\left.-\\sqrt{\\dfrac{\\Delta}{M(\\Gamma\\,t+\\chi)^2}-\\omega_0^2}\n+\\omega_0\\left\\{\\tan^{-1}\\left(\\dfrac{\\omega_0\\chi}{\\sqrt{\\dfrac{\\Delta}{M}-\\chi^2\\omega_0^2}}\\right)-\\tan^{-1}\\left(\\dfrac{\\omega_0(\\Gamma\\,t+\\chi)}{\\sqrt{\\dfrac{\\Delta}{M}-\\omega_0^2(\\Gamma\\,t+\\chi)^2}}\\right)\\right\\}\\,\\, \\right].\n\\label{eqn62}\n\\end{align} \nWe can now get the eigenfunction of this system by using Eqn.(\\ref{eqnpsi}).\n\n\n\n\\section{Expectation Values}\nIn this section, we intend to calculate the expectation value of energy. For \nthis we need to calculate the expectation value of the Hamiltonian $H(t)$ in \nit's own eigenstates. \nThe expectation value $\\langle H \\rangle$ is given by (using Eqn.(\\ref{eqn2})), \n\\begin{equation}\n\\langle H\\rangle = \\dfrac{a(t)}{2}(\\langle{p_1}^2\\rangle+\\langle{p_2}^2\\rangle)+\\dfrac{b(t)}{2}(\\langle{x_1}^2\\rangle+\\langle{x_2}^2\\rangle)+c(t)(\\langle{p_1}{x_2}\\rangle-\\langle{p_2}{x_1}\\rangle)\\,\\,\\,.\\label{eqn63}\n\\end{equation}\nTo calculate this we need to get the expectation value of the individual canonical \noperators. To set up our notation we denote the eigenstates of the Hamiltonian $H(t)$ \nby $|n,l\\rangle_H$. \n\n\n\n\n\\subsection{Matrix elements of the coordinate operators raised to arbitrary finite powers}\n\\noindent We start by calculating the matrix element of an arbitrary power of $x$, $_{H}\\langle n,l|x^k|n,l\\rangle_{H}$, which is given by\n\\begin{eqnarray} \n_{H}\\langle n,m-n|x^k|n,m'-n\\rangle_{H}&=&\\int r dr d\\theta\n~_{H}\\langle n,m-n |r,\\theta\\rangle\\langle r,\\theta|r^k cos^k\\theta|n,m'-n\\rangle_{H}\\nonumber\\\\\n&=&\\dfrac{1}{2^k}e^{i(\\Theta_{n,m'-n}-\\Theta_{n,m-n})}\\int r^{k+1} dr d\\theta~(e^{i\\theta}+e^{-i\\theta})^k \\nonumber\\\\\n&&~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\times\\phi^*_{n,m-n}(r,\\theta)\\phi_{n,m'-n}(r,\\theta)\\nonumber\\\\ \n\\label{eqn64}\n\\end{eqnarray} \nwhere we have used the relations, \n$|n,l\\rangle_{H}~=~e^{i\\Theta_{n,l}}|n,l\\rangle$ where $|n,l\\rangle_H$ and \n$|n,l\\rangle$ are eigenstates of the Hamiltonian $H(t)$ and Lewis invariant $I(t)$ \nrespectively. We have also used the relation $\\langle r,\\theta|n,m'-n\\rangle$~=~$\\phi_{n,m'-n}(r,\\theta)$, with $\\phi$ being the eigenfunction of $I(t)$.\nNow, Eqn.(\\ref{eqn64}) can be rewritten as,\n\\begin{eqnarray}\n_{H}\\langle n,m-n|x^k|n,m'-n\\rangle_{H}&=&\\dfrac{\\pi}{2^{k-1}} \\sum_{r=0}^{k} {^{k}C_r}\\delta_{m',m+2r-k} A(n,m,m+2r-k)\\nonumber\\\\\n&\\times& \\int_0^\\infty r~dr~r^{2(n-m-r+k)}\ne^{\\dfrac{-r^2}{\\hbar \\rho^2}} \\nonumber\\\\\n&\\times& U\\left(-m,1-m+n,\\dfrac{r^2}{\\hbar\\rho^2} \\right)\\nonumber\\\\\n&\\times& U\\left(-m-2r+k,1-m-2r+k+n,\\dfrac{r^2}{\\hbar\\rho^2} \\right)\n\\label{eqn67}\n\\end{eqnarray}\nwhere $A(n,m,m+2r-k)=e^{i(\\Theta_{n,m-n+2r-k}-\\Theta_{n,m-n})}\\lambda_n^2\\dfrac{(-i\\hbar^{1\/2}\\rho)^{m}(i\\hbar^{1\/2}\\rho)^{m+2r-k}}{\\sqrt{m!(m+2r-k)!}}$~. \n\n\\noindent Now defining $w=-\\dfrac{r^2}{\\hbar \\rho^2}$, we have, \n\\begin{eqnarray}\n_{H}\\langle n,m-n|x^k|n,m'-n\\rangle_{H}&=&\\sum_{r=0}^{k}\\frac{\\pi}{2^k}e^{i(\\Theta_{n,m-n+2r-k}-\\Theta_{n,m-n})}(-1)^{k+r}i^{-k}~{^{k}C_r}\\delta_{m',m+2r-k}\\nonumber\\\\\n&\\times& \\lambda_n^2(\\hbar^{1\/2} \\rho)^{2n+k+2}\\sqrt{m!(m+2r-k)!}\\nonumber\\\\\n&\\times& \\int_0^\\infty dw w^{n-m-r+k}e^{-w} L_m^{(n-m)}(w) L_{m+2r-k}^{(n-m-2r+k)}(w)\n\\label{eqn68}\n\\end{eqnarray}\nwhere we have used the following result on special functions \\cite{Arfken, uva},\n\\begin{eqnarray}\nL_n^{(\\zeta)}(w)=\\frac{(-1)^n}{n!}U(-n,\\zeta+1,w) \\label{eqn69}\n\\label{eqn70}\n\\end{eqnarray}\nwhere $L_n^{(\\zeta)}(w)$ are associated Laguerre polynomials.\n\n\\noindent Now, we get using the relation for phase given in \\cite{Dey},\n\\begin{eqnarray}\n\\Theta_{n,l}=(n+l)\\int^t \\left[c(\\tau)-\\frac{a(\\tau)}{\\rho^2 (\\tau)}\\right]d\\tau\n\\label{eqn73}\n\\end{eqnarray} \nthe following relation,\n\\begin{eqnarray}\ne^{i(\\Theta_{n,m-n+2r-k}-\\Theta_{n,m-n})}&=&e^{i[\\{(n+m-n+2r-k)-(n+m-n)\\}\\int^t (c(\\tau)-\\frac{a(\\tau)}{\\rho^2 (\\tau)})d\\tau]} \\nonumber\\\\\n&=& e^{i(0+2r-k)\\int^t (c(\\tau)-\\frac{a(\\tau)}{\\rho^2 (\\tau)})d\\tau}\\nonumber\\\\\n&=&e^{i\\Theta_{0,2r-k}}.\n\\label{eqn74}\n\\end{eqnarray}\nSo, we finally get the following relation for the matrix element of $x^k$,\n\\begin{eqnarray}\n_{H}\\langle n,m-n|x^k|n,m'-n\\rangle_{H}&=&\\sum_{r=0}^{k}\\frac{\\pi}{2^k}e^{i\\Theta_{0,2r-k}}(-1)^{k+r}i^{-k}~{^{k}C_r}\\delta_{m',m+2r-k}\\nonumber\\\\\n&\\times& \\lambda_n^2(\\hbar^{1\/2} \\rho)^{2n+k+2}\\sqrt{m!(m+2r-k)!}\\nonumber\\\\\n&\\times& \\int_0^\\infty dw~w^{n-m-r+k}e^{-w} L_m^{(n-m)}(w) L_{m+2r-k}^{(n-m-2r+k)}(w).\n\\label{eqn75}\n\\end{eqnarray}\nThis is a new result in this paper and can be used to obtain the matrix element or expectation value of any power of $x$.\nFor the sake of completeness, we also write down the matrix element of $x^k$ in the eigenstates of the Lewis invariant $I(t)$, which reads\n\\begin{eqnarray}\n\\langle n,m-n|x^k|n,m'-n\\rangle&=&\\sum_{r=0}^{k}\\frac{\\pi}{2^k}(-1)^{k+r}i^{-k}~{^{k}C_r}\\delta_{m',m+2r-k}\\nonumber\\\\\n&\\times& \\lambda_n^2(\\hbar^{1\/2} \\rho)^{2n+k+2}\\sqrt{m!(m+2r-k)!}\\nonumber\\\\\n&\\times& \\int_0^\\infty dw~w^{n-m-r+k}e^{-w} L_m^{(n-m)}(w) L_{m+2r-k}^{(n-m-2r+k)}(w).\n\\label{eqn75I}\n\\end{eqnarray}\nNote that the phase factor does not appear in the above result.\n\n\\noindent Now, we proceed to evaluate the matrix element \n$_{H}\\langle n,m-n|x|n,m'-n\\rangle_{H}$ using the expression \nobtained in Eqn.(\\ref{eqn75}). This reads\n\\begin{eqnarray}\n_{H}\\langle n,m-n|x|n,m'-n\\rangle_{H}=\n_{H}\\langle n,m-n|x^k\\mid_{\\,k=1;\\,r=0}|n,m'-n\\rangle_{H}\\nonumber\\\\\n+ _{H}\\langle n,m-n|x^k\\mid_{\\,k=1;\\,r=1}|n,m'-n\\rangle_{H}.\n\\label{eqn76}\n\\end{eqnarray}\nEvaluating the above matrix elements give,\n\\begin{eqnarray}\n_{H}\\langle n,m-n|x^k\\mid_{\\,k=1;\\,r=0}|n,m'-n\\rangle_{H}= -\\frac{i}{2}\n{(\\rho\\hbar^{1\/2})}\\sqrt{m}e^{-i\\Theta_0,1}\\delta_{m,m'+1}\n\\label{eqn77}\n\\end{eqnarray}\n\\begin{eqnarray}\n_{H}\\langle n,m-n|x^k\\mid_{\\,k=1;\\,r=1}|n,m'-n\\rangle_{H}=\\frac{i}{2}\n{(\\rho\\hbar^{1\/2})}\\sqrt{m'}e^{i\\Theta_0,1}\\delta_{m',m+1}.\n\\label{eqn78}\n\\end{eqnarray}\nIn order to obtain Eqn(s).(\\ref{eqn77}, \\ref{eqn78}), we \nused the following relations involving the associated Laguerre polynomials,\n\\begin{eqnarray}\nL_n^{(\\zeta)}(w)=L_n^{(\\zeta+1)}(w)-L_{n-1}^{(\\zeta+1)}(w) \\nonumber\\\\\n\\int_0^\\infty dw~w^{\\zeta} e^{-w}L_n^{(\\zeta)} (w)L_m^\\zeta (w) =\\frac{(n+\\zeta)!}{n!}\\delta_{n,m}~.\n\\label{eqn80}\n\\end{eqnarray}\nCombining Eqn(s).(\\ref{eqn77}, \\ref{eqn78}), we get the following expression,\n\\begin{eqnarray}\n_{H}\\langle n,m-n|x|n,m'-n\\rangle_{H}~=~\\frac{i}{2}\n{(\\rho\\hbar^{1\/2})}[\\sqrt{m'}e^{i\\Theta_{0,1}}\\delta_{m',m+1}\n-\\sqrt{m}e^{-i\\Theta_{0,1}}\\delta_{m,m'+1}].\n\\label{eqn79}\n\\end{eqnarray}\nNext, we evaluate,\n\\begin{eqnarray}\n_{H}\\langle n,m-n|x^2|n,m'-n\\rangle_{H}= _{H}\\langle n,m-n|x^k\\mid_{\\,k=2;\\,r=0}|n,m'-n\\rangle_{H}\\nonumber\\\\\n+_{H}\\langle n,m-n|x^k\\mid_{\\,k=2;\\,r=1}|n,m'-n\\rangle_{H}+ _{H}\\langle n,m-n|x^k\\mid_{\\,k=2;\\,r=2}|n,m'-n\\rangle_{H}. \n\\label{eqn81}\n\\end{eqnarray}\nEvaluation of the above matrix elements yield, \n\\begin{eqnarray}\n_{H}\\langle n,m-n|x^k\\mid_{\\,k=2;\\,r=0}|n,m'-n\\rangle_{H}&=& \n-\\frac{1}{4}{(\\hbar\\rho^2)}e^{-i\\Theta_{0,2}}\\delta_{m',m-2}\\sqrt{m(m-1)}\\nonumber\\\\\n_{H}\\langle n,m-n|x^k\\mid_{\\,k=2;\\,r=1}|n,m'-n\\rangle_{H}&=&~\\frac{1}{2}{(\\hbar\\rho^2)}e^{-i\\Theta_{0,0}}\\delta_{m,m'}(m+n+1)\\nonumber\\\\\n_{H}\\langle n,m-n|x^k\\mid_{\\,k=2;\\,r=2}|n,m'-n\\rangle_{H}&=& \n-\\frac{1}{4}{(\\hbar\\rho^2)}e^{i\\Theta_{0,2}}\\delta_{m',m+2}\\sqrt{(m+2)(m+1)}.\n\\label{eqn82}\n\\end{eqnarray}\nIn order to calculate the above expressions, apart from the relations between \nspecial functions given by Eqn.(\\ref{eqn80}), we need the following relation,\n\\begin{eqnarray}\n\\int_{0}^{\\infty} dw~w^{k+p}e^{-w}L_n^k(w)L_n^k(w)~=~\\frac{(n+k)!}{n!}\\times(2n+k+1)^p ~.\n\\label{eqn83}\n\\end{eqnarray}\nSo we have,\n\\begin{eqnarray}\n_{H}\\langle n,m-n|x^2|n,m'-n\\rangle_{H}&=&\\frac{(\\hbar\\rho^2)}{2}\\delta_{m,m'}(m+n+1)\\nonumber\\\\\n&&-\\frac{(\\hbar\\rho^2)}{4}\\left[e^{-i\\Theta_{0,2}}\\delta_{m',m-2}\\sqrt{m(m-1)}\\right.\\nonumber\\\\\n&& \\left. +e^{i\\Theta_{0,2}}\\delta_{m',m+2}\\sqrt{(m+2)(m+1)}\\right].\n\\label{eqn84}\n\\end{eqnarray}\nIt is to be noted that the matrix elements for $x$ and $x^2$ in the \neigenstates of the Hamiltonian [given by Eqn(s).(\\ref{eqn79}, \\ref{eqn84}) \nrespectively], matches exactly with the corresponding expression \ngiven in \\cite{Dey}, although the result \nquoted in \\cite{Dey} is in the eigenstate of the invariant $I(t)$.\n\n\\noindent The matrix element of $y^k$ in the \neigenstates of the Hamiltonian can be obtained similarly, and reads,\n\\begin{eqnarray}\n_{H}\\langle n,m-n|y^k|n,m'-n\\rangle_{H}&=&\\sum_{r=0}^{k}\\frac{\\pi}{2^k}e^{i\\Theta_{0,2r-k}}~{^{k}C_r}\\delta_{m',m+2r-k}\\nonumber\\\\\n&\\times& \\lambda_n^2(\\hbar^{1\/2} \\rho)^{2n+k+2}\\sqrt{m!(m+2r-k)!}\\nonumber\\\\\n&\\times& \\int_0^\\infty dw~w^{n-m-r+k}e^{-w} L_m^{(n-m)}(w) L_{m+2r-k}^{(n-m-2r+k)}(w).\n\\label{eqn85}\n\\end{eqnarray}\nOnce again we write down the matrix element of $y^k$ in the eigenstates of the Lewis invariant $I(t)$. This reads\n\\begin{eqnarray}\n\\langle n,m-n|y^k|n,m'-n\\rangle&=&\\sum_{r=0}^{k}\\frac{\\pi}{2^k}~{^{k}C_r}\\delta_{m',m+2r-k}\\nonumber\\\\\n&\\times& \\lambda_n^2(\\hbar^{1\/2} \\rho)^{2n+k+2}\\sqrt{m!(m+2r-k)!}\\nonumber\\\\\n&\\times& \\int_0^\\infty dw~w^{n-m-r+k}e^{-w} L_m^{(n-m)}(w) L_{m+2r-k}^{(n-m-2r+k)}(w).\n\\label{eqn85I}\n\\end{eqnarray}\nUsing Eqn.(\\ref{eqn85}), we may evaluate the matrix element of $y$ and $y^2$ \nin the eigenstate of the Hamiltonian. We find, \n\\begin{eqnarray}\n_{H}\\langle n,m-n|y|n,m'-n\\rangle_{H}&=&\n_{H}\\langle n,m-n|y^k\\mid_{\\,k=1;\\,r=0}|n,m'-n\\rangle_{H}\\nonumber\\\\&&+ _{H}\\langle n,m-n|y^k\\mid_{\\,k=1;\\,r=1}|n,m'-n\\rangle_{H}\n\\nonumber\\\\\n&=&-\\frac{1}{2}\n{(\\rho\\hbar^{1\/2})}[\\sqrt{m}e^{-i\\Theta_{0,1}}\\delta_{m',m-1}\n+\\sqrt{m+1}e^{i\\Theta_{0,1}}\\delta_{m',m+1}].\n\\label{eqn86}\n\\end{eqnarray}\n\\begin{eqnarray}\n_{H}\\langle n,m-n|y^2|n,m'-n\\rangle_{H}&=&_{H}\\langle n,m-n|y^k\\mid_{\\,k=2;\\,r=0}|n,m'-n\\rangle_{H}\\nonumber\\\\\n&&+_{H}\\langle n,m-n|y^k\\mid_{\\,k=2;\\,r=1}|n,m'-n\\rangle_{H}\\nonumber\\\\\n&&+_{H}\\langle n,m-n|y^k\\mid_{\\,k=2;\\,r=2}|n,m'-n\\rangle_{H} \\nonumber\\\\\n&=&\\frac{\\hbar\\rho^2}{4}\\delta_{m',m-2}\\sqrt{m(m-1)}e^{-i\\Theta_{0,2}}\\nonumber\\\\\n&&+\\frac{1}{2}\\delta_{m,m'}{(\\hbar\\rho^2)}(m+n+1)+\\frac{\\hbar\\rho^2}{4}\\delta_{m',m+2}\\sqrt{(m+2)(m+1)}e^{i\\Theta_{0,2}}.\\nonumber\\\\\n\\label{eqn87}\n\\end{eqnarray}\nFrom the above analysis, we find that even the expression for the matrix element of the operator $y^k$ in the eigenstate of $H(t)$ \nmatches with that found in \\cite{Dey} for $k=1, 2$, \nthough again they had inappropriately quoted the results in the \neigenstate of the Lewis invariant. \n\n\n\n\\subsection{Analysis of the expectation value of energy}\nAs we have already seen from Eqn.(\\ref{eqn2}), in order to calculate the expectation value of energy one needs the expectation values $\\braket{{p_1}^2}$, $\\braket{{p_2}^2}$, $\\braket{{x_1}^2}$, $\\braket{{x_2}^2}$, $\\braket{{p_1}{x_2}}$ and \n$\\braket{{p_2}{x_1}}$. As we have seen in the previous subsection, our calculated generalized expressions for matrix elements $_{H}\\langle n,m-n|x^k\\mid_{\\,k=1;\\,r=0}|n,m'-n\\rangle_{H}$ and $_{H}\\langle n,m-n|y^k\\mid_{\\,k=1;\\,r=0}|n,m'-n\\rangle_{H}$ matched exactly with the calculations in \\cite{Dey} for $k=1, 2$. Hence, we use the matrix elements quoted in the said work to calculate \nthe following expectation values,\n\\begin{eqnarray}\n\\braket{x_j^2}=\\dfrac{\\rho^2}{2}(n+m+1)\\,\\,;\\,\\,\\braket{p_j^2}=\\dfrac{1}{2}\\left(\\dfrac{1}{\\rho^2}+\\dfrac{\\dot{\\rho}^2}{a^2} \\right)\\,(n+m+1)\\,\\,;\\,\\,\\braket{x_j\\,p_k}=\\dfrac{1}{2}\\,\\epsilon_{jk}(m-n)\\,\\,;\\label{eqn88}\n\\end{eqnarray}\nwhere $j,k=1,2$ and $\\epsilon_{jk}=-\\epsilon_{kj}$ with $\\epsilon_{12}=1$. So, the expectation value of energy $\\braket{E_{n,m-n}(t)}$ with \nrespect to energy eigenstate $\\psi_{n,m-n}(r,\\theta,t)$ can be expressed as,\n\\begin{align}\n&\\braket{E_{n,m-n}(t)}=\\dfrac{1}{2}\\,(n+m+1)\\left[b(t)\\rho^2(t)+\\dfrac{a(t)}{\\rho^2(t)}+\\dfrac{\\dot{\\rho}^2(t)}{a(t)} \\right]+c(t)\\,(n-m)\\,\\,.\\nonumber\\\\\n&=\\dfrac{1}{2}\\left[\\,(n+m+1)\\left(b(t)\\rho^2(t)+\\dfrac{a(t)}{\\rho^2(t)}+\\dfrac{\\dot{\\rho}^2(t)}{a(t)} \\right)+(n-m)\\left(\\dfrac{f(t)\\Omega(t)}{M}+\\dfrac{M\\omega^2(t)\\theta(t)}{f(t)}\\right) \\right].\n\\label{eqn89}\n\\end{align}\n\n\n\n\n\\noindent It is interesting to note that even when the frequency of oscillation $\\omega{\\rightarrow}0$, the \nexpectation value of energy is non-zero. This is because all the three parameters of the Hamiltonian $a(t)$, $b(t)$ and $c(t)$ are finite even as $\\omega{\\rightarrow}0$, as is clear from the Eqn(s).(\\ref{3},\\ref{4},\\ref{eqn3}). Now we will proceed to study the time-dependent behaviour of $\\braket{E_{n,m-n}(t)}$ for various types of damping.\n\n\n\n\n\\subsubsection{Exponentially decaying solution}\nFor the exponentially decaying solution given by Eqn.(\\ref{EPsoln1}), the energy expectation value takes the following form,\n\\begin{equation}\n\\braket{E_{n,m-n}(t)}=(n+m+1)\\mu^2\\Delta+c(t)\\,(n-m)\n\\label{eqn90}\n\\end{equation}\nwhere we have set the constant $\\xi^2$ to unity and used the constraint relation given by Eqn.(\\ref{EPreln1}).\n\n\\vskip 0.1cm\n\n\n\n\\noindent{\\bf $\\langle A\\rangle$ Solution Set-Ia}\n\\vskip 0.15cm\n\n\\noindent For this case we consider $f(t)=1$ and $\\omega(t)=\\omega_0\\,e^{-\\Gamma\\,t\/2}$. The expectation value of \nenergy for the ground state has the following expression,\n\\begin{eqnarray}\n\\braket{E_{n,-n}(t)}&=&(n+1)\\mu^2\\Delta+\\,n\\,\\left[\\sqrt{\\dfrac{ \\Delta\\,exp\\,(\\Gamma{t})-M{\\omega_0}^2\\,exp\\,(-\\Gamma{t}) }{M}} \n\\,\\right.\\nonumber\\\\\n&&~~~~~~~~~~~~~~~~~~~~~~~~~~\\left. +\\,\\omega_0\\,exp\\,(-\\Gamma{t\/2})\\sqrt{\\,M\\sigma\\,exp\\,(-\\Gamma{t})-1 \\,}\\right].\n\\label{eqn91}\n\\end{eqnarray}\nFrom Eqn.(\\ref{eqn91}), we see that the expectation value of the energy becomes complex beyond a certain time limit. The \ncondition for getting the expectation value of energy to be real is as follows,\n\\begin{eqnarray}\nM\\,\\sigma\\,e^{-\\Gamma\\,t}>1\\,\\,\\Rightarrow\\,t\\leq\\,\\dfrac{ln(M\\,\\sigma)}{\\Gamma}~.\\label{eqn92}\n\\end{eqnarray}\n\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[scale=0.4]{expdecay.eps}\n\\caption{\\textit{A study of the variation of expectation value of energy, scaled by \n$\\frac{1}{\\omega_0}$ ($\\frac{\\langle E \\rangle}{\\omega_0}$) in order to make \nit dimensionless, as we vary $\\Gamma$t (again a dimensionless quantity). Here \nwe consider mass M=1, $\\mu$=1,$\\Delta$=$10^7$, $\\sigma$=$10^7$, \n$\\omega_0$=$10^3$ and $\\Gamma$=1 in natural units. The expectation value of \nenergy $\\langle E \\rangle$ is calculated for exponentially decaying \nHamiltonian parameters when $\\langle A\\rangle$ Set-IA $f(t)=1$ and \n$\\omega(t)=\\omega_0 e^{-{\\Gamma}t\/2}$; $\\langle B\\rangle$ Set-IB \n$f(t)=e^{-{\\Gamma}t}$ and $\\omega(t)=\\omega_0$ and $\\langle C\\rangle$ \nSet-IC $f(t)=e^{-{\\Gamma}t}$ and $\\omega(t)=\\omega_0 e^{-{\\Gamma}t\/2}$. While \nfor $\\langle A\\rangle$ the energy first decreases, then increases with time, \nfor $\\langle B\\rangle$ the energy remains constant as we vary time. \nFor $\\langle C\\rangle$ the energy decays off with time.}} \n\\label{fig1}\n\\end{figure}\n\n\n\n\\noindent We see from Fig.(\\ref{fig1}), that the energy initially decays but then increases with time. This is \nbecause for large time at which $exp\\,(-\\Gamma{t\/2})\\approx\\,0 $, the \napproximated expression of energy reads\n\\begin{equation}\nE_{n,-n}(t)\\approx\\,(n+1)\\mu^2\\Delta+\\,n\\,\\sqrt{\\dfrac{ \\Delta\\,exp\\,(\\Gamma{t}) }{M}}\\,\\,\n\\label{e7}\n\\end{equation}\nwhich is still increasing with time. The reason for the increase of energy with time is the form of the \ncoefficient $b(t)$ in the Hamiltonian. Although the coefficient $a(t)$ is exponentially decaying with \ntime, the coefficient $b(t)$ exponentially increases with time in order to satisfy EP equation. However, since there is an upper limit of time within which the energy remains real, so the energy remains finite within the allowed time interval.\n\n\\vskip 0.1cm\n\n\n\n\n\\noindent{{\\bf{$\\langle B\\rangle$ Solution Set-Ib}}}\n\n\\noindent Here we set $f(t)=e^{-\\Gamma\\,t}$ and $\\omega(t)=\\omega_0$. With this the energy expression for the ground state takes the form,\n\\begin{equation}\n\\braket{E_{n,-n}(t)}=(n+1)\\mu^2\\Delta+\\,n\\,\\left[\\sqrt{\\dfrac{ \\Delta\\,-M{\\omega_0}^2\\, }{M}} \\,+\\,\\omega_0\\sqrt{M\\sigma-1}\\right].\\label{e8}\n\\end{equation}\n\n\n\\noindent We note from Fig.(\\ref{fig1}), that the expectation value of the energy remarkably remains constant as we vary time, as is observed from Eqn.(\\ref{e8}). This must be because the effect of the exponentially decaying Hamiltonian \ncoefficient $a(t)$ and damping term $f(t)$ gets balanced out by the exponentially increasing Hamiltonian \ncoefficient $b(t)$.\n\\vskip 0.1cm\n\n\n\n\\noindent{{\\bf{$\\langle C\\rangle$ Solution Set-Ic}}}\n\n\\noindent Here we set $f(t)=e^{-\\Gamma\\,t}$ and $\\omega(t)=\\omega_0\\,e^{-\\Gamma\\,t\/2}$. \nWith this the expectation value of the energy expression takes the form,\n\\begin{equation}\n\\braket{E_{n,-n}(t)}=(n+1)\\mu^2\\Delta+\\,n\\,\\left[\\sqrt{\\dfrac{ \\Delta\\,-M{\\omega_0}^2\\,exp\\left[-\\Gamma{t}\\right] }{M}} \n + {\\omega_0}\\,exp\\,(-\\Gamma{t\/2})\\sqrt{M{\\sigma}\\,-1}\\right].\\label{e9}\n\\end{equation}\nThe above expression gives a very nice decaying expression for the expectation value of energy with respect to time, and finally approaching a constant value in the limit \n$t\\rightarrow\\infty$. This behaviour is also exhibited in the nature of the plot of variation of the expectation value of energy with time seen in Fig.(\\ref{fig1}).\n\n\\vskip 0.1cm\n\n\n\n\\subsubsection{Rationally decaying solution}\n\n\\noindent In this case the expectation value of energy for $k=2$ reads\n\\begin{eqnarray}\nE_{n,-n}(t)&=&\\dfrac{(n+1)}{2(\\Gamma\\,t+\\chi)}\\left[2\\left(\\dfrac{\\sigma}{\\mu^2}+\\Delta\\mu^2\\right)+\\dfrac{\\mu^2\\Gamma^2}{8\\sigma} \\right]\\nonumber\\\\\n&&~~~~~~~~~~~~~~~~~~~~~~~ +n\\left[\\dfrac{\\omega_0}{\\Gamma\\,t+\\chi}\\,\\sqrt{\\dfrac{4\\sigma\\,M}{(\\Gamma{t}+\\chi)^2}\\,-\\,1}\\,+\\,\\sqrt{\\dfrac{\\Delta}{M}-\\dfrac{\\omega_0^{\\,2}}{(\\Gamma\\,t+\\chi)^{\\,2}}} \\right].\\label{e10}\n\\end{eqnarray}\n\\noindent Note that although it has a nice decaying property like the damping case on commutative plane, there is an upper bound of time above which the energy ceases to be real. The upper bound on time reads,\n\\begin{eqnarray}\n4\\sigma\\,M\\,\\geq\\,(\\Gamma\\,t+\\chi)^2\\,\\,\\Rightarrow\\,\\,t\\,\\leq\\,\\dfrac{1}{\\Gamma}(2\\sqrt{M\\,\\sigma}-\\chi).\n\\label{e11}\n\\end{eqnarray}\n\n\n\\vskip 0.1cm\n\\begin{figure}[t]\n\\centering\n\\includegraphics[scale=0.4]{ratdecay.eps}\n\\caption{\\textit{A study of the variation of expectation value of energy, scaled by \n$\\frac{1}{\\omega_0}$ ($\\frac{\\langle E \\rangle}{\\omega_0}$) in order to make \nit dimensionless, as we vary $\\Gamma$t (again a dimensionless quantity). Here \nwe consider mass M=1, $\\mu$=1,$\\Delta$=$10^7$, $\\sigma$=$10^7$, \n$\\omega_0$=$10^3$, $\\chi=1$ and $\\Gamma$=1 in natural units. The expectation value of \nenergy $\\langle E \\rangle$ is calculated for rationally decaying \nHamiltonian parameters. We consider $f(t)=1$ and $\\omega(t)=\\dfrac{\\omega_0}{(\\Gamma\\,t+\\chi)}$.}} \n\\label{fig2}\n\\end{figure}\n\n\\noindent From Fig.(\\ref{fig2}), we see indeed the expectation value of energy $\\langle E \\rangle$ decays with time \nfollowing power law as expected for the rationally decaying solutions. \n\n\n\\subsubsection{Elementary solution}\nFor the elementary solution set, the expectation value of the energy reads,\n\\begin{eqnarray}\n\\braket{E_{n,-n}(t)}&=&\\dfrac{1}{2}(n+1)\\left[\\left(\\Delta\\mu^2+\\dfrac{\\sigma}{\\mu^2}\\right)\\dfrac{1}{(\\Gamma\\,t+\\chi)^2}+\\dfrac{\\mu^2\\Gamma^2}{\\sigma}\\right]\n\\nonumber\\\\&&+n\\left[\\dfrac{\\omega_0\\sqrt{M\\sigma-1}}{(\\Gamma\\,t+\\chi)}+\\dfrac{1}{(\\Gamma\\,t+\\chi)}\\sqrt{\\dfrac{\\Delta}{M\\,(\\Gamma\\,t+\\chi)^2}-\\omega_0^2 } \\right].\\label{e12}\n\\end{eqnarray}\nFurther, the constraint relation $\\Delta\\mu^4=\\xi^2\\sigma$ results in the following form for the expectation value of energy (setting $\\xi^2=1$),\n\\begin{align}\n\\braket{E_{n,-n}(t)}\n&=\\dfrac{1}{2}(n+1)\\left[\\dfrac{2\\sigma}{\\mu^2(\\Gamma\\,t+\\chi)^2}+\\dfrac{\\mu^2\\Gamma^2}{\\sigma}\\right]+n\\,\\left[\\dfrac{\\omega_0\\sqrt{M\\sigma-1}}{(\\Gamma\\,t+\\chi)}+\\dfrac{1}{(\\Gamma\\,t+\\chi)}\\sqrt{\\dfrac{\\Delta}{M\\,(\\Gamma\\,t+\\chi)^2}-\\omega_0^2}\\right].\\label{e130}\n\\end{align}\nThis expression also provides an upper bound of the time limit above which the expectation value of energy would become complex. This upper bound reads,\n\\begin{eqnarray}\n\\dfrac{\\Delta}{M\\,(\\Gamma\\,t+\\chi)^2}\\,\\geq\\,\\omega_0^2\\,\\Rightarrow\\,t\\,\\leq\\,\\dfrac{1}{\\Gamma}\\left[ \\dfrac{1}{\\omega_0}\\sqrt{\\dfrac{\\Delta}{M}}-\\chi \\right].\\label{e14}\n\\end{eqnarray}\n\\begin{figure}[t]\n\\centering\n\\includegraphics[scale=0.4]{elmdecay.eps}\n\\caption{\\textit{A study of the variation of expectation value of energy, scaled by \n$\\frac{1}{\\omega_0}$ ($\\frac{\\langle E \\rangle}{\\omega_0}$) in order to make \nit dimensionless, as we vary $\\Gamma$t (again a dimensionless quantity). Here \nwe consider mass M=1, $\\mu$=1,$\\Delta$=$10^7$, $\\sigma$=$10^7$, \n$\\omega_0$=$10^3$, $\\chi=1$ and $\\Gamma$=1 in natural units. The expectation value of \nenergy $\\langle E \\rangle$ is calculated for elementarily decaying \nHamiltonian parameters. We consider $f(t)=1$ and $\\omega(t)=\\dfrac{\\omega_0}{(\\Gamma\\,t+\\chi)}$.}} \n\\label{fig3}\n\\end{figure}\n\n\\noindent In Fig.(\\ref{fig3}), we observe that the expectation value of energy again undergoes a power law decay with time for the elementary solution.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\section{Conclusion}\nWe now summarize our results. In this paper we have considered a two-dimensional damped harmonic oscillator in noncommutative space\nwith time dependent noncommutative parameters.\nWe map this system in terms of commutative variables by using a shift of variables connecting the noncommutative and commutative\nspace, known in the literature as Bopp-shift. We have then obtained the exact solution of this time dependent system by using the well known Lewis invariant which in turn leads to a non-linear differential equation known as the Ermakov-Pinney equation. We first obtain the Lewis invariant in Cartesian coordinates. \nWe then make a transformation to polar coordinates and write down our results in these coordinates. Doing \nso, we use the operator approach to obtain the eigenstates of the invariant. With this background in place, we make various choices of the parameters in the problem which in turn leads to solutions for the time dependent noncommutative parameters. We have considered three different sets of choices for which solutions have been obtained, namely, exponentially decaying solutions, rationally decaying solutions and elementary solutions. Interestingly, the solutions obtained make it possible to integrate the phase factor exactly thereby giving an exact solution for the eigenstates of the Hamiltonian. We have then computed the matrix elements of operators raised to a finite integer power in both the eigenstates of the Hamiltonian as well as the Lewis invariant. From these results, we are able to compute the expectation value of the Hamiltonian. Expectedly, the expectation value of the energy varies with time. For the exponentially decaying solutions, we get three kinds of behaviour corresponding to the choices of the damping factor and the frequency of the oscillator. For the case where the damping factor is set to unity and the frequency of the oscillator decays with time, the expectation value of the energy first decreases with time and then increases.\nThe reason for this behaviour is due to the particular form of the solutions of the Ermakov-Pinney equation which fixes the forms of the noncommutative parameters. It is these time dependent forms of the noncommutative parameters that results in the above mentioned behaviour of the expectation value of the energy with time. \nIn this case, we also observe that there is an upper bound of time above which the energy expectation value ceases to be real. \nFor the case where the damping factor has a decaying part and the frequency of the oscillator is a constant, we observe that the expectation value of the energy remarkably remains constant with time. This must be the case because the effect of the exponentially decaying coefficient in the Hamiltonian \nand the damping term gets balanced out by the exponentially increasing coefficient in the Hamiltonian. For the case where both the damping term as well as the frequency of the oscillator decays with time, we find an exponentially decaying behaviour of the expectation value of the energy. For the rationally decaying and the elementary solution, we observe a power law decay of the energy expectation value with time together with an upper bound of time above which the energy expectation value ceases to be real. Investigating these cases of damped oscillators, we conclude that the behaviour corresponding to the exponentially decaying solution, where both the frequency and damping term are decaying exponentially with time, is similar to a damped oscillator in commutative space. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\section*{Acknowledgement}\nMD would like to thank Ms. Riddhi Chatterjee and Ms.Rituparna Mandal for their helpful assistance to operate the software Mathematica.\n\n\n\n\n\n\n\n\n\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\n The Bloch--Kato conjecture, which relates the dimension of the Selmer group of a $p$-adic geometric Galois representation to the order of vanishing of its $L$-function, is one of the most important open problems in number theory. In a recent paper \\cite{LZ20}, we proved this conjecture for the 4-dimensional Galois representations arising from automorphic representations of $\\GSp_4$, under various technical hypotheses, using the ``method of Euler systems''; this relied crucially on the construction of an Euler system for $\\GSp_4$ in our earlier work \\cite{LSZ17} with Skinner, and the construction of a $p$-adic spin $L$-function for $\\GSp_4$ in the paper \\cite{LPSZ1} with Pilloni and Skinner.\n\n In this paper, we prove new cases of the Bloch--Kato conjecture, for the 8-dimensional tensor product Galois representation $V(\\pi) \\otimes V(\\sigma)$ associated to a $\\GSp_4$ automorphic representation $\\pi$ and a $\\GL_2$ automorphic representation $\\sigma$. The principal ingredients are the the Euler system for these Galois representations constructed in \\cite{HJS20}, and the formula proved in \\cite{LZ20b-regulator} relating these Euler system classes to periods of $p$-adic modular forms for $\\GSp_4 \\times \\GL_2$ (obtained by integrating a class in $H^2$ arising from $\\pi$, restricted to $\\GL_2 \\times_{\\GL_1} \\GL_2 \\subset \\GSp_4$, against the product of a cusp form in $\\sigma$ and a non-classical $p$-adic Eisenstein series). The main result of this paper, \\cref{thm:BKconj}, proves the Bloch--Kato conjecture for a certain twist of $V(\\pi)^* \\otimes V(\\sigma)^*$, corresponding to a critical value of the $L$-function $L(\\pi \\times \\sigma, s)$.\n\n The main new technical input needed in order to prove this theorem is to interpolate the $p$-adic automorphic periods arising from \\cite{LZ20b-regulator} in $p$-adic families, with both of the weights $(r_1, r_2)$ of the $\\GSp_4$ automorphic representation allowed to vary. This is not accessible by the methods of our earlier work \\cite{LPSZ1}, since the version of higher Hida theory used in that paper (based on the earlier work \\cite{pilloni20}) is only applicable to 1-parameter families in which $r_1$ varies for a fixed $r_2$. A similar issue arises in our earlier work \\cite{LZ20}, but in that setting, we were able to bypass the problem by applying the functorial lift from $\\GSp_4$ to $\\GL_4$, and applying the results of \\cite{DJR18, barreradimitrovwilliams} on $p$-adic $L$-functions for $\\GL_{2n}$. However, this does not work for $\\GSp_4 \\times \\GL_2$, since there appears to be no known construction of $p$-adic $L$-functions for $\\GL_4 \\times \\GL_2$.\n\n We therefore develop a direct approach to interpolating these $p$-adic periods in 2-parameter families for $\\GSp_4$, with both $r_1$ and $r_2$ varying, using the new ``higher Coleman theory'' introduced in \\cite{boxerpilloni20}. Our main result in this direction is \\cref{thm:bspairing}, whose proof occupies the majority of the present paper. This result shows that there is a well-defined pairing between the higher Coleman theory spaces for $\\GSp_4$ and spaces of overconvergent modular forms for $\\GL_2 \\times \\GL_2$; and in the final sections of the paper, we use this to define $p$-adic $L$-functions in families for $\\GSp_4 \\times \\GL_2$ by pairing a family of $H^2$ eigenclasses for $\\GSp_4$ with the product of a $\\GL_2$ cusp-form family and an auxiliary Eisenstein series. The existence of this $p$-adic $L$-function then allows us to prove a reciprocity law relating the Euler system of \\cite{HJS20} to critical complex $L$-values, and thus to prove the Bloch--Kato conjecture.\n\n These new methods can also be used to strengthen the results of \\cite{LZ20} for the degree 4 motive of $\\GSp_4$; for reasons of space, we shall pursue this in a forthcoming paper. Our methods also give, as a by-product, the construction of a ``$p$-adic Eichler--Shimura isomorphism in families'' for $\\GSp_4$, interpolating the comparison isomorphisms between de Rham and \\'etale cohomology for all (or almost all) specialisations of a $\\GSp_4$ Hida family. Our results give an interpolation of the comparison isomorphism after projecting to a specific filtration step of de Rham cohomology, corresponding to $H^2$ of automorphic vector bundles. Our results are thus complementary to the recent work of Diao et al \\cite{diao-rosso-wu21} which interpolates the filtration step corresponding to $H^0$.\n\n\\begin{remark}\n The switch from ``Hida'' to ``Coleman'' theory allows us to define $p$-adic $L$-functions for finite-slope families, rather than just for ordinary (i.e.~slope 0) families. However, this comes at a price: the use of Coleman theory requires an overconvergence condition on the Eisenstein series, which does not hold for the 2-parameter family of Eisenstein series used in \\cite{LPSZ1}. So the price we pay for including the second weight variable $r_2$ is that we lose sight of the cyclotomic variable -- for each automorphic representation $\\pi \\times \\sigma$ of $\\GSp_4 \\times \\GL_2$, there is an interval of integers $n$ such that $V(\\pi)^* \\otimes V(\\sigma)^*(-n)$ is critical, but in the present paper we can only prove the Bloch--Kato conjecture for a specific $n$, corresponding to the lower endpoint of this interval. Even in the ordinary case, to prove the Bloch--Kato conjecture for all of the critical twists, we would need a version of higher Hida (rather than Coleman) theory for $\\GSp_4$ with both $r_1$ and $r_2$ varying. Such a theory is not available at present, although analogous results for Hilbert modular groups have been announced by Giada Grossi \\cite{grossi21}.\n\\end{remark}\n\n\\emph{Acknowledgements.} We would like to thank George Boxer and Vincent Pilloni for answering our questions about their beautiful theory. We are very grateful for their patience.\n\n\n\n\n\\section{Preliminaries}\n\n Throughout this paper $p$ is a prime.\n\n \\subsection{The group $G$}\n\n Let $G=\\GSp(4)$, with respect to the anti-diagonal Hermitian form with matrix $J = \\begin{smatrix} & &&1\\\\&&1\\\\&-1\\\\-1\\end{smatrix}$. Write $B_G$ for the Borel subgroup consisting of upper-triangular matrices, and write $P_{\\Kl}$ and $P_{\\Sieg}$ for the Klingen and Siegel parabolic subgroups containing $B_G$. We then have the Levi decompositions\n \\[ B_G = T N_{B}, \\qquad P_{\\Sieg}= M_{\\Sieg} N_{\\Sieg},\\qquad P_{\\Kl}= M_{\\Kl} N_{\\Kl},\\]\n where $T$ is the diagonal torus.\n\n The Siegel parabolic $P_{\\Sieg}$ and its Levi $M_{\\Sieg}$ plays a distinguished role in our constructions, since it is conjugate to the centraliser of the cocharacter defining the Shimura datum; the Klingen parabolic is less important here (in contrast with our previous paper \\cite{LPSZ1}). Hence we shall often write simply $P_G$, $M_G$ for $P_{\\Sieg}, M_{\\Sieg}$. We identify $M_{G}$ with $\\GL_2 \\times \\GL_1$ via $\\stbt{A}{0}{0}{\\star} \\mapsto (A, \\nu)$, where $\\nu$ is the symplectic multiplier.\n\n Let $W_G=N_G(T)\/T$ denote the Weyl group of $(G,T)$. The group $W_G$ is generated by the $T$-cosets of the elements\n $s_1=\\begin{smatrix} 1\\\\ & & 1 \\\\ & -1 \\\\ &&&1 \\end{smatrix}$\n and\n $s_2=\\begin{smatrix} & 1\\\\ -1 \\\\ &&& -1 \\\\ &&1\\end{smatrix}$.\n \n\n Let $W_{M_G} = \\langle s_2 \\rangle $ denote the Weyl group of $(M_G, T)$, and let ${}^{M} W_G=W_{M_G}\\backslash W_G$. This has a distinguished set of coset representatives (the \\emph{Kostant representatives}) given by\n \\[ {}^MW_G=\\{ \\mathrm{id}, w_1, w_2, w_3 \\}\\]\n where $w_1 = s_1$, $w_2 = s_1 s_2$, $w_3 = s_1 s_2 s_1$. These have lengths $\\ell(w_i) = i$. We use $w_G^{\\max}$ for the long Weyl element of $G$, and $w_{M_G}^{\\max} = s_2$ the long Weyl element of $M_G$.\n\n \\begin{remark}\n Note that:\n \\begin{enumerate}[(i)]\n \\item Since $W_G$ permutes the coordinates of the diagonal torus, we can realize it as subgroup of $S_4$. Then ${}^{M}W_G$ identifies with the permutations $w \\in W_G$ such that $w(1) m} \\mathcal{B}_{m'}, \\qquad \\overline{\\mathcal{B}}^\\circ_m = \\{ |.| : |z| < |p|^m\\}.\n \\]\n Thus $\\mathcal{B}^{\\circ}_m \\subset \\overline{\\mathcal{B}}^\\circ_{m} \\subset \\mathcal{B}_m \\subset\\overline{\\mathcal{B}}_m$, and (as the notation suggests) $\\overline{\\mathcal{B}}_m$ is the closure of $\\mathcal{B}_m$, and similarly $\\overline{\\mathcal{B}}_m^\\circ$ of $\\mathcal{B}^{\\circ}_m$. Moreover, the sets $\\overline{\\mathcal{B}}_m - \\mathcal{B}_m$ and $\\overline{\\mathcal{B}}^\\circ_m -\\mathcal{B}^{\\circ}_m$ consist entirely of rank $> 1$ points.\n\n \\begin{remark}\n Compare the four flavours of root subgroups in \\bp{\\S 3.3.2}. The space $\\overline{\\mathcal{B}}_m$ corresponds to the ``dagger affinoid disc'' in Grosse-Kl\\\"onne's theory of dagger spaces.\n \\end{remark}\n\n More generally, if $A$ is a subset of $\\overline{\\mathbf{Q}}_p$, we write $A + \\mathcal{B}_m = \\bigcup_{a \\in A}(a + \\mathcal{B}_m)$ etc; we shall only use this if $A$ is compact, in which case the union is finite.\n\n \\subsection{Level groups at $p$}\n\n \\begin{notation} Let $t \\in \\mathbf{Z}_{\\ge 1}$.\n \\begin{itemize}\n \\item Let $K_{\\Iw}^G(p^t) = \\{ g \\in G(\\ZZ_p): g\\bmod p \\in B_G(\\mathbf{Z}\/p^t)\\}$ be the depth $t$ upper-triangular Iwahori of $G$, and similarly for $H$.\n \\item Let $K^H_{\\diamondsuit}(p^t)$ denote the group $H(\\QQ_p) \\cap \\hat\\gamma K^G_{\\Iw}(p^t) \\hat\\gamma^{-1}$, which is concretely given by\n \\[ K^H_{\\diamondsuit} =\n \\left \\{ h \\in H(\\ZZ_p): h = \\left(\\stbt{x}{y}{0}{z},\\stbt{x}{-y}{0}{z}\\right) \\bmod p^t \\text{ \\textup{for some} $x,y,z$}\\right\\}.\\]\n \\end{itemize}\n \\end{notation}\n\n Note that $K^H_{\\diamondsuit}(p^t)$ is, fortuitiously, a subgroup of $K^H_{\\Iw}(p^t)$.\n\n \\subsection{Tubes of ``radius one''}\n\n We note that if $\\mathcal{X}$ is the analytic adic space associated to a finite-type $\\ZZ_p$-scheme $X$, then there is a specialisation map $\\sp: \\mathcal{X} \\to X_{\\mathbf{F}_p}$ which is a continuous map of topological spaces. If $Z \\subset X_{\\mathbf{F}_p}$ is a locally closed subset, we let $]Z[$ be the \\emph{interior} of $\\sp^{-1}(Z)$; this is the adic space corresponding to the tube in the sense of classical rigid geometry, while $\\sp^{-1}(Z)$ is not a classical rigid space in general. Of course, if $Z$ is open, then $\\sp^{-1}(Z) =\\ ]Z[$; on the other hand, if $Z$ is closed, then $\\sp^{-1}(Z) =\\ \\overline{]Z[}$.\n\n \\begin{definition}\n Let $\\scalebox{1.15}{$\\mathtt{U}$}^G_0 =\\ ]Y^G_{w_1, \\mathbf{F}_p}[$, $\\scalebox{1.15}{$\\mathtt{Z}$}^G_0 = \\ \\overline{]X^G_{w_1, \\mathbf{F}_p}[}$, and $\\scalebox{1.15}{$\\mathtt{I}$}^G_{0,0} = \\scalebox{1.15}{$\\mathtt{Z}$}^G_0 \\cap \\scalebox{1.15}{$\\mathtt{U}$}^G_0$.\n \\end{definition}\n\n Note that $\\scalebox{1.15}{$\\mathtt{U}$}^G_0$ is open and $\\scalebox{1.15}{$\\mathtt{Z}$}^G_0$ closed, and both are invariant under the Iwahori $K^G_{\\Iw}(p)$ (since the Borel subgroup of $G_{\\mathbf{F}_p}$ fixes the mod $p$ Bruhat cells). Thus $\\scalebox{1.15}{$\\mathtt{I}$}^G_{0, 0}$ is a ``partial closure'' of the Bruhat cell $]C_{w_1, \\mathbf{F}_p}^G[$.\n\n We also write $\\scalebox{1.15}{$\\mathtt{Z}$}^H_0 = \\overline{]X^H_{\\mathrm{id}}[}$ (the preimage of the point $\\{\\mathrm{id}_H\\} \\in \\FL^H_{\\mathbf{F}_p}$) which is stable under $K^H_{\\Iw}(p)$, and we formally set $\\scalebox{1.15}{$\\mathtt{U}$}^H_0 = \\scalebox{1.15}{$\\mathtt{FL}$}^H$.\n\n \\begin{proposition}\n We have $\\scalebox{1.15}{$\\mathtt{I}$}^G_{0,0} \\subset U^G_{w_1}$, and in the coordinates on $U^G_{w_1}$ described in \\cref{sect:explicitparam}, we have\n \\[ \\scalebox{1.15}{$\\mathtt{I}$}^G_{0,0} = \\{ \\stbt x y z x : x, z \\in \\overline{\\mathcal{B}}_{0}^\\circ, y \\in \\mathcal{B}_0 \\}.\\]\n Similarly\n \\[ \\scalebox{1.15}{$\\mathtt{I}$}^H_{0,0} = \\scalebox{1.15}{$\\mathtt{Z}$}^H_0 = \\{ (z_1, z_2) : z_i \\in \\overline{\\mathcal{B}}_{0}^\\circ \\}.\\]\n \\end{proposition}\n\n \\begin{proof}\n This is an instance of \\bp{Lemma 3.21 (5)}.\n \\end{proof}\n\n \\begin{proposition}\n \\label{prop:Cartadic}\n We have a Cartesian diagram of adic spaces\n \\[\n \\begin{tikzcd}\n \\scalebox{1.15}{$\\mathtt{Z}$}^H_0 = \\scalebox{1.15}{$\\mathtt{I}$}^H_{0, 0} \\dar[\"\\hat\\iota\"] \\rar[hook] &\\scalebox{1.15}{$\\mathtt{U}$}^H_0 \\dar[\"\\hat\\iota\"]\\\\\n \\scalebox{1.15}{$\\mathtt{I}$}^G_{0,0} \\rar[hook] & \\scalebox{1.15}{$\\mathtt{U}$}^G_0\n \\end{tikzcd}\n \\]\n in which all the morphisms are closed embeddings.\n \\end{proposition}\n\n \\begin{proof}\n This follows readily from \\cref{prop:intersectcells} and the definition of the $\\scalebox{1.15}{$\\mathtt{U}$}$'s, $\\scalebox{1.15}{$\\mathtt{I}$}$'s and $\\scalebox{1.15}{$\\mathtt{Z}$}$'s.\n \\end{proof}\n\n \\subsection{Tubes of smaller radius}\n\n Let $m, n, t$ be integers with\n \\begin{equation}\\label{eq:mnt}\n 0 \\le n \\le m < t, \\qquad \\text{$m > n$ if $n \\ne 0$}.\n \\end{equation}\n\n \\begin{definition}\n We define subsets $\\scalebox{1.15}{$\\mathtt{I}$}_{m, n}^G \\subset \\scalebox{1.15}{$\\mathtt{U}$}_n^G$ in $\\scalebox{1.15}{$\\mathtt{FL}$}^G$ as follows: we let\n \\[ \\scalebox{1.15}{$\\mathtt{I}$}_{m, n}^G = \\{ \\stbt x y z x : x, z \\in \\overline{\\mathcal{B}}_{m}^\\circ, y \\in \\mathcal{B}_n + \\ZZ_p \\}. \\]\n (consistently with the $(m, n) = (0, 0)$ case described above). For $n \\ge 1$ we set\n \\[ \\scalebox{1.15}{$\\mathtt{U}$}_n^G =\\{ \\stbt x y z x : x, z \\in \\mathcal{B}_n^\\circ, y \\in \\mathcal{B}_n + \\ZZ_p \\}.\\]\n and for $n = 0$ we use the definition above.\n \\end{definition}\n\n \\begin{proposition} \\\n \\begin{enumerate}[(i)]\n \\item The sets $\\scalebox{1.15}{$\\mathtt{U}$}_n^G$ and $\\scalebox{1.15}{$\\mathtt{U}$}_n^G - \\scalebox{1.15}{$\\mathtt{I}$}_{m, n}^G$ are open in $\\scalebox{1.15}{$\\mathtt{FL}$}^G$.\n \\item If $n > 0$ then $\\scalebox{1.15}{$\\mathtt{I}$}_{m, n}^G = \\scalebox{1.15}{$\\mathtt{I}$}_{m, 0}^G \\cap \\scalebox{1.15}{$\\mathtt{U}$}_n^G$.\n \\item The sets $\\scalebox{1.15}{$\\mathtt{U}$}_n^G$ and $\\scalebox{1.15}{$\\mathtt{I}$}_{m, n}^G$ are stable under $K_{G, \\Iw}(p^t)$, and in the notation of \\bp{\\S 3.3.3}, we have\n \\[ \\scalebox{1.15}{$\\mathtt{U}$}_n^G =\\ ]C_{w_1, \\mathbf{F}_p}[_{(n, n)}K_{G, \\Iw}(p^t),\\qquad \\scalebox{1.15}{$\\mathtt{I}$}_{m, n} =\\ ]C_{w_1, \\mathbf{F}_p}[_{(\\overline{m}, n)}K_{\\Iw}^G(p^t).\\]\n \\end{enumerate}\n \\end{proposition}\n\n \\begin{proof}\n The first two statements are obvious. For the stability under $K_{G, \\Iw}(p^t)$, we treat $n = 0$ and $n > 0$ separately: in the $n = 0$ case, the stability of $\\scalebox{1.15}{$\\mathtt{U}$}_0^G$ is already established, and the stability of $\\scalebox{1.15}{$\\mathtt{I}$}_{m, n}$ follows from the identification $\\scalebox{1.15}{$\\mathtt{I}$}_{m, 0} = \\, ]C_{w_1, \\mathbf{F}_p}\\![\\,_{\\overline{m}, 0} = \\mathcal{P}\\backslash \\mathcal{P} w \\mathcal{G}_{\\overline{m}, 0}$ of \\bp{\\S 3.3.3}.\n\n For $n > 0$, we argue as in Lemma 3.18 of \\emph{op.cit.} to describe the spaces $]C_{w_1, \\mathbf{F}_p}[_{(n, n)} K_{\\Iw}^G(p^t)$ and $]C_{w_1, \\mathbf{F}_p}[_{(\\overline{m}, n)}K_{\\Iw}^G(p^t)$ as finite unions of translates of $]C_{w_1, \\mathbf{F}_p}[_{(n, n)}$, indexed by coset representatives for $N_{B_G}(\\mathbf{Z}\/p^n)$ modulo its intersection with $w^{-1}P_G w$. We can take these coset representatives to be of the form\n \\[ \\begin{smatrix} 1 \\\\ &1 & \\alpha \\\\ &&1 \\\\ &&&1\\end{smatrix}, \\qquad \\alpha \\in \\mathbf{Z} \/ p^n, \\]\n which act as $\\stbt x y z x \\mapsto \\stbt x {y + \\alpha} z x$.\n \\end{proof}\n\n \\begin{remark}\n One can choose a closed subset $\\scalebox{1.15}{$\\mathtt{Z}$}_m^G$ such that $\\scalebox{1.15}{$\\mathtt{I}$}_{m, n}^G = \\scalebox{1.15}{$\\mathtt{Z}$}_m^G \\cap \\scalebox{1.15}{$\\mathtt{U}$}_n^G$; however, it is a little awkward to choose such a subset which is invariant under $K^G_{\\Iw}(p^t)$, and in any case the choice of such a subset will not matter for our theory, so we shall not privilege any particular choice.\n \\end{remark}\n\n\n \\begin{remark}\n Formally setting $n = 0$ in the formula defining $\\scalebox{1.15}{$\\mathtt{U}$}_n^G$ for $n > 0$ gives a well-defined and $K^G_{\\Iw}(p)$-invariant set (in fact it is exactly $]C_{w_1}[\\,$), but this set does not contain $\\scalebox{1.15}{$\\mathtt{I}$}_{0, 0}$. Hence we use the formula of the previous section in which $x, z$ are allowed to ``go off to infinity''.\n \\end{remark}\n\n\n\n \\subsection{Iwahori-level tubes for $H$}\n\n For $n < t$ we shall define\n \\[ \\scalebox{1.15}{$\\mathtt{U}$}_n^H = \\hat\\iota^{-1}(\\scalebox{1.15}{$\\mathtt{U}$}_n^G) \\subseteq \\scalebox{1.15}{$\\mathtt{FL}$}^H.\\]\n This is an open $K_{H, \\Iw}(p^t)$-invariant set, containing $\\{\\mathrm{id}\\}$. For $n = 0$ it is the whole of $\\scalebox{1.15}{$\\mathtt{FL}$}^H$.\n\n \\begin{proposition}\n The set $\\hat\\iota^{-1}(\\scalebox{1.15}{$\\mathtt{I}$}_{m, n}^G)$ is closed in $\\scalebox{1.15}{$\\mathtt{FL}$}_H$ and invariant under $K_{H, \\Iw}(p^t)$, and does not depend on $n$; explicitly it is given by\n \\[ \\scalebox{1.15}{$\\mathtt{Z}$}_m^H = \\{ (z_1, z_2): z_i \\in \\overline{\\mathcal{B}}^\\circ_m\\}.\\]\n \\end{proposition}\n\n \\begin{proof}\n Clear from \\eqref{eq:gamma-param}.\n \\end{proof}\n\n We therefore have a Cartesian diagram of closed embeddings generalising \\cref{prop:Cartadic} above, for any $m, n, t$ as in \\eqref{eq:mnt}:\n \\begin{equation}\\label{eq:Cartadic2}\n \\begin{tikzcd}\n \\scalebox{1.15}{$\\mathtt{Z}$}^H_m \\dar[\"\\hat\\iota\"] \\rar[hook] &\\scalebox{1.15}{$\\mathtt{U}$}^H_n \\dar[\"\\hat\\iota\"]\\\\\n \\scalebox{1.15}{$\\mathtt{I}$}^G_{m,n} \\rar[hook] & \\scalebox{1.15}{$\\mathtt{U}$}^G_n.\n \\end{tikzcd}\n \\end{equation}\n\n It is convenient to extend the definition of $\\scalebox{1.15}{$\\mathtt{Z}$}_m$ to remove the requirement that $m < t$. For any integers $t \\ge 1$ and $m \\ge 0$, let us define\n \\[ \\scalebox{1.15}{$\\mathtt{Z}$}_m^H \\coloneqq \\{ (z_1, z_2): z_i \\in \\overline{\\mathcal{B}}^\\circ_m + p^t \\ZZ_p\\}.\\]\n This is invariant under $K_{H, \\Iw}(p^t)$, and hence also under $K_{H, \\diamondsuit}(p^t)$. If $m < t$ then this agrees with the definitions above. We define an open set $\\scalebox{1.15}{$\\mathtt{U}$}_n^H$ similarly. (However, in the $m \\ge t$ case we do not know if these sets $\\scalebox{1.15}{$\\mathtt{U}$}_n^H$ and $\\scalebox{1.15}{$\\mathtt{Z}$}_m^H$ can be fitted into a diagram like \\eqref{eq:Cartadic2}.)\n\n\n\n\n\\section{Pullbacks in overconvergent cohomology}\n \\subsection{Adic spaces and period maps}\n\n We consider the analytifications $\\mathcal{S}_{G,K}^{\\an}=(S_K\\times \\Spec(\\QQ_p))^{\\an}$, $\\mathcal{S}_{G,K}^{\\mathrm{tor}}=(S_{G,K}^{\\mathrm{tor}}\\times\\Spec(\\QQ_p))^{\\an}$ and $\\scalebox{1.15}{$\\mathtt{FL}$}_G=(\\FL_{G}\\times\\Spec(\\QQ_p))^{\\an}$, as well as the groups $\\mathcal{G}^{\\an}=(G\\times \\Spec(\\QQ_p))^{\\an}$, $\\mathcal{P}^{\\an}_{G}=(P_{G}\\times \\Spec(\\QQ_p))^{\\an}$ and $\\mathcal{M}_G^{\\an}=(M_G\\times \\Spec(\\QQ_p))^{\\an}$.\n\n Since we have fixed an integral model of $G$, we have quasi-compact, affinoid groups $\\mathcal{G}\\hookrightarrow \\mathcal{G}^{\\an}$, $\\mathcal{P}_{G,\\Sieg}\\hookrightarrow \\mathcal{P}_{G,\\Sieg}^{\\an}$ and $\\mathcal{M}_G\\hookrightarrow \\mathcal{M}_G^{\\an}$.\n\n Write $\\mathcal{S}_{G,K^p}^{\\mathrm{tor}}$ for the perfectoid space $\\varprojlim_{K_p} \\mathcal{S}_{G,K^pK_p}^{\\mathrm{tor}}$. We can then consider the Hodge--Tate period map\n \\[ \\pi_{\\HT,G}^{\\mathrm{tor}}: \\mathcal{S}_{G,K^p}^{\\mathrm{tor}} \\longrightarrow \\scalebox{1.15}{$\\mathtt{FL}$}_G\\]\n which for every open compact $K_p\\subset G(\\QQ_p)$ descends to a map of topological spaces (c.f. \\cite[\\S 4.5]{boxerpilloni20})\n \\[ \\pi_{\\HT,G,K_p}^{\\mathrm{tor}}: \\mathcal{S}_{G,K^pK_p}^{\\mathrm{tor}}\\longrightarrow \\scalebox{1.15}{$\\mathtt{FL}$}_G\/K_p.\\]\n\n There is an analogous Hodge--Tate period map for $H$ also. To lighten the notation, we shall frequently omit many of the subscripts from $\\pi_{\\HT,G,K_p}^{\\mathrm{tor}}$ when they are clear from context (in particular, we shall almost always omit the ``$\\mathrm{tor}$'', since the non-compactified Shimura variety plays no role here).\n\n We shall attempt to consistently maintain the convention that subsets of flag varieties are denoted by typewriter letters $\\scalebox{1.15}{$\\mathtt{U}$}$ etc, and the preimages of these spaces under the Hodge--Tate period maps are denoted by calligraphic letters $\\mathcal{U}$ etc.\n\n\n \\subsection{Period maps}\n\n \\begin{theorem}\n \\label{thm:HTmaps}\n There is a commutative diagram of Hodge--Tate period maps (where we have omitted some unimportant subscripts and superscripts for clarity)\n \\[\n \\begin{tikzcd}\n \\mathcal{S}^{\\mathrm{tor}}_{H,\\Iw}(p^t) \\rar[\"\\pi^H_{\\Iw}\"] & \\scalebox{1.15}{$\\mathtt{FL}$}_H \/ K_{H, \\Iw}(p^t)\\\\\n \\mathcal{S}^{\\mathrm{tor}}_{H,\\diamondsuit}(p^t) \\rar[\"\\pi^H_{\\diamondsuit}\"] \\dar[\"\\hat\\iota\" left] \\uar[\"\\pr_\\diamondsuit\" left] & \\scalebox{1.15}{$\\mathtt{FL}$}_H \/ K_{H, \\diamondsuit}(p^t) \\dar[\"\\hat\\iota\" right] \\uar[\"\\pr_{\\diamondsuit}\" right]\\\\\n \\mathcal{S}^{\\mathrm{tor}}_{G,\\Iw}(p^t) \\rar[\"\\pi^G_{\\Iw}\"] & \\scalebox{1.15}{$\\mathtt{FL}$}_G \/ K_{G, \\Iw}(p^t)\n \\end{tikzcd}\n \\]\n in which the maps $\\pr_{\\diamondsuit}$ are the natural quotients, and the downward ones are given by the composite of the natural embedding $H \\hookrightarrow G$ and right-translation by $\\hat\\gamma = \\gamma w_1$.\n \\end{theorem}\n\n \\begin{proof}\n It suffices to check that the Hodge--Tate period maps for $H$ and for $G$ at perfectoid infinite level are compatible; but this is a direct consequence of the construction, since the Hodge--Tate period map for Hodge-type Shimura varieties is defined using an embedding into a Siegel Shimura variety. See \\cite[\\S 4.4.7]{boxerpilloni20}).\n \\end{proof}\n\n For $(m, n, t)$ as in \\eqref{eq:mnt}, we define subspaces\n \\[ \\mathcal{I}^G_{m, n} \\subset \\mathcal{U}^G_n \\subset \\mathcal{S}_{G,\\Iw}(p^t), \\qquad \\mathcal{Z}^H_{m} \\subset \\mathcal{U}^H_n \\subset \\mathcal{S}_{H,\\diamondsuit}(p^t)\n \\]\n as the preimages of the subsets $\\scalebox{1.15}{$\\mathtt{I}$}^G_{m, n} \\subset \\scalebox{1.15}{$\\mathtt{U}$}^G_n \\subset \\scalebox{1.15}{$\\mathtt{FL}$}^G$ under $\\pi^G_{\\Iw}$, respectively $\\mathcal{Z}^H_{m} \\subset \\mathcal{U}^H_n \\subset \\scalebox{1.15}{$\\mathtt{FL}$}^H$ under $\\pi^H_{\\diamondsuit}$. Combining \\cref{thm:HTmaps} and \\cref{eq:Cartadic2}, we obtain a Cartesian diagram\n \\begin{equation}\\label{eq:adicSh}\n \\begin{tikzcd}\n \\mathcal{Z}^{H}_m \\dar \\rar[hook] &\\mathcal{U}^H_n \\dar\\\\\n \\mathcal{I}^G_{m, n} \\rar[hook] & \\mathcal{U}^G_n\n \\end{tikzcd}\n \\end{equation}\n in which the horizontal arrows are closed embeddings; and $\\mathcal{Z}^{H}_m$ is closed in $\\mathcal{S}_{H,\\diamondsuit}(p^t)$.\n\n\n \\subsection{Overconvergent pullback}\n We can now define the pullback map on overconvergent cohomology. We give the definitions for the non-cuspidal cohomology, using the coefficient sheaf $\\mathcal{V}= \\mathcal{V}_\\kappa$ for some $M_G$-dominant integral weight $\\kappa$; the definitions are the same for cuspidal cohomology using $\\mathcal{V}= \\mathcal{V}_\\kappa(-D)$ instead.\n\n Using the diagram \\eqref{eq:adicSh} and the functoriality of cohomology with support, we get a map\n \\begin{equation}\n \\label{eq:iota1}\n \\hat\\iota^* : R\\Gamma_{\\mathcal{I}^G_{mn}}(\\mathcal{U}_n^G, \\mathcal{V}) \\longrightarrow R\\Gamma_{\\mathcal{Z}_m^{H}}\\left(\\mathcal{U}_n^H, \\hat\\iota^* \\mathcal{V}_{\\kappa}\\right) \\cong R\\Gamma_{\\mathcal{Z}_m^{H}}\\left(\\mathcal{S}_{H,\\diamondsuit}(p^t), \\hat\\iota^* \\mathcal{V}\\right),\n \\end{equation}\n where the final isomorphism comes from excision, using the fact that $\\mathcal{Z}_m^{H}$ is closed in $\\mathcal{S}_{H,\\diamondsuit}(p^t)$.\n\n As in \\bp{\\S 5.4.1}, for any $t \\ge 1$, we can define the finite-slope overconvergent cohomology for $G$ as\n \\begin{align*}\n R\\Gamma^G_{w_1}(\\kappa)^{-, \\fs} &\\coloneqq R\\Gamma_{\\mathcal{I}_{00}}(\\mathcal{U}^G_0, \\mathcal{V}_\\kappa)^{-, \\fs}.\n \\end{align*}\n So \\eqref{eq:iota1} for $(m, n) = (0,0)$ gives our first definition of the pullback map on overconvergent cohomology, taking values in $R\\Gamma_{\\mathcal{Z}_0^{H}}\\left(\\mathcal{S}_{H,\\diamondsuit}(p^t), \\hat\\iota^* \\mathcal{V}\\right)$.\n\n \\begin{proposition}[Comparison with classical pullback]\n \\label{prop:classicalcomp}\n We have the following commutative diagram:\n \\[\n \\begin{tikzcd}\n R\\Gamma_{\\mathcal{I}_{00}^G}(\\mathcal{U}_0^G,\\mathcal{V}) \\rar[\"\\quad \\hat\\iota^*\\quad\" above, \"\\eqref{eq:iota1}\" below] \\dar[\"\\cores\"] & R\\Gamma_{\\mathcal{Z}_0^{H}}\\left(\\mathcal{S}^{\\mathrm{tor}}_{H, \\diamondsuit}(p^t), \\hat\\iota^* \\mathcal{V}\\right) \\dar[\"\\cores\"]\\\\\n \n R\\Gamma(\\mathcal{U}_0^G,\\mathcal{V}) \\rar{\\quad \\hat\\iota^*\\quad } & R\\Gamma\\left(\\mathcal{S}^{\\mathrm{tor}}_{H,\\diamondsuit}(p^t),\\hat\\iota^*\\mathcal{V}\\right)\\\\\n \n R\\Gamma\\left(\\mathcal{S}^{\\mathrm{tor}}_{G,\\Iw}(p^t),\\mathcal{V}\\right)\\uar[\"\\mathrm{res}\"] \\arrow[\"\\hat\\iota^*\" below]{ru}\n \\end{tikzcd}\n \\]\n in which the bottom horizontal map corresponds to the classical pushforward via the rigid-analytic GAGA theorem. Moreover, the spaces in the left column of the diagram have actions of the prime-to-$p$ Hecke algebra and the operators $\\mathcal{U}'_{\\Sieg}$, $\\mathcal{U}'_{\\Kl}$ at $p$, and the maps $\\mathrm{res}$ and $\\cores$ are compatible with these actions.\n \\end{proposition}\n\n \\begin{proof}\n The only non-obvious step of the diagram is the existence of the middle horizontal map, which follows from \\cref{prop:Cartadic}. The compatibility with Hecke actions is an easy check, cf.~\\bp{Lemma 5.17}.\n \\end{proof}\n\n \\begin{proposition}[Change of support condition]\n \\label{prop:changesupport}\n The maps \\eqref{eq:iota1} for $(m, n)$, $(m, 0)$, and $(0, 0)$ fit into a diagram\n \\[\n \\begin{tikzcd}\n R\\Gamma_{\\mathcal{I}_{mn}^G}(\\mathcal{U}_n^G,\\mathcal{V}) \\arrow{r}{\\hat\\iota^*} &\n R\\Gamma_{\\mathcal{Z}_m^H}\\left(\\mathcal{S}^{\\mathrm{tor}}_{H, \\diamondsuit}(p^t), \\hat\\iota^*(\\mathcal{V})\\right)\\\\\n R\\Gamma_{\\mathcal{I}_{m0}^G}(\\mathcal{U}_0^G,\\mathcal{V}) \\arrow{u}{\\mathrm{res}} \\arrow{r}{\\hat\\iota^*} \\dar[\"\\cores\"]& R\\Gamma_{\\mathcal{Z}_m^H}\\left(\\mathcal{S}^{\\mathrm{tor}}_{H, \\diamondsuit}(p^t), \\hat\\iota^*(\\mathcal{V})\\right) \\uar[equals] \\arrow{d}{\\cores} \\\\\n R\\Gamma_{\\mathcal{I}_{00}^G}(\\mathcal{U}_0^G,\\mathcal{V}) \\arrow{r}{\\hat\\iota^*} &\n R\\Gamma_{\\mathcal{Z}_0^H}\\left(\\mathcal{S}^{\\mathrm{tor}}_{H, \\diamondsuit}(p^t), \\hat\\iota^*(\\mathcal{V})\\right).\n \\end{tikzcd}\n \\]\n The complexes in the left column have compatible actions of the Hecke operators away from $p$, and of $\\mathcal{U}'_{\\Kl}$ and $\\mathcal{U}'_{\\Sieg}$ at $p$, and the maps $\\mathrm{res}$ and $\\cores$ are compatible with these.\n \\end{proposition}\n\n \\begin{proof}\n Immediate from the fact that $\\mathcal{I}^G_{mn} = \\mathcal{I}_{m0}^G \\cap \\mathcal{U}_n^G$ and standard functoriality properties of cohomology with support.\n \\end{proof}\n\n \\begin{proposition}\n The spaces in the left columns of the diagrams in \\cref{prop:classicalcomp,prop:changesupport} all have actions of the prime-to-$p$ Hecke operators, and of the Hecke operators $\\mathcal{U}'_{\\Sieg}$, $\\mathcal{U}'_{\\Kl}$, $\\mathcal{U}'_B$ at $p$. Moreover, the maps in the left column of \\cref{prop:changesupport} become isomorphisms on the finite-slope part for $\\mathcal{U}'_B$.\n \\end{proposition}\n\n \\begin{proof}\n The compatibility with Hecke operators away from $p$ is clear, since the Hodge--Tate period map is invariant under the action of the prime-to-$p$ Hecke algebra. The fact that the maps in the left column of \\cref{prop:classicalcomp} are maps of Hecke modules is an instance of \\bp{Lemma 5.17}.\n\n The assertions regarding the finite-slope part follow from \\bp{Theorem 5.66}, since one can check that $(\\mathcal{I}_{m, n}, \\mathcal{U}_n)$ defines an ``allowed support condition'' in the sense of \\bp{\\S 5.4.3}.\n \\end{proof}\n\n This shows that we have well-defined maps\n \\begin{equation}\n \\label{eq:iota2}\n R\\Gamma^G_{w_1}(\\kappa)^{-, \\fs} \\longrightarrow R\\Gamma_{\\mathcal{Z}_m^H}\\left(\\mathcal{S}^{\\mathrm{tor}}_{H, \\diamondsuit}(p^t), \\hat\\iota^*(\\mathcal{V})\\right)\n \\end{equation}\n for any $0 \\le m < t$, compatible under corestriction, extending \\eqref{eq:iota1} for in the $m = 0$ case.\n\n\n\n\n \\subsection{Functoriality of coefficients}\n \\label{sect:branchcoeffs}\n \\begin{proposition}\n Let $\\kappa_1 = (r_1, -r_2-2; r_1 + r_2)$ with $r_1 \\ge r_2 \\ge -1$, and let $\\tau = (t_1, t_2; r_1 + r_2)$ where $t_i \\ge -1$ and $t_1 + t_2 = r_1 - r_2 - 2$. Then there is a nonzero homomorphism of $(\\gamma^{-1} M_H \\gamma)$-representations\n \\[ V^G_{\\kappa_1} |_{\\gamma^{-1} M_H\\gamma } \\to V^H_{\\tau}, \\]\n uniquely determined up to scaling.\n \\end{proposition}\n\n It will be helpful to fix a normalisation for this map, by choosing a vector $f \\in (V^G_{\\kappa_1})^\\vee$ which transforms by $\\tau^{-1}$ under $\\gamma^{-1} M_H \\gamma$. We have an explicit presentation of $(V^G_{\\kappa_1})^\\vee = V^G_{(-w_{0, M} \\kappa_1)}$ as the space of polynomial functions $f \\in \\mathcal{O}(M_G)$ which satisfy $f(mb) = \\kappa_1(b) f(m)$ for all $b \\in B_{M_G}$ and $m \\in M_G$, with $M_G$ acting by left-translation. Since $\\gamma^{-1} M_H \\gamma \\cdot B_{M_G}$ is open in $M_G$, we can choose a unique $f$ which satisfies $f(\\mathrm{id}) = 1$ and transforms via $\\tau^{-1}$ under the action of $M_H$.\n\n This map gives a homomorphism of sheaves on $\\mathcal{S}^{\\mathrm{tor}}_{H, \\diamondsuit}(p^t)$,\n \\[ \\hat\\iota^*\\left(\\mathcal{V}_\\kappa^G\\right) \\longrightarrow \\mathcal{V}_\\tau^H, \\]\n and combining this with \\cref{eq:iota2}, we obtain maps of complexes\n \\[ R\\Gamma^G_{w_1}(\\kappa_1)^{-, \\fs} \\longrightarrow R\\Gamma_{\\mathcal{Z}_m^H}\\left(\\mathcal{S}^{\\mathrm{tor}}_{H, \\diamondsuit}(p^t), \\mathcal{V}^H_\\tau\\right)\n \\]\n for any $\\tau$ in the appropriate range, and similarly for cuspidal cohomology.\n\n \\begin{remark}\n The map is formally well-defined for a rather wider range of values of the parameters; but we have restricted to the case when $r_1 \\ge r_2 \\ge -1$ and $t_1, t_1 \\ge -1$, in order that there are interesting cuspidal automorphic representations contributing to $H^2$ for both $\\mathcal{V}_{\\kappa_1}^G$ and $\\mathcal{V}^H_{\\tau}$.\n\n If we set $k_i = r_i + 3$ and $c_i = t_i + 2$, then $(k_1, k_2, c_1, c_2)$ will define a point lying on the top edge of the region labelled $(f)$ in \\cite[Diagram 2]{LZvista}. Unfortunately, it seems to be difficult to extend our present analysis to points in the interior of this region; this would require some sort of ``nearly version'' of higher Coleman theory, analogous to the theory of nearly-overconvergent families in $H^0$ of modular curves recently introduced by Andreatta--Iovita \\cite{andreattaiovita21}. See \\cite[\\S 6]{LPSZ1} for an analogous theory in the ordinary case (with $r_2$ fixed, rather than varying as here).\n \\end{remark}\n\n\n \\subsection{Change of level}\n \\label{sect:changelevel}\n Finally, we note that for any $t \\ge 1$, we have $[K_{H, \\diamondsuit}(p^t): K_{H, \\diamondsuit}(p^{t+1})] = p^4 = [K_{G, \\Iw}(p^t) : K_{G, \\Iw}(p^{t+1})]$, and hence the natural map\n \\[ \\mathcal{S}_{H, \\diamondsuit}(p^{t+1}) \\longrightarrow \\mathcal{S}_{H, \\diamondsuit}(p^{t}) \\times_{\\mathcal{S}_{G, \\Iw}(p^{t})} \\mathcal{S}_{G, \\Iw}(p^{t+1})\n \\]\n is an isomorphism. So the pushforward (trace) maps arising from changing $t$ on the two spaces are compatible with the pullback $\\hat\\iota^*$, and similarly for the cohomology with supports, for any support condition invariant under $K_{G, \\Iw}(p^t)$.\n\n Hence, if we temporarily write $\\mathcal{I}_{mn}^G(p^t)$ etc to distinguish our various locally closed subspaces of Shimura varieties at the different levels, then we have trace maps\n \\[ R\\Gamma_{\\mathcal{I}^G_{mn}(p^{t+1})}(\\mathcal{U}^G_n(p^{t+1}), \\mathcal{V}^G_\\kappa) \\longrightarrow R\\Gamma_{\\mathcal{I}^G_{mn}(p^t)}(\\mathcal{U}^G_n(p^t), \\mathcal{V}^G_\\kappa) \\]\n and\n \\[ R\\Gamma_{\\mathcal{Z}^H_{m}(p^{t+1})}(\\mathcal{S}_{H, \\diamondsuit}(p^{t+1}), \\mathcal{V}^H_\\tau) \\longrightarrow R\\Gamma_{\\mathcal{Z}^H_{m}(p^t)}(\\mathcal{S}_{H, \\diamondsuit}(p^{t}), \\mathcal{V}^H_\\tau).\\]\n and these are compatible with the pullback maps $\\hat\\iota^*$, and the restriction\/corestriction maps for varying $m, n$. Moreover, the trace maps for $G$ are isomorphisms on the finite-slope part by \\bp{Theorem 5.14}.\n\n We can thus define a map\n \\[\n R\\Gamma_{w_1}(\\kappa)^{-, \\fs} \\longrightarrow R\\Gamma_{\\mathcal{Z}^H_m(p^t)}(\\mathcal{S}_{H, \\diamondsuit}(p^t), \\mathcal{V}^H_\\tau).\n \\]\n for any $t \\ge 1$, $m \\ge 0$ (not necessarily with $t > m$) by composing with the trace map from level $t'$ for some auxiliary $t' > m$; this allows us to define $\\hat\\iota^*$ as a map\n \\[\n R\\Gamma^G_{w_1}(\\kappa)^{-, \\fs} \\to \\varprojlim_m R\\Gamma_{\\mathcal{Z}^H_m(p^t)}(\\mathcal{S}_{H, \\diamondsuit}(p^t), \\mathcal{V}^H_\\tau).\n \\]\n Since the spaces $\\mathcal{Z}_m^H$ are actually invariant under the Iwahori of $H$, we can trace down further to land in the space\n \\[ \\varprojlim_m R\\Gamma_{\\mathcal{Z}^H_m(p^t)}(\\mathcal{S}_{H, \\Iw}(p^t), \\mathcal{V}^H_\\tau),\\]\n where we have abused notation a little by using $\\mathcal{Z}^H_m(p^t)$ for the preimages of $\\scalebox{1.15}{$\\mathtt{Z}$}_m^H$ at either Iwahori or $\\diamondsuit$ level.\n \\begin{definition}\n We define\n \\[ R\\Gamma_{\\mathrm{id}}\\left(\\mathcal{S}_{H, \\Iw}(p^t), \\tau\\right)^{(-, \\dag)} = \\varprojlim_m R\\Gamma_{\\mathcal{Z}^H_{m}(p^t)}\\left( \\mathcal{S}_{H, \\Iw}(p^t), \\mathcal{V}^H_\\tau\\right) .\\]\n \\end{definition}\n\n This space can be interpreted as the compactly-supported cohomology of the intersection $\\bigcap_m \\mathcal{Z}^H_m(p^t) = \\pi_{H}^{-1}\\left( \\{\\mathrm{id}_H\\} \\right)$; we shall recall this in a little more detail in the next section, where we shall allow more general coefficients.\n\n\n\n\\section{Torsors}\n\n We now begin constructing the ``locally analytic'' version of the pullback map on higher Coleman theory.\n \\subsection{Torsors on flag varieties}\n\n The map $x \\mapsto x^{-1}: G\\rightarrow \\FL_G$ (recall that $\\FL_G=P_G\\backslash G$) allows us to regard $G$ as a right $P_G$-torsor over $\\FL_G$, and similarly to regard $G\/ N_G\\rightarrow \\FL_G$ as a right $M_G$-torsor. We consider their analytifications\n \\[ \\scalebox{1.15}{$\\mathtt{P}$}^G: \\mathcal{G}\\rightarrow \\scalebox{1.15}{$\\mathtt{FL}$}_G \\qquad \\text{and} \\qquad \\scalebox{1.15}{$\\mathtt{M}$}^G:\\mathcal{G}\/ \\mathcal{N}_G\\rightarrow \\scalebox{1.15}{$\\mathtt{FL}$}_G.\\]\n which are torsors over $\\scalebox{1.15}{$\\mathtt{FL}$}_G$ under the (affinoid) analytic groups $\\mathcal{P}_G$ and $\\mathcal{M}_G$ respectively. We similarly define torsors over the flag varieties of $H$ and $H_i$ for $i=1,2$.\n\n \\begin{definition} Define $\\mathcal{P}^G_{\\HT}$ and $\\mathcal{M}^G_{\\HT}$ to be the pullbacks via $\\pi^G_{\\HT}$ of the torsors $\\scalebox{1.15}{$\\mathtt{P}$}^G$ and $\\scalebox{1.15}{$\\mathtt{M}$}^G$; these are (right) torsors over $\\mathcal{S}_{G,\\Iw}(p^t)$ for the groups $\\mathcal{P}_G$ and $\\mathcal{M}_G$. We similarly define $\\mathcal{P}^H_{\\HT}$ and $\\mathcal{M}^H_{\\HT}$, $\\mathcal{P}^{H_i}_{\\HT}$ and $\\mathcal{M}^{H_i}_{\\HT}$ for $i=1,2$.\n \\end{definition}\n\n \\begin{note}\n It is easy to check that $\\mathcal{M}^H_{\\HT}=\\mathcal{M}^{H_1}_{\\HT}\\times_{\\GL_1}\\mathcal{M}^{H_2}_{\\HT}$, where we take the fibre product with respect to the action of $\\nu$ in the parametrisation of $T$.\n \\end{note}\n\n\n \\subsection{Reduction of structure}\n\n \\begin{definition}\n For $n > 0$, let $\\mathcal{M}^1_{G,n} \\triangleleft \\mathcal{M}_G$ be the (affinoid analytic) group of elements which reduce to the identity $\\pmod{p^n}$. Define\n \\[ \\mathcal{M}^\\square_{G,n} = \\mathcal{M}^1_{G,n} \\cdot B_{M_G}(\\ZZ_p), \\]\n which is an affinoid analytic subgroup containing $\\Iw_{M_G}(p^n)$. A similar definition applies to $M_H = T$; we write the group as $\\mathcal{T}^{\\square}_{n} = T(\\ZZ_p) \\mathcal{T}^1_{n}$.\n \\end{definition}\n\n \\begin{note}\n We follow \\cite{boxerpilloni20} here in using affinoid subgroups and affinoid subspaces of flag varieties to develop the locally-analytic theory, rather than the ``mixed'' spaces (products of some copies of $\\mathcal{B}_n$ and some of $\\mathcal{B}^\\circ_n$) used in the previous sections.\n \\end{note}\n\n \\begin{note}\\label{note:identifyMGsq}\n Identifying $M_G$ with $\\GL_2 \\times \\GL_1$ as in the introduction, we have\n \\[\\mathcal{M}^\\square_{G, n} =\n \\left\\{\n (\\stbt x y z w, \\lambda) :\n \\begin{array}{c}\n x,w,\\lambda \\in \\ZZ_p^\\times \\cdot (1 + \\mathcal{B}_n),\\\\ z \\in \\mathcal{B}_n, y \\in \\ZZ_p + \\mathcal{B}_n.\n \\end{array}\n \\right\\}\n \\qedhere\n \\]\n \n \\end{note}\n\n \\begin{notation}\n Define\n \\[ \\mathcal{T}^\\diamondsuit_n = \\{\\diag(t_1, t_2, \\nu t_2^{-1}, \\nu t_1^{-1}) \\in \\mathcal{T}^\\square_n: t_1-t_2 \\in \\mathcal{B}_n\\}. \\]\n \\end{notation}\n\n Thus $\\mathcal{T}^\\diamondsuit_n$ and $\\mathcal{T}^\\square_n$ are both disjoint unions of copies of $\\mathcal{T}^1_n$, but $\\mathcal{T}^\\diamondsuit_n$ has fewer of these components than $\\mathcal{T}^\\square_n$.\n\n \\begin{proposition} \\label{prop:redofstr} Let $t > n > 0$.\n \\begin{enumerate}\n \\item Over $\\mathcal{U}^G_n$, the torsor $\\mathcal{M}_{\\HT}^G$ has a reduction of structure to an \\'etale torsor $\\mathcal{M}^G_{\\HT,n}$ under the group $\\mathcal{M}^\\square_{G,n}$.\n\n \\item Over $\\mathcal{U}^H_{\\Iw,n}$, the torsor $\\mathcal{M}_{\\HT}^H$ has a reduction of structure to an \\'etale torsor $\\mathcal{M}^H_{\\HT,n,\\Iw}$ under the group $\\mathcal{T}^{\\square}_{n}$.\n\n \\item Over $\\mathcal{U}^H_n$, the torsor $\\mathcal{M}_{\\HT}^H$ has a reduction of structure to an \\'etale torsor $\\mathcal{M}^H_{\\HT,n,\\diamondsuit}$ under the group $\\mathcal{T}^\\diamondsuit_{n}$ (and this refines the pullback of $\\mathcal{M}^H_{\\HT,n,\\Iw}$ to level $K^H_{\\diamondsuit}(p^t)$).\n\n \n \\end{enumerate}\n \\end{proposition}\n\n \\begin{proof}\n Part (1) is essentially the result of \\bp{\\S 6.2.1}. The proofs of (2) and (3) are similar.\n \\end{proof}\n\n \\begin{lemma}\n We have the following inclusions of subgroups.\n \\begin{itemize}\n \\item As subgroups of $\\mathcal{M}_G$, we have\n \\[ \\mathcal{T}^\\diamondsuit_{n} = \\mathcal{T} \\cap \\gamma \\mathcal{M}^\\square_{G,n}\\gamma^{-1}. \\]\n \\item As subgroups of $\\mathcal{G}$,\n \\[ \\hat\\gamma^{-1} \\cdot K^H_{\\diamondsuit}(p^n)\\mathcal{H}^1_{n} \\cdot \\hat\\gamma \\subset K^G_{\\Iw}(p^n)\\mathcal{G}^1_{n}, \\]\n where $\\hat\\gamma = \\gamma w_1$ as usual.\n \\end{itemize}\n \\end{lemma}\n\n \\begin{proof}\n \n If $\\tau = (\\stbt{t_1}{}{}{t_2}, \\nu)$ is an element of $\\mathcal{T}$ then $\\gamma^{-1} \\tau \\gamma = ( \\stbt{t_1}{}{t_2-t_1}{t_2}, \\nu)$. It is now clear that $\\gamma^{-1}T_{\\diamondsuit}(p^t) \\gamma \\subset \\Iw_{M_G}(p^t)$ and $\\gamma^{-1}\\mathcal{T}^1_{n} \\gamma \\subset \\mathcal{M}^1_{G,n}$, so the required inclusion follows.\n\n The second statement can be verified similarly; the inclusion on $\\ZZ_p$-points is the definition of $K^H_{\\diamondsuit}$, and the inclusion on $\\mathcal{H}^1_{n}$ follows from the fact that $\\mathcal{H}^1_{n} \\subset \\mathcal{G}^1_n$ and $\\mathcal{G}^1_n$ is normal in $\\mathcal{G}$.\n \\end{proof}\n\n\n \\begin{proposition}\\label{prop:pullbackcomp}\n We have an equality of $\\mathcal{M}^\\square_{G, n}$-torsors over $\\mathcal{U}^H_{n,\\diamondsuit}$:\n \\[ \\hat\\iota^*\\left( \\mathcal{M}^G_{\\HT,n,\\Iw}\\right) = \\mathcal{M}^H_{\\HT,n,\\diamondsuit}\\times^{\\left[\\mathcal{T}^\\diamondsuit_{n}, \\gamma\\right]} \\mathcal{M}^\\square_{G,n}, \\]\n where we regard $\\mathcal{T}^{\\diamondsuit}_{n}$ as a subgroup of $\\Iw_{M_G}(p^t)\\mathcal{M}^1_{G,n}$ via conjugation by $\\gamma$.\n \\end{proposition}\n\n \\begin{proof}\n We check the analogous statement on the flag varities. We first observe that we have a commutative diagram of adic spaces\n \\[\n \\begin{tikzcd}\n K^H_{\\diamondsuit}(p^t)\\mathcal{H}^1_{n} \\dar\\rar & K^G_{\\Iw}(p^t)\\mathcal{G}^1_{n}\\dar\\\\\n \\mathcal{B}^H\\backslash \\mathcal{B}^H K^H_{\\diamondsuit}(p^t)\\mathcal{H}^1_{n}\\rar[hook, \"\\hat\\iota\"]\n & \\mathcal{P}^G\\backslash \\mathcal{P}^G w_1K^G_{\\Iw}(p^t)\\mathcal{G}^1_{n}.\n \\end{tikzcd}\n \\]\n Here, the vertical maps are given by $h\\mapsto \\mathcal{B}^H\\backslash \\mathcal{B}^H h^{-1}$ on the left, and $g\\mapsto \\mathcal{P}^G\\backslash \\mathcal{P}^G w_1g^{-1}$ on the right; the lower horizontal map $\\hat\\iota$ is $\\mathcal{B}^H h\\mapsto \\mathcal{P}^G h \\gamma w_1$, and the map along the top making the diagram commute is $h \\mapsto \\hat\\gamma^{-1} h\\hat\\gamma$, which is well-defined by the preceding lemma. (Note that the commutativity of the diagram relies on the fact that $\\gamma \\in P_G$.)\n\n The right-translation action of $\\mathcal{B}^H$ on $\\mathcal{G}$ makes the left-hand column into a torsor for the group $\\mathcal{B}^H \\cap K^H_{\\diamondsuit}(p^t)\\mathcal{H}^1_{n}$. Similarly, via right-translation conjugated by $w_1$, $K^G_{\\Iw}(p^t)\\mathcal{G}^1_{n}$ becomes a torsor for the group\n \\( \\mathcal{P}\\cap w \\mathcal{G}^1_n K^G_{\\Iw}(p^t)w^{-1}; \\)\n and these structures are compatible if we consider $\\mathcal{B}^H \\cap K^H_{\\diamondsuit}(p^t)\\mathcal{H}^1_{n}$ as a subgroup of $\\mathcal{P} \\cap w \\mathcal{G}^1_n K^G_{\\Iw}(p^t)w^{-1} $ via conjugation by $\\gamma$.\n\n Passing to the $\\mathcal{N}_H$-coinvariants on the left, we obtain a torsor for $T_\\diamondsuit(p^t) \\mathcal{T}^1_{n} = \\mathcal{T}^\\diamondsuit_n$; and passing to $\\mathcal{N}_G$-coinvariants on the right, we obtain a torsor for the projection of $\\mathcal{P} \\cap w \\mathcal{G}^1_n K^G_{\\Iw}(p^t)w^{-1}$ to the Levi $\\mathcal{M}_G$, which is the group $\\mathcal{M}^\\square_{G, n}$. Moreover, these structures are compatible via the $\\gamma$-conjugation inclusion $\\mathcal{T}^\\diamondsuit_n \\hookrightarrow \\mathcal{M}^\\square_{G,n}$ established in the above lemma.\n\n We now note that $\\scalebox{1.15}{$\\mathtt{U}$}^G_n \\subset \\scalebox{1.15}{$\\mathtt{FL}$}^G$ is contained in the subset $\\mathcal{P}^G w_1K^G_{\\Iw}(p^t)\\mathcal{G}^1_{n}$, since $\\scalebox{1.15}{$\\mathtt{U}$}^G_n$ is the orbit of $w_1$ under $K^G_{\\Iw}(p^t) \\mathcal{G}^1_{n, n}$ in the notation of \\bp{\\S 3.3.3}, and $\\mathcal{G}^1_{n, n} \\subset \\mathcal{G}^1_n$. So pulling back to $\\mathcal{U}^G_n$ via the Hodge--Tate period map gives the result.\n \\end{proof}\n\n\\section{Spaces of distributions and branching laws}\n\n \\subsection{Analytic characters}\n\n \\begin{definition}\n Let $n \\in \\mathbf{Q}_{> 0}$. We say a continuous character $\\kappa: \\ZZ_p^\\times \\to A^\\times$, for $(A, A^+)$ a complete Tate algebra, is \\textbf{$n$-analytic} if it extends to an analytic $A$-valued function on the affinoid adic space\n \\[ \\ZZ_p^\\times \\cdot \\mathcal{B}_n \\subset \\mathbf{G}_m^{\\mathrm{ad}}.\\]\n This definition extends naturally to characters $T(\\ZZ_p) \\to A^\\times$: the $n$-analytic characters are exactly those which extend to $\\mathcal{T}^\\square_n$.\n \\end{definition}\n\n \\begin{remark}\n For compatibility with our notations for algebraic weights, we shall denote a $p$-adic character $\\kappa$ of $T(\\ZZ_p)$ by a triple $(\\rho_1, \\rho_2; \\omega)$ of characters of $\\ZZ_p^\\times$, via\n \\[ \\kappa(\\diag(st_1, st_2, st_2^{-1}, st_1^{-1})) = \\rho_1(t_1) \\rho_2(t_2) \\omega(s), \\]\n so that formally\n \\[ \\kappa(\\diag(t_1, t_2, \\nu t_2^{-1}, \\nu t_1^{-1})) = \\rho_1(t_1) \\rho_2(t_2) \\left(\\frac{\\omega}{\\rho_1\\rho_2}\\right)^{\\tfrac{1}{2}}(\\nu).\\]\n This is of course not well-defined as written, since $p$-adic characters do not have a unique square root, so we should understand the triple $(\\rho_1, \\rho_2; \\omega)$ as coming with an implicit choice of square root of $\\omega \/\\rho_1 \\rho_2$ which is being suppressed from the notation.\n \\end{remark}\n\n \\subsection{Analytic inductions}\n\n We recall some definitions from \\bp{\\S 6.1.2}. Let $(A,A^+)$ be a complete Tate algebra over $(\\QQ_p,\\ZZ_p)$. Let $n_0 > 0$, and assume that $\\kappa_A: T(\\ZZ_p)\\rightarrow A^\\times$ is an $n_0$-analytic character. For $?\\in \\{G,H\\}$ and $n \\geq n_0$, let $\\mathcal{M}^1_{?,n}$ be the affinoid subgroup of $\\mathcal{M}_?$ defined above, and let $B_{M_G}$ be the Borel of $M_?$.\n\n \\begin{definition}\n For $n\\geq n_0$, define\n \\begin{align*}\n V^{n-\\an}_{G,\\kappa_A}=&\\, \\an\\Ind^{\\left(\\mathcal{M}^\\square_n\\right) }_{\\left(\\mathcal{M}^\\square_n \\cap \\mathcal{B}_{G}\\right)}(w_{0,M_?}\\kappa_A)\\\\\n =&\\, \\Big\\{ f \\in \\mathcal{O}(\\mathcal{M}^\\square_{G, n}) \\mathop{\\hat\\otimes} A : f(mb)=(w_{0,M}\\kappa_A)(b^{-1})f(m),\\\\\n &\\quad \\forall \\, m\\in \\mathcal{M}^\\square_{G, n},\\, \\forall b\\in \\mathcal{M}^\\square_{G, n} \\cap \\mathcal{B}_G \\Big\\}.\n \\end{align*}\n We define a left action of $\\mathcal{M}^\\square_{G, n}$ on $V^{n-\\an}_{G,\\kappa_A}$ by $(h \\cdot f)(m) = f(h^{-1} m)$.\n\n Write $D^{n-\\an}_{G,\\kappa_A}$ for the dual space, and $\\langle -, - \\rangle$ for the pairing between these; we equip $D^{n-\\an}_{G,\\kappa_A}$ with a left action of the same group $\\mathcal{M}^\\square_{G, n}$, in such a way that $\\langle h\\mu , hf \\rangle = \\langle \\mu ,f \\rangle$.\n \\end{definition}\n\n Let us describe $V_{G,\\kappa_A}^{n-\\an}$ explicitly. We use the description of $\\mathcal{M}_{G, n}^{\\square}$ given in \\cref{note:identifyMGsq}. Since $\\mathcal{M}_{G, n}^{\\square}$ has an Iwahori decomposition, restriction to elements of the form $(\\stbt{1}{}{\\star}{1}, 1)$ identifies $V_{G,\\kappa_A}^{n-\\mathrm{an}}$ with the space of analytic functions of $z \\in \\mathcal{B}_n$; this space is independent of $\\kappa_A$, but the action of $\\mathcal{M}^{\\square}_{G,n}$ does depend on $\\kappa_A$, as follows.\n\n \\begin{propqed}\n Suppose $\\kappa_A$ is the character $(\\rho_1, \\rho_2; \\omega)$. Then the action of $(\\stbt a b c d, \\nu)$ on $f \\in \\mathcal{O}(\\mathcal{B}_n) \\mathop{\\hat\\otimes} A$ is given by\n \\[ \\left((\\stbt a b c d, \\nu) f\\right)(z) = f\\left( \\frac{az-c}{-bz + d}\\right) (-bz + d)^{\\rho_1 - \\rho_2} (ad-bc)^{\\rho_2} \\nu^{(\\omega-\\rho_1-\\rho_2)\/2}.\\qedhere\\]\n \\end{propqed}\n\n \\begin{note}\n For $H$ in place of $G$, we can make the same definitions; but the resulting spaces are much simpler, since $\\mathcal{M}_H = \\mathcal{T}$ is commutative and contained in $\\mathcal{B}_H$. Hence any function $f \\in V^{n-\\an}_{H,\\kappa_A}$ is uniquely determined by its value at 1. So $V^{n-\\an}_{H,\\kappa_A}$ is canonically $A$, with $\\mathcal{T}^\\square_n$ acting via $\\kappa_A$; and dually $D^{n-\\an}_{H,\\kappa_A}$ is $A$ with $\\mathcal{T}^\\square_n$ acting via $\\kappa_A^{-1}$.\n \\end{note}\n\n\n \\begin{note}\n If $\\kappa_A$ is an algebraic character $(k_1, k_2; c)$, then $V_{G,\\kappa_A}^{n-\\an}$ naturally contains the algebraic $M_H$-representation of highest weight $\\kappa_A$ (identified with polynomials in $z$ of degree $\\le k_1 - k_2$); and dually, $D^{n-\\an}_{?,\\kappa_A}$ surjects onto the algebraic representation of highest weight $\\kappa_A^\\vee$ (the dual of the weight $\\kappa_A$ representation).\n \\end{note}\n\n\n\n \\subsection{Branching laws in families}\\label{ss:kraken}\n\n \\begin{definition}\n Let $A$ be a Tate algebra endowed with an $n_0$-analytic character $\\kappa_A: T(\\ZZ_p) \\to A^\\times$ as above, and additionally with a character $\\lambda: (1 + \\mathcal{B}_n)^\\times \\to A^\\times$. Define the \\emph{kraken} to be the function\n \\[ \\mathscr{K}^{\\lambda}(z)=\\lambda(1+z),\\]\n viewed as an element of $V_{G,\\kappa_{A}}^{n-\\an}$.\n \\end{definition}\n\n \\begin{lemma}\n The function $\\mathscr{K}^{\\lambda}$ is an eigenvector for $\\gamma^{-1} \\mathcal{T}^\\diamondsuit_n \\gamma \\subset \\mathcal{M}^\\square_{G, n}$, with eigencharacter $w_{0, M} \\kappa_A + (\\lambda, -\\lambda; 0)$.\n \\end{lemma}\n\n \\begin{proof}\n We have $\\gamma^{-1}\\left( \\stbt x {} {} y, \\nu\\right) \\gamma=(\\stbt x {} {-x+y} y, \\nu)$.\n If this condition is satisfied, then (writing $\\kappa = (\\rho_1, \\rho_2; \\omega)$ as before) we have\n \\[ (\\stbt x {} {-x+y} y, \\nu) \\mathscr{K}^{\\lambda}(z)= x^{\\rho_2}y^{\\rho_1}\\nu^{(\\omega-\\rho_1-\\rho_2)\/2} \\mathscr{K}^{\\lambda}\\left( \\frac{x}{y}(z+1) - 1 \\right)=x^{\\rho_2+\\lambda}y^{\\rho_1-\\lambda} \\nu^{(\\omega-\\rho_1-\\rho_2)\/2}\\mathscr{K}^{\\lambda}(z).\\qedhere \\]\n \\end{proof}\n\n As an immediate consquence, we obtain the following result:\n\n \\begin{propqed}\\label{prop:krakenpower}\n Pairing with the element $\\mathscr{K}^{\\lambda}$ defines a homomorphism of $\\mathcal{T}^\\diamondsuit_n$-representations\n \\[ \\hat\\iota^*( D^{n-\\an}_{G,\\kappa_A}) \\longrightarrow D^{n-\\an}_{H,w_{0, M} \\kappa_A + (\\lambda, -\\lambda;0)}.\\qedhere\\]\n \\end{propqed}\n\n \\begin{note}\n Note that $D^{n-\\an}_{H,w_{0, M} \\kappa_A + (\\lambda, -\\lambda;0)}$ is one-dimensional (and independent of $n$).\n \\end{note}\n\n We now consider a special case. Let $A = \\QQ_p$ and take $\\kappa_A$ to be the algebraic weight $(r_2 + 2, -r_1; -r_1-r_2)$, for some integers $r_1 \\ge r_2\\ge 0$, so that $\\kappa_A^\\vee$ is the weight $\\kappa_1$ of \\eqref{eq:ourweights}. If we choose $\\lambda$ to be an integer in the range $[0, r_1 + r_2 + 2]$, then $\\mathscr{K}^\\lambda$ lies in the polynomial subspace $V_{G, \\kappa_A} \\subset V_{G, \\kappa_A}^{n-\\an}$. Its value at the identity element of $\\mathcal{M}_{G, n}^{\\square}$ is 1, by definition.\n\n So, if $t_i \\ge -1$ are integers with $t_1 + t_2 = r_1 - r_2 - 2$, and we we take $\\lambda$ such that $(r_1 - \\lambda, \\lambda - 2 - r_2) = (t_1, t_2)$, then we obtain a commutative diagram of $\\mathcal{T}^\\diamondsuit_n$-representations\n \\[\\begin{tikzcd}\n \\iota^*(D^{n-\\an}_{G,\\kappa_A}) \\rar \\dar &D^{n-\\an}_{H,-\\tau_A} \\dar[\"\\cong\"] \\\\\n \\iota^*\\left(V_{G,\\kappa_A^\\vee}\\right) \\rar &V_{H, \\tau_A}\n \\end{tikzcd}\\]\n where $\\tau_A^\\vee = (t_1, t_2; r_1 + r_2)$. Hence the homomorphism of Proposition \\ref{prop:krakenpower} is compatible with the classical branching law described in \\cref{sect:branchcoeffs}\n\n\n\\section{Sheaves of distributions}\n\n We use the above function spaces and morphisms as ``models'' for sheaves on the Shimura variety.\n\n \\subsection{Labelling of weights}\n \\label{ss:analyticweights}\n\n We recall some definitions from \\bp{\\S 6.2} (this theory is a bit messy owing to the need to reconcile various different conventions).\n\n As above, we let $(A,A^+)$ be a Tate algebra over $(\\QQ_p,\\ZZ_p)$. Given a weight $\\nu_A : T(\\ZZ_p) \\to A^\\times$ for some coefficient ring $A$, following \\bp{\\S 6}, we define $\\kappa_A: T(\\ZZ_p) \\to A^\\times$ by\n \\[ \\kappa_A = -w_{0, M} w_1 (\\nu + \\rho) - \\rho. \\]\n Explicitly, if $\\nu_A$ is $(\\nu_1, \\nu_2; \\omega)$ for some $\\nu_i, \\omega: \\ZZ_p^\\times \\to A^\\times$, then\n \\[ \\kappa_A = (\\nu_2 - 1, -3-\\nu_1; -\\omega).\\]\n\n We are not so much interested in the linear dual $\\kappa_A^\\vee$ as the ``Serre dual'' $\\kappa_A' = (\\kappa_A + 2\\rho_{nc})^\\vee$. Explicitly this is $(\\nu_1, -2-\\nu_2; c) = w_1(\\nu_A+ \\rho) - \\rho$. So when $A = \\QQ_p$ and $\\nu = (r_1, r_2; r_1 + r_2)$ is an integral algebraic weight, we have $\\kappa_A' = \\kappa_1$ in the notation of \\eqref{eq:ourweights}.\n\n\n \\subsection{Sheaves on $G$}\n\n Let $1 \\le n < t$ be integers.\n\n \\begin{definition}\n We now define two sheaves $\\mathcal{V}^{n-\\an}_{G,\\nu_A}$ and $\\mathcal{D}^{n-\\an}_{G,\\nu_A}$ over $\\mathcal{U}^G_n$. The former can be defined as a subsheaf of $\\pi_* (\\mathcal{M}^G_{\\HT, n,\\Iw})$ transforming like functions in $V^{n-\\an}_{\\kappa_A}$; an alternative, possibly cleaner description is as a coproduct\n \\[ \\mathcal{V}^{n-\\an}_{G, \\nu_A} = \\mathcal{M}^G_{\\HT, n,\\Iw} \\times^{\\mathcal{M}^{\\square}_{G, n}} V^{n-\\an}_{G, \\kappa_A},\\]\n and similarly\n \\begin{equation}\\label{eq:Dsheafascofibreprod}\n \\mathcal{D}^{n-\\an}_{G, \\nu_A} = \\mathcal{M}^G_{\\HT, n,\\Iw} \\times^{\\mathcal{M}^{\\square}_{G, n}} D^{n-\\an}_{G, (\\kappa_A + 2\\rho_{nc})}.\n \\end{equation}\n \\end{definition}\n\n (The shift by $2 \\rho_{nc}$ is present so that the pairing between $\\mathcal{D}^{n-\\an}_{G, \\nu_A}$ and $\\mathcal{V}^{n-\\an}_{G, \\nu_A}$ lands in the dualizing sheaf of $\\mathcal{S}_G$, rather than in the structure sheaf.)\n\n \\begin{lemma}\\label{lem:sheafspec}\n The sheaves $\\mathcal{V}^{n-\\an}_{G,\\nu_A}$ and $\\mathcal{D}^{n-\\an}_{G,\\nu_A}$ are sheaves of $A$-modules, whose formation is compatible with base-change in $A$; and if $A = \\QQ_p$ and $\\nu_A = (r_1, r_2; c)$ for integers $r_1 \\ge r_2 \\ge -1$, we have classical comparison maps\n \\[ \\mathcal{V}_{G, \\kappa_A} \\hookrightarrow \\mathcal{V}^{n-\\an}_{G, \\nu_A},\n \\qquad\n \\mathcal{D}^{n-\\an}_{G, \\nu_A} \\twoheadrightarrow \\mathcal{V}_{G,(\\kappa_A + 2\\rho_{nc})^\\vee} = \\mathcal{V}_{G, \\kappa_1}. \\]\n \\end{lemma}\n\n \\begin{proof}\n See \\bp{Prop. 6.18}.\n \\end{proof}\n\n\n \\subsection{Sheaves on $H$}\n\n There are analogous constructions for sheaves for $H$. Here we use the element $\\mathrm{id} \\in {}^M W_H$ in place of $w_1$, and $w_{0, M_H}$ is the identity. So given an $n$-analytic character $\\tau_A$, we define $\\kappa_A^H = -\\tau_A-2\\rho_H$; and we set\n \\[ \\mathcal{V}^{n-\\an}_{H, \\diamondsuit, \\tau_A} = \\mathcal{M}^H_{\\HT, n,\\diamondsuit} \\times^{\\mathcal{T}^{\\diamondsuit}_n} V^{n-\\an}_{H, \\kappa_A^H},\\]\n and\n \\[ \\mathcal{D}^{n-\\an}_{H, \\diamondsuit, \\tau_A} = \\mathcal{M}^G_{\\HT, n, \\diamondsuit} \\times^{\\mathcal{T}^{\\diamondsuit}_{n}} D^{n-\\an}_{H,(\\kappa_A^H + 2\\rho_H)}.\\]\n\n Thus $\\mathcal{D}^{n-\\an}_{H,\\diamondsuit,\\tau_A}$ for a $\\QQ_p$-valued algebraic character $\\tau_A$ is simply (the restriction to $\\mathcal{U}_n^H$ of) the line bundle $\\mathcal{V}^H_{\\tau_A}$. The same definitions make sense at Iwahori level, of course, giving line bundles $\\mathcal{D}^{n-\\an}_{H,\\Iw,\\tau_A}$ and $\\mathcal{V}^{n-\\an}_{H,\\Iw,\\tau_A}$. These sheaves are in fact independent of $n$ (in the sense that $\\mathcal{D}^{(n+1)-\\an}_{H,\\Iw,\\tau_A}$ is isomorphic to the restriction of $\\mathcal{D}^{n-\\an}_{H,\\Iw,\\tau_A}$ to $\\mathcal{U}_{n+1}^H$), so we shall frequently drop the $n$ and write simply $\\mathcal{D}^{\\an}_{H,\\Iw,\\tau_A}$ etc.\n\n \\begin{remark}\\label{remark:bigsheafsmallt}\n Note that (for simplicity) we have only attempted to define the locally-analytic sheaves for $G$ when the level group at $p$ is $\\Iw(p^t)$ with $t > n$; thus our functions are defined on $\\mathcal{B}_n$ itself, rather than on a union of translates of $\\mathcal{B}_n$. (This restriction on the levels is inherited from \\bp{\\S 6.3}.)\n\n However, for $H$ the technical difficulties disappear, and we can make sense of $\\mathcal{V}^{\\an}_{H,\\Iw,\\tau_A}$ and $\\mathcal{D}^{\\an}_{H,\\Iw,\\tau_A}$ as vector bundles on $\\mathcal{U}_{n, \\Iw}^H(p^t)$ for any $n, t \\ge 1$.\n \\end{remark}\n\n\n \\subsection{Branching for sheaves}\n\n \\begin{definition}\n \\label{def:compat}\n We say the $A$-valued, $n$-analytic characters $\\nu_A$ and $\\tau_A$ of $T(\\ZZ_p)$ are \\emph{compatible} if $\\nu_A = (\\nu_1, \\nu_2; \\nu_1 + \\nu_2)$, $\\tau_A = (\\tau_1, \\tau_2; \\nu_1 + \\nu_2)$, for some characters $\\nu_i, \\tau_i$ of $\\ZZ_p^\\times$, and we have the relation\n \\[\n \\tau_1 + \\tau_2 = \\nu_1 - \\nu_2 - 2.\n \\]\n \\end{definition}\n\n\n\n Recall the kraken $\\mathscr{K}^\\lambda$ defined in \\cref{ss:kraken}. If $\\nu_A, \\tau_A$ are compatible, then taking $\\lambda = \\nu_1 - \\tau_1 = \\nu_2 + \\tau_2 + 2$, we obtain a homomorphism of $\\mathcal{T}^\\diamondsuit_n$-representations\n \\[ D^{n-\\an}_{G,(\\kappa_A + 2\\rho_{nc})}\n \\longrightarrow\n \n D^{n-\\an}_{H,-\\tau_A}\\]\n where $\\mathcal{T}^{\\diamondsuit}_n$ acts on $D^{n-\\an}_{G,(\\kappa_A + 2\\rho_{nc})}$ via $\\gamma$-conjugation. So the following result is an immediate consequence of \\cref{prop:krakenpower} and the results of \\cref{ss:analyticweights}:\n\n \\begin{propqed}\\label{prop:krakenonsheaves}\n Pairing with $\\mathscr{K}^\\lambda$ induces a morphism of sheaves over $\\mathcal{U}^H_{n}$:\n \\[ \\hat\\iota^*( \\mathcal{D}^{n-\\an}_{G,\\nu_A})\\longrightarrow \\mathcal{D}^{\\an}_{H, \\diamondsuit,\\tau_A}.\\]\n This morphism is compatible with specialisation in $A$, and if $A = \\QQ_p$ and $\\nu = (r_1, r_2; r_1+r_2)$, $\\tau = (t_1, t_2; r_1 + r_2)$ are algebraic weights with $r_1 - r_2 \\ge 0$ and $r_i, t_i \\ge -1$, then this morphism is compatible with the map of finite-dimensional sheaves $\\hat\\iota^*\\left(\\mathcal{V}_{\\kappa_1}\\right) \\to \\mathcal{V}^H_{\\tau}$ defined in \\S\\ref{sect:branchcoeffs}.\n \\end{propqed}\n\n\n\n \\subsection{Locally analytic overconvergent cohomology}\n\n Let $m,n,t$ be as in \\cref{eq:mnt}, with $n > 0$; and suppose $\\nu_A$ is an $n$-analytic $A$-valued character of $T(\\ZZ_p)$. We define cuspidal, locally analytic, overconvergent cohomology to be\n \\begin{equation}\\label{eq:cusplocanaoc}\n R\\Gamma^G_{w, \\an}(\\nu_A, \\cusp)^{-, \\fs} = R\\Gamma_{\\mathcal{I}_{mn}^G}\\left(\\mathcal{U}^G_n, \\mathcal{D}^{n-\\an}_{G,\\nu_A}(-D_G)\\right)^{-, \\fs},\n \\end{equation}\n and similarly for the non-cuspidal version. As shown in \\bp{\\S 6}, this complex is independent of $m$, $n$ and $t$, and is concentrated in degrees $[0, 1, 2]$.\n\n\n \\begin{proposition}\n \\label{prop:bigsheafpullback}\n Given $\\nu_A$ and $\\tau_A$ satisfying the compatibility condition of Definition \\ref{def:compat}, we have a morphism of complexes of $A$-modules\n \\[\n \\hat\\iota^*: R\\Gamma^G_{w, \\an}(\\nu_A, \\cusp)^{-, \\fs}\n \\to R\\Gamma_{\\mathcal{Z}^H_m}\\left(\\mathcal{U}_n^H, \\mathcal{D}^{n-\\an}_{H,\\diamondsuit,\\tau_A}(-D_H)\\right).\n \\]\n \\end{proposition}\n\n \\begin{proof}\n Immediate from \\cref{prop:krakenonsheaves}.\n \\end{proof}\n\n We have only defined this morphism for $m, n$ small relative to $t$. However, using \\cref{remark:bigsheafsmallt}, we can argue as in \\cref{sect:changelevel} and define\n \\[ R\\Gamma_{\\mathrm{id}, \\an}(\\mathcal{S}_{H, \\Iw}(p^t), \\tau_A, \\cusp)^{-, \\dag} = \\varprojlim_m R\\Gamma_{\\mathcal{Z}^H_{m, \\Iw}(p^t)}\\left( \\mathcal{U}^H_{n, \\Iw}, \\mathcal{D}^{\\an}_{H,\\Iw,\\tau_A}(-D_H)\\right).\\]\n Then we obtain a natural map\n \\[R\\Gamma^G_{w, \\an}(\\nu_A, \\cusp)^{-, \\fs} \\to R\\Gamma_{\\mathrm{id}, \\an}(\\mathcal{S}_{H, \\Iw}(p^t), \\tau_A, \\cusp)^{-, \\dag}.\\]\n\n \\begin{note}\n By construction, this morphism is compatible with derived base-change in $A$. If $A = \\QQ_p$, and $\\nu_A$ and $\\tau_A$ are algebraic weights such that $r_1 \\ge r_2 \\ge -1$ and $t_1, t_2 \\ge -1$, then this map fits into a commutative diagram with the pullback map on overconvergent cohomology defined in \\S\\ref{sect:changelevel}.\n \\end{note}\n\n \\subsection{Pairings and duality}\n\n Dually to the above, we define\n \\[\n R\\Gamma_{\\mathrm{id}, \\an}(\\mathcal{S}_{H, \\Iw}(p^t), \\tau_A)^{+, \\dag} = \\varinjlim_m R\\Gamma\\left(\\mathcal{Z}^H_{m, \\Iw}(p^t), \\mathcal{V}^{\\an}_{H,\\Iw, \\tau_A}\\right).\n \\]\n\n Note that if $\\tau_A = (t_1, t_2; c)$ then $\\mathcal{V}^{\\an}_{H,\\Iw, \\tau_A}$ is the sheaf $\\mathcal{V}^H_{-\\tau-2\\rho_H} = \\mathcal{V}_{(-2-t_1, -2-t_2; -c)}$, which is the sheaf of modular forms of weight $(t_1 + 2, t_2 + 2)$ (with the normalisation of the central character depending on $c$).\n\n \\begin{proposition}\n The above complex is concentrated in degree 0 and independent of $t$. It can be identified with the space of $p$-adic overconvergent modular forms for $H$ of tame level $K^{H, p}$ and weight $\\tau_A + (2, 2)$.\n \\end{proposition}\n\n \\begin{proof}\n For simplicity we suppose $K^{H, p}$ is the principal congruence subgroup of level $N$ for some $N$ (the general case reduces easily to this). Then the Shimura variety for $H$ is simply the fibre product (over $\\mu_N$) of two copies of the level $N$ modular curve parametrising elliptic curves with full level $N$ structure and a cyclic subgroup of order $p^t$. Then $\\pi_{HT}^{-1}(\\{\\mathrm{id}_H\\})$ is the ``canonical locus'', where the $p$-subgroups are both multiplicative; and the $\\mathcal{Z}_{m, \\Iw}^H$ are a cofinal family of neighbourhoods of this locus. Via the theory of the canonical subgroup, this space is independent of the choice of levels.\n\n Since the canonical locus is affinoid (and sufficiently small strict neighbourhoods of it also have this property), its cohomology vanishes above degree 0, and the degree 0 cohomology identifies with overconvergent sections of $\\mathcal{V}^{\\an}_{H, \\Iw, \\tau_A}$. If we choose an extension $\\tilde\\tau_A$ of $\\tau_A$ to the maximal torus of $\\GL_2 \\times \\GL_2$, then $\\mathcal{V}^{\\an}_{H, \\Iw, \\tau_A}$ decomposes as the product of two copies of the corresponding sheaves on the individual modular curves. This is precisely the construction of overconvergent modular forms described in \\cite{pilloni13} (see the discussion following Prop 6.2 of \\emph{op.cit.} for a comparison with Coleman's original approach).\n \\end{proof}\n\n \\begin{theorem}[c.f. \\bp{Theorem 6.38}]\n \\label{thm:bspairing}\n The cup product induces a pairing\n \\[ H^2_{\\mathrm{id}, \\an}(\\mathcal{S}_{H, \\Iw}(p^t), \\tau_A, \\cusp)^{-, \\dag} \\times H^0_{\\mathrm{id}, \\an}(\\mathcal{S}_{H, \\Iw}(p^t), \\tau_A)^{+, \\dag} \\longrightarrow A, \\]\n whose formation is compatible with base-change in $A$, and which is compatible with the Serre duality pairing on classical cohomology when $A = \\QQ_p$ and $\\nu$, $\\tau$ are classical weights.\n \\end{theorem}\n\n \\begin{proof}\n We define this pairing by combining the pullback map of \\ref{prop:bigsheafpullback} with the pairing between the cohomology groups $H^2_{\\mathrm{id}, \\an}(\\mathcal{S}_{H, \\Iw}(p^t), \\tau_A, \\cusp)^{-, \\dag}$ and $H^0_{\\mathrm{id}, \\an}(\\mathcal{S}_{H, \\Iw}(p^t), \\tau_A, \\cusp)^{+, \\dag}$. By construction, this is compatible with Serre duality for each classical weight.\n \\end{proof}\n\n\n\n\n\n\n\\section{Construction of the $p$-adic $L$-function}\n\n Let $L$ be a finite extension of $\\QQ_p$.\n\n \\subsection{Families of Eisenstein series}\n\n We refer to \\cite[\\S 7]{LPSZ1} for the construction of $p$-adic families of Eisenstein series $\\mathcal{E}^{\\Phi^{(p)}}(\\kappa_1, \\kappa_2; \\chi^{(p)})$, depending on a prime-to-$p$ Schwartz function $\\Phi^{(p)}$ and prime-to-$p$ Dirichlet character $\\chi^{(p)}$ (both valued in $L$) and a pair of characters $\\kappa_1, \\kappa_2$ of $\\ZZ_p^\\times$ (valued in some $p$-adically complete $L$-algebra $A$).\n\n \\begin{note}\n Note that this Eisenstein series is $p$-depleted, i.e.~lies in the kernel of $U_p$; and it is zero on any components of $\\Spec(A)$ which do not satisfy the parity condition $\\kappa_1(-1) \\kappa_2(-1) = -\\chi^{(p)}(-1)$.\n\n The construction factors through the projection of $\\Phi^{(p)}$ to the eigenspace where $\\stbt{a}{0}{0}{a}$ for $a \\in \\widehat{\\mathbf{Z}}^{(p)}$ acts as $\\widehat{\\chi}^{(p)}(a)^{-1}$, where $\\widehat{\\chi}^{(p)}$ is the adelic character attached to $\\chi^{(p)}$ as in \\cite[\\S 2.2]{LPSZ1}. We shall henceforth assume, without loss of generality, that $\\Phi^{(p)}$ lies in this eigenspace; thus $\\chi^{(p)}$ is uniquely determined by $\\Phi^{(p)}$ and we sometimes drop it from the notation.\n \\end{note}\n\n \\begin{proposition}\n If $A$ is an affinoid algebra, and one of the $\\kappa_i$ is a finite-order character, then $\\mathcal{E}^{\\Phi^{p}}(\\kappa_1, \\kappa_2)$ is an overconvergent $A$-valued cusp form of weight-character $1 + \\kappa_1 + \\kappa_2$.\n \\end{proposition}\n\n \\begin{proof}\n Since twisting by a finite-order character preserves overconvergence, it suffices to assume $\\kappa_1$ or $\\kappa_2$ is 0. Then our $p$-adic Eisenstein series is the $p$-depletion of a family of \\emph{ordinary} Eisenstein series, cf.~\\cite[\\S 2.3]{ohta99}, and it is well-known that these ordinary Eisenstein series are overconvergent (indeed, this is true by definition in Coleman's approach to overconvergent modular forms).\n \\end{proof}\n\n As noted in \\emph{op.cit.}, for $k \\ge 1$, the Eisenstein series $F^{k}_{\\Phi^p \\Phi_{\\mathrm{dep}}}$ described in \\cite[\\S 4.3]{LZ20b-regulator} is (the classical form associated to) $\\mathcal{E}^{\\Phi^p}(k-1, 0)$, and $E^{k}_{\\Phi^p \\Phi_{\\mathrm{dep}}}$ is $\\mathcal{E}^{\\Phi^p}(0, k-1)$. It also implies the following relation:\n\n \\begin{proposition}[cf.~{\\cite[Prop 16.2.1]{LZ20b-regulator}}]\n Let $t \\in \\mathbf{Z}_{\\ge 0}$. As overconvergent cusp forms of weight $-t$, we have\n \\[ \\theta^{-(1+t)}\\left(F^{(t+2)}_{\\Phi^p \\Phi_{\\mathrm{dep}}}\\right) = \\mathcal{E}^{\\Phi^p}(0, -1-t; \\Phi^{(p)}), \\]\n where $\\theta = q \\tfrac{\\mathrm{d}}{\\mathrm{d}q}$ is the Serre differential operator.\n \\end{proposition}\n\n \\subsection{Tame test data}\n\n We fix the following data:\n \\begin{itemize}\n \\item $M_0, N_0$ are positive integers coprime to $p$ with $M_0^2 \\mid N_0$, and $\\chi_0$ is a Dirichlet character of conductor $M_0$ (valued in $L$).\n \\item $M_2, N_2$ are positive integers coprime to $p$ with $M_2 \\mid N_2$, and $\\chi_2$ is a Dirichlet character of conductor $M_2$ (valued in $L$).\n \\end{itemize}\n We will consider automorphic representations $\\pi$ of $G$ with conductor $N_0$ and character $\\widehat{\\chi}_0$ up to twists by norm, and similarly $\\sigma$ of $\\GL_2$ with conductor $N_2$ and character $\\widehat{\\chi}_2$ up to twists by norm.\\footnote{This numbering of the parameters comes from the fact that the zeta-integral computations of \\cite{LPSZ1} are simpler to write down if the Eisenstein series lives on the first factor of $H$.}\n\n Let $S$ denote the set of primes dividing $N_0 N_2$. By \\emph{tame test data} we shall mean a pair $\\gamma_S = (\\gamma_{0, S}, \\Phi_S)$, where:\n \\begin{itemize}\n \\item $\\gamma_{0, S} \\in G(\\mathbf{Q}_S)$, where $\\mathbf{Q}_S = \\prod_{\\ell \\in S} \\mathbf{Q}_\\ell$;\n \\item $\\Phi_S \\in C^\\infty_c(\\mathbf{Q}_S^2, L)$, lying in the $\\left(\\widehat{\\chi}_0 \\widehat{\\chi}_2\\right)^{-1}$-eigenspace for $\\mathbf{Z}_S^\\times$.\n \\end{itemize}\n We let $K_S$ be the quasi-paramodular subgroup of $G(\\mathbf{Q}_S)$ of level $(N_0, M_0)$; and we let $\\widehat{K}_S$ be some open compact subgroup of $G(\\mathbf{Q}_S)$ such that:\n \\begin{itemize}\n \\item $\\widehat{K}_S \\subseteq \\gamma_{0, S} K_S \\gamma_{0, S}^{-1}$,\n \\item the projection of $\\widehat{K}_S \\cap H$ to the first factor of $H$ acts trivially on $\\Phi_S$,\n \\item the projection of $\\widehat{K}_S \\cap H$ to the second factor of $H$ is contained in $\\{ \\stbt \\star\\star0 1 \\bmod N_2\\}$.\n \\end{itemize}\n We define $K^p$ and $\\widehat{K}^p$ to be the products of $K_S$ and $\\widehat{K}_S$ with $G(\\AA_{\\mathrm{f}}^{pS})$, and $\\Phi^{(p)} = \\Phi_S \\cdot \\operatorname{ch}\\left((\\widehat{\\mathbf{Z}}^{S \\cup \\{p\\}})^2\\right)$.\n\n \\subsection{The correction term $Z_S$}\n\n Let $\\pi$ and $\\sigma$ be cohomological cuspidal automorphic representations of $G$ and of $\\GL_2$, both defined over some number field $E$ contained in the $p$-adic field $L$, and both globally generic and unramified outside $S$. We normalise so these are cohomological with weights $(r_1, r_2; r_1 + r_2)$ and $(t_2; t_2)$ respectively, for some integers $r_1, r_2, t_2$; and we let $\\Pi$ and $\\Sigma$ be the unitary twists of $\\pi$ and $\\sigma$ respectively, so that\n \\[ L(\\Pi \\times \\Sigma, s) = L(\\pi \\times \\sigma, s + \\tfrac{r_1 + r_2 + t_2}{2}).\\]\n\n \\begin{definition}\n For $W_0 \\in \\mathcal{W}(\\pi)_E$, $W_2 \\in \\mathcal{W}(\\sigma)_E$, and $\\Phi \\in \\mathcal{S}(\\mathbf{Q}_S^2, E)$, we consider the zeta-integral\n \\[ Z(W_0, \\Phi, W_2; s) = \\int_{(Z_G N_H \\backslash H)(\\mathbf{Q}_S)} W_0(h) f^{\\Phi}(h_1; \\omega_{\\pi}\\omega_\\sigma, s) W_2(h_2) \\, \\mathrm{d}h. \\]\n \\end{definition}\n\n We shall set\n \\[\n Z_S(\\pi \\times \\sigma, \\gamma_S; s) =\n \\frac{Z(\\gamma_{0, S} \\cdot W_0^{\\mathrm{new}}, \\Phi_S, W_2^\\mathrm{new}; s)}{G(\\chi_2^{-1})\\prod_{\\ell \\in S} L(\\pi_\\ell \\times \\sigma_\\ell, s)},\n \\]\n and\n \\[ Z_S(\\pi \\times \\sigma, \\gamma_S) = Z_S(\\pi \\times \\sigma, \\gamma_S; 1 + \\tfrac{t_1}{2})\\]\n where $t_1 = r_1 -r_2-2-t_2$ as usual. Here $G(\\chi) = \\sum_{a \\bmod N_\\chi} \\chi(a) \\exp(2\\pi i a \/ N_\\chi)$ is the Gauss sum of the character $\\chi$. One can check that this is a product of polynomials in the variables $\\ell^{\\pm s}$, for $\\ell \\in S$, with coefficients in $E$.\n\n \\begin{proposition}\n For any given $\\pi, \\sigma$, one can choose $\\gamma_S$ such that $Z_S(\\pi \\times \\sigma, \\gamma_S; s) \\ne 0$.\n \\end{proposition}\n\n \\begin{proof}\n This follows from the definition of the $L$-factor as a GCD of local zeta-integrals.\n \\end{proof}\n\n \\subsection{P-adic families for $G$}\n\n Let $U \\subset \\mathcal{W}^2$ be an open affinoid disc; and let $\\mathbf{r}_1$, $\\mathbf{r}_2: \\ZZ_p^\\times \\to \\mathcal{O}(U)^\\times$ be the universal characters associated to the two factors of $\\mathcal{W}^2$. Let $\\nu_U$ be the character $(\\mathbf{r}_1, \\mathbf{r}_2; \\mathbf{r}_1+\\mathbf{r}_2)$ of $T(\\ZZ_p)$.\n\n The theory of \\cite{boxerpilloni20} shows that there exists a rigid space $\\mathcal{E} \\xrightarrow{\\kappa} \\mathcal{W}^2$, with a map $\\mathbb{T}^- \\to \\mathcal{O}(\\mathcal{E})$ (the eigenvariety for $G$), and graded coherent sheaves $H^k(\\mathcal{M}^{\\bullet,-,\\fs}_{\\cusp, w_j})$ on $\\mathcal{E}$ for $0 \\le j, k \\le 3$, whose pushforward to any affinoid $U\\subset \\mathcal{W}^2$ as above is $H^k_{w_j,\\an}(K^p, \\nu_U, \\cusp)^{(-,\\fs)}$. By construction, the points of $\\mathcal{E}$ biject with systems of $\\mathbb{T}^-$-eigenvalues appearing in one of these modules.\n\n \\begin{definition}\n By a \\emph{family of automorphic representations} $\\underline{\\pi}$ over $U$ (of tame level $N_0$ and character $\\chi_0$), we mean the data of a finite flat covering $\\tilde{U} \\to U$, and a homomorphism $\\tilde{U} \\to \\mathcal{E}$ lifting the inclusion $U \\hookrightarrow \\mathcal{W}$, such that the following conditions hold:\n \\begin{itemize}\n \\item $\\tilde{U}$ is 2-dimensional and smooth;\n \\item the restriction of the sheaf $H^k(\\mathcal{M}^{\\bullet,-,\\fs}_{\\cusp, w_j})$ to $\\tilde{U}$ is zero if $k \\ne 3-j$, and the sheaves $S^k(\\underline{\\pi}) = H^k(\\mathcal{M}^{\\bullet,-,\\fs}_{\\cusp, w_{3-k}})$ are either free over $\\mathcal{O}(\\tilde{U})$ of rank 1 for all $k$ (a general-type family), or free of rank 1 for $k = 1,2$ and zero for $k = 0, 3$ (a Yoshida-type family);\n \\item the centre of $G(\\AA_{\\mathrm{f}}^p)$ acts on the modules $S^k(\\underline{\\pi})$ by the character $|\\cdot|^{-(\\mathbf{r}_1 + \\mathbf{r}_2)} \\widehat{\\chi}_0$.\n \\end{itemize}\n \\end{definition}\n\n Such a family determines a $\\mathcal{O}(\\tilde{U})$-valued character $\\lambda_{\\underline{\\pi}}^-$ of $\\mathbb{T}^-$, which is the system of eigenvalues by which $\\mathbb{T}$ acts on the modules $H^k(\\mathcal{M}^{\\bullet,-,\\fs}_{\\cusp, w_j})$; conversely, the character $\\lambda_{\\underline{\\pi}}^-$ and ring extension $\\mathcal{O}(\\tilde U)$ of $\\mathcal{O}(U)$ uniquely determine $\\underline{\\pi}$.\n\n \\begin{definition}\n We say a point $P \\in \\tilde{U}(L)$ is ``good for $\\underline{\\pi}$'' if the following conditions hold:\n \\begin{itemize}\n \\item the weight of $P$ is $(r_1, r_2) \\in U \\cap \\mathbf{Z}^2$ with $r_1 \\ge r_2 \\ge -1$;\n \\item the specialisation at $P$ of the system of eigenvalues $\\lambda^-_{\\underline{\\pi}}$ is the character of $\\mathbb{T}^-$ assocated to a $p$-stabilised automorphic representation $\\pi_P$, which is cuspidal, globally generic, and has conductor $N_0$ and character $\\chi_0$;\n \\item the fibre of $S^2(\\underline{\\pi})$ at $P$ maps isomorphically to the $\\pi_P$-eigenspace in the classical $H^2(K^p, \\kappa_1(\\nu), \\cusp)$; in particular, this eigenspace is 1-dimensional.\n \\end{itemize}\n \\end{definition}\n\n \\begin{remark}\n Note that we do not suppose that the $\\pi_P$ \\emph{generalised} eigenspace be 1-dimensional, and this will not hold when $\\tilde{U} \\to U$ is ramified at $P$.\n \\end{remark}\n\n By the classicity theorems for higher Coleman theory recalled above, given a family $\\underline{\\pi}$, all specialisations of integer weight $(r_1, r_2)$ with $r_1 - r_2$ and $r_2$ sufficiently large relative to the slope of $\\underline{\\pi}$ will be good; and if $\\underline{\\pi}$ is ordinary, it suffices to assume that $r_1-r_2 \\ge 3$ and $r_2 \\ge 0$.\n\n We shall choose a basis $\\underline{\\eta}$ of $S^2(\\underline{\\pi})$. Since the spaces of higher Coleman theory (of varying levels) have an action of $G(\\AA_{\\mathrm{f}}^p)$, we can make sense of $\\gamma_{0, S} \\cdot \\underline{\\eta}$ as a family of classes at tame level $\\widehat{K}^p$, which is still an eigenfamily for the Hecke operators away from $S$.\n\n \\subsection{Families for $\\GL_2$}\n\n Similarly, we choose a disc $U' \\subset \\mathcal{W}$, a finite flat covering $\\tilde{U}'\\to U'$ with $\\tilde{U}'$ smooth, and a finite-slope overconvergent $p$-adic family of modular eigenforms $\\mathcal{G}$ over $\\tilde{U}'$ (of weight $\\mathbf{t_2} + 2$ where $\\mathbf{t_2}$ is the universal character associated to $U'$). We suppose that this family is new away from $p$ of tame level $N_2$, and nebentype character $\\chi_2$.\n\n We say a point $Q \\in \\tilde{U}'$ is ``good for $\\mathcal{G}$'' if it lies above an integer $t \\in U' \\cap \\mathbf{Z}_{\\ge -1}$, and the specialisation of $\\mathcal{G}$ at $Q$, which is \\emph{a priori} an overconvergent form of weight $t + 2$, is in fact a classical form. (This is automatic if $t$ is sufficiently large compared to the slope of $\\mathcal{G}$.) We write $\\sigma_t$ for the corresponding automorphic representation (normalised to have central character $|\\cdot|^{-t} \\widehat{\\chi}_2$); and we formally write $\\underline{\\sigma}$ for the collection of the $\\sigma_t$ for varying $t$.\n\n A mildly irritating detail is that if $\\mathcal{G}$ is normalised to have $a_1(\\mathcal{G}) = 1$, and $t$ is a good specialisation, then $\\mathcal{G}_t$ has $q$-expansion coefficients in some number field $E$; but the modular form $\\mathcal{G}_t$ is not defined over $E$ as a coherent cohomology class, since the cusp $\\infty$ on $X_1(N)$ is not defined over $\\mathbf{Q}$ (with our conventions). However, the class $G(\\chi_2^{-1})\\mathcal{G}_t$ is $E$-rational. We write $S^0(\\sigma_t, E)$ for the $E$-vector space spanned by this form, and similarly $S^0(\\underline{\\sigma})$ for the $\\mathcal{O}(\\tilde{U}')$-module of overconvergent cusp forms generated by $G(\\chi_2^{-1}) \\mathcal{G}$.\n\n \\begin{remark}\n Note that by definition $S^0(\\underline{\\sigma})$ is free of rank 1, and its fibre at any good specialisation is in the image of the classical $H^0$ (because of the $q$-expansion principle for $p$-adic modular forms). Hence we do not need any auxiliary hypotheses about local freeness of sheaves.\n \\end{remark}\n\n \\subsection{Deforming eigenforms}\n\n Conversely, we say a classical ($p$-stabilised) automorphic representation $\\pi$, of some weight $\\nu$, is \\emph{deformable} if we can find a disc $U$ containing $\\nu$, a family $\\underline{\\pi}$ over some covering $\\tilde{U} \/ U$, and some $Q \\in \\tilde{U}$ above $\\nu$, such that $Q$ is good for $\\underline{\\pi}$ and the specialisation there is $\\pi$. The arguments of \\cref{sect:families} show that any generic $\\pi$ of cohomological weight, with a regular $p$-stabilisation of sufficiently small slope, will be deformable in the above sense (and we may suppose that $\\tilde{U}=U$); again, if $\\pi$ is ordinary, it suffices to suppose that $r_1 - r_2 \\ge 3$ and $r_2 \\ge 0$.\n\n For $\\GL_2$ we are in much better shape (partly because $\\GL_2$ is better understood than $\\GSp_4$, and partly because our definition of ``family'' is less restrictive): any classical $p$-stabilised newform of integer weight and Iwahori level at $p$ will be deformable, even in the worst-case scenario of non-$p$-regular weight 1 forms, since we may take $\\tilde{U}'$ to be a neighbourhood of $\\sigma$ in the normalisation of the eigencurve. Moreover, if $\\sigma$ is ordinary and has weight $\\ge 2$, we may suppose $\\tilde{U}' = U'$.\n\n \\begin{remark}\n We also expect that there exist interesting examples of deformable $\\pi$ for $G$ which do not satisfy these stringent conditions. It seems likely that the extra generality of a finite flat covering of weight space will be genuinely necessary, at least in the non-regular-weight case $r_2 = -1$. However, for simplicity of notation we shall assume $\\tilde{U} = U$ and $\\tilde{U}' = U'$ henceforth; extending these arguments to the general case is straightforward and we leave this to the reader.\n \\end{remark}\n\n\n \\subsubsection*{Families over $U \\times U'$} Let $A = \\mathcal{O}(U \\times U')$. We have two canonical $A$-valued characters of $T(\\ZZ_p)$: the canonical character $\\nu_A = (\\mathbf{r}_1, \\mathbf{r}_2; \\mathbf{r}_1 + \\mathbf{r}_2)$, and the character $\\tau_A = (\\mathbf{t}_1, \\mathbf{t}_2; \\mathbf{r}_1 + \\mathbf{r}_2)$ defined as follows: $\\mathbf{t}_2$ is the canonical character of $U'$ as above, and $\\mathbf{t}_1 = \\mathbf{r}_1 - \\mathbf{r}_2 - 2 - \\mathbf{t}_2$ and the action of the centre are determined by the requirement that $\\nu_A$ and $\\tau_A$ be ``compatible'' in the sense of \\cref{def:compat}. Then we can consider\n \\[ \\mathcal{E}^{\\Phi^{(p)}}(0, \\mathbf{t}_1 + 1) \\boxtimes G(\\chi_2^{-1}) \\mathcal{G}^{[p]} \\in H^0_{\\mathrm{id}, \\an}(\\mathcal{S}_{H, \\Iw}(p^2), \\tau_{A})^{+, \\dag},\\]\n where the tame level is taken to be $H \\cap \\widehat{K}^p$.\n\n \\begin{definition}\n We let $\\mathcal{L}_{p, \\gamma_S}(\\underline{\\pi} \\times \\underline{\\sigma}; \\underline{\\eta})$ denote the element of $A$ defined by\n \\[ \\left\\langle \\hat\\iota^*\\left(\\gamma_{0, S} \\cdot \\underline{\\eta}\\right), \\mathcal{E}^{\\Phi^{(p)}}(0, \\mathbf{t}_1 + 1) \\boxtimes G(\\chi_2^{-1}) \\mathcal{G}^{[p]}\\right\\rangle.\\]\n \\end{definition}\n\n (The product denotes the Serre duality pairing at level $\\widehat{K}^p \\cap H$, normalised by a factor $\\operatorname{vol}(\\widehat{K}^p \\cap H)$ in order to make it independent of the choice of $\\widehat{K}_p$.)\n \\begin{definition} \\\n \\begin{itemize}\n \\item We say a point $(P, Q)$ of $U \\times U'$ is \\emph{good} if $P = (r_1, r_2)$ and $Q = (t_2)$ are integer points, with $P$ good for $\\underline{\\pi}$ and $Q$ good for $\\underline{\\sigma}$.\n\n \\item We say $(P, Q)$ is \\emph{good critical} if we also have $t_2 \\le r_1 -r_2 - 1$ (i.e.~the specialisation $t_1$ of $\\mathbf{t}_1$ at $(P, Q)$ is $\\ge -1$).\n\n \\item If instead we have $r_1 - r_2 \\le t_2 \\le r_1$, we say $P$ is \\emph{good geometric}.\n \\end{itemize}\n \\end{definition}\n\n One checks easily that any integer point $(r_1, r_2, t_2)$ is the limit of a sequence of good geometric (or good critical) points, so if we exclude the pathological case when $(U \\times U') \\cap \\mathbf{Z}^3$ is empty, then the sets of good critical points and of good geometric points are both Zariski-dense in $U \\times U'$.\n\n \\subsection{Values in the critical range}\n\n \\begin{definition}\n For $(P, Q) = (r_1, r_2, t_2) \\in U \\times U'$ a good critical point, we define a degree 8 Euler factor\n \\[\n \\mathcal{E}_p(\\pi_P \\times \\sigma_Q) = \\left(1 - \\tfrac{p^{r_1 + 1}}{\\alpha \\mathfrak{a}}\\right)\\dots \\left( 1- \\tfrac{p^{r_1 + 1}}{\\beta \\mathfrak{b}}\\right) \\left(1 - \\tfrac{\\gamma \\mathfrak{a}}{p^{r_1 + 2}}\\right)\\dots\\left(1 - \\tfrac{\\delta \\mathfrak{b}}{p^{r_1 + 2}}\\right).\n \\]\n where $\\alpha, \\dots, \\delta$ are the Hecke parameters of $\\pi_P$, and $\\mathfrak{a},\\mathfrak{b}$ the Hecke parameters of $\\sigma_Q$ (so that $\\mathfrak{a}\\mathfrak{b} = p^{t_2 + 1} \\chi_2(p)$).\n \\end{definition}\n\n \\begin{proposition}\n If $\\pi_P$ is ordinary, then $\\mathcal{E}_p(\\pi_P\\times \\sigma_Q) \\ne 0$.\n \\end{proposition}\n\n \\begin{proof}\n This follows by a (somewhat tedious) explicit check from the bounds on the valuations of the Hecke parameters.\n \\end{proof}\n\n \\begin{theorem}\n The $p$-adic $L$-function $\\mathcal{L}_{p, \\gamma_S}(\\underline{\\pi} \\times \\underline{\\sigma}, \\underline{\\eta})$ has the following interpolation property: if $(P, Q)$ is good critical, then\n \\[ \\frac{\\mathcal{L}_{p, \\gamma_S}(\\underline{\\pi} \\times \\underline{\\sigma}, \\underline{\\eta})(P, Q)}{\\Omega_p(\\pi_P, \\eta_P)}\n =Z_S(\\pi_{P} \\times \\sigma_{Q}, \\gamma_S) \\cdot \\mathcal{E}_p(\\pi_P \\times \\sigma_Q) \\cdot\n \\frac{G(\\chi_2^{-1})^2 \\Lambda\\left(\\Pi_P \\times \\Sigma_Q, 1 + \\tfrac{t_1}{2}\\right)}{\\Omega_\\infty(\\pi_P, \\eta_P)}, \\]\n with both sides lying in the field of rationality of $\\pi_P\\times \\sigma_Q$.\n\n Here $\\Pi_P$ and $\\Sigma_Q$ are the (unitary) automorphic representations generated by the specialisations of $\\underline{\\eta}$ and $\\mathcal{G}$ at $P$; and $\\Lambda(\\Pi_P\\times \\Sigma_Q, s)$ denotes the $L$-function of these automorphic representations, with its archimedean $\\Gamma$-factors included.\n \\end{theorem}\n\n \\begin{remark}\n Note that $s = 1 + \\tfrac{t_1}{2}$ is the upper endpoint of the interval of critical values (in the sense of Deligne) for the degree 8 $L$-function $L\\left(\\Pi_P \\times \\Sigma_Q, s\\right)$. This critical interval is symmetric about $s = \\tfrac{1}{2}$, so unless $t_2 = r_1 - r_2 - 1$ (so that $t_1 = -1$), there are other critical values which we do not see by this method.\n\n We optimistically hope that there should be a $p$-adic $L$-function on the 4-dimensional space $U \\times U' \\times \\mathcal{W}$ which interpolates the full range of critical values, and that both the above $p$-adic $L$-function on $U \\times U'$, and the 2-variable $p$-adic $L$-function on $U' \\times \\mathcal{W}$ (for fixed $\\pi$) considered in \\cite[\\S 5]{LZ20b-regulator}, should be ``slices'' of this more general construction. However, this seems beyond reach with our present methods.\n \\end{remark}\n\n \\begin{proof}\n By construction, we have\n \\[ \\mathcal{L}_{p, \\gamma_S}(\\underline{\\pi} \\times \\underline{\\sigma}; \\underline{\\eta})(P, Q) = G(\\chi_2^{-1}) \\left\\langle \\hat\\iota^*\\left(\\gamma_{0, S} \\cdot \\eta_P\\right), \\mathcal{E}^{\\Phi^{(p)}}(0, t_1 + 1) \\boxtimes \\mathcal{G}_P^{[p]}\\right\\rangle. \\]\n This expands as the product of $G(\\chi_2^{-1})\\Lambda\\left(\\Pi_P \\times \\Sigma_Q, 1 + \\tfrac{t_1}{2}\\right)$ and a product of normalised local zeta-integrals, exactly as in \\cite{LPSZ1}. The local integrals away from $pS$ are all 1. The local zeta-integral at $p$ is evaluated in \\cite{LZ21-zeta2}, and gives the Euler factor $\\mathcal{E}_p(-)$. The product of zeta-integrals at the bad primes is by definition $G(\\chi_2^{-1}) Z_S(\\dots)$ and the result follows.\n \\end{proof}\n\n \\subsection{Values in the geometric range}\n\n Suppose $(P, Q)\\in U \\times U'$ is a point in the good geometric range; and let us set $t_1' = -2-t_1 = t_2 - r_1 + r_2$. Then the ``geometric'' condition implies that $0 \\le t_1' \\le r_2$, and the quadruple $(r_1, r_2, t_1', t_2)$ satisfies the branching law for algebraic representations defined in \\cite[Proposition 6.4]{LPSZ1}, which is the condition needed to define motivic cohomology classes associated to $\\pi_P \\otimes \\sigma_Q$, using the pushforward of a $\\GL_2$ Eisenstein class of weight $t_1'$ (see \\cite{HJS20}).\n\n \\begin{remark}\n Note that this Euler system class lands in the Galois representation $V_p(\\pi \\times \\sigma)^*(-1-r_1)$, and corresponds to the complex $L$-function $L(\\pi \\times \\sigma, s)$ at $s = -\\tfrac{t_1'}{2} = 1 + \\tfrac{t_1}{2}$; but this is no longer a critical value, and the Archimedean $\\Gamma$-factors force the $L$-function to vanish here to degree exactly one (except in some exceptional cases when $t_1' = 0$ and $\\pi$ is a Yoshida lift, when it can happen that the completed $L$-function has a simple pole at $s = 0, 1$).\n\n The $L$-values having this property are an interval (disjoint from the critical interval, if any) and the value $s = -\\tfrac{t_1'}{2}$ is the \\emph{upper} end of this interval. So our restriction to using only overconvergent, rather than nearly-overconvergent, Eisenstein series pegs us to the the upper endpoint of the critical interval when $P$ is critical, and to the upper endpoint of the geometric interval when $P$ is geometric.\n \\end{remark}\n\n In \\cite[\\S 4]{LZ20b-regulator}, we defined an object $\\operatorname{Per}_{\\eta}(\\pi_P \\times \\sigma_Q)$ associated to $\\pi_P \\times \\sigma_Q$, the choice of twist $t_1'$, and the basis vector $\\eta_P \\in S^2(\\pi_P, L)$. This was an $H(\\AA_{\\mathrm{f}}^p)$-equivariant map $\\mathcal{T}^p \\to L$, where $\\mathcal{T}^p = \\mathcal{W}(\\pi_{P, \\mathrm{f}}^p) \\otimes C^\\infty_c( (\\AA_{\\mathrm{f}}^p)^2) \\otimes \\mathcal{W}(\\sigma_{Q, \\mathrm{f}}^p)$. Our choice of $\\gamma_S$ defines a choice of vector\n \\[ (\\gamma_{0, S} W^{\\mathrm{new}}_{\\pi_P}) \\otimes \\Phi_S \\otimes W^{\\mathrm{new}}_{\\sigma_Q} \\in \\mathcal{T}^p \\]\n and we write $\\operatorname{Per}_{\\eta}(\\pi \\times \\sigma, \\gamma_S) \\in L$ for the value of $\\operatorname{Per}_{\\eta}(\\pi \\times \\sigma)$ on this vector.\n\n \\begin{remark}\n If $t_1 \\ne 0$, then one can check that the space of $H(\\AA_{\\mathrm{f}}^p)$-equivariant maps in which $\\operatorname{Per}_{\\eta}(\\pi_P \\times \\sigma_Q)$ lies is in fact 1-dimensional and spanned by the product of zeta integrals used to define $Z_S(\\dots)$. It follows that there is a quantity $\\operatorname{Per}_{\\eta}(\\pi_P \\times \\sigma_Q)^{\\mathrm{univ}} \\in L$ such that for all $\\gamma_S$ we have\n \\[ \\operatorname{Per}_{\\eta}(\\pi_P \\times \\sigma_Q, \\gamma_S) = Z_S(\\pi_{P} \\times \\sigma_{Q}, \\gamma_S) \\operatorname{Per}_{\\eta}(\\pi_P \\times \\sigma_Q)^{\\mathrm{univ}}. \\]\n Similar results also hold for $t_1 = 0$ under some mild additional conditions on $\\pi_P$ and $\\sigma_Q$; compare Theorem 6.6.2 of \\cite{LZ20} in the $\\GSp_4$ case. However, we do not need this for the proof of our main theorem, so we shall not pursue it further here.\n \\end{remark}\n\n \\begin{proposition}\n \\label{prop:coherentperiod}\n We have\n \\[\n \\mathcal{L}_{p, \\gamma_S}(\\underline{\\pi} \\times \\underline{\\sigma}; \\underline{\\eta})(P, Q) = \\operatorname{Per}_{\\eta_P}(\\pi_P \\times \\sigma_Q, \\gamma_S).\n \\]\n \\end{proposition}\n\n \\begin{proof}\n By construction, we have\n \\[\n \\operatorname{Per}_{\\eta_P}(\\pi_P \\times \\sigma_Q, \\gamma_S) = \\left\\langle \\iota^*_{\\Kl}\\left(\\gamma_{0, S}\\cdot \\eta_{P, \\Kl}\\right), \\theta^{-{t_1'}}\\left(F^{(t_1' + 2)}_{\\Phi^p \\Phi_{\\mathrm{dep}}}\\right) \\boxtimes G(\\chi_2^{-1}) \\mathcal{G}^{[p]}\\right\\rangle, \\]\n where $\\iota_{\\Kl}$ is an embedding of Shimura varieties at Klingen level. The term on the right-hand side is exactly the specialisation at $P$ of our family of $p$-adic modular forms for $H$. From the zeta-integral computations of \\cite{LZ21-zeta2}, we may replace $\\iota^*_{\\Kl}\\left(\\eta_{\\Kl}\\right)$ with $\\hat\\iota^*\\left(\\eta_{\\Iw}\\right)$ without changing the value of the pairing.\n \\end{proof}\n\n\n\n\\section{Families of cohomology classes}\n\n We persist with the notation and assumptions of the previous section. We also suppose that the family $\\underline{\\pi}$ is not of Yoshida type, so that for each classical specialisation $P$, the $\\lambda_{P}$-eigenspace in \\'etale cohomology of $S_{G, K^p\\Iw(p)}$ is 4-dimensional. We suppose furthermore that $\\underline{\\pi}$ and the $\\GL_2$ family $\\underline{\\sigma}$ are ordinary at $p$.\n\n \\subsection{Galois representations}\n\n Associated with the family $\\underline{\\pi}$ we have a family of Galois representations $V(\\underline{\\pi})$, which is a rank 4 $\\mathcal{O}(U)$-module with an action of $\\Gal(\\overline{\\mathbf{Q}}\/\\mathbf{Q})$, unramified outside $pN_0$ and satisfying $\\operatorname{tr}(\\operatorname{Frob}_\\ell^{-1} | V(\\underline{\\pi})) = \\lambda(T_{1,\\ell})$ for $\\ell \\nmid pN_0$.\n\n The existence of this family is a consequence of the results of \\cite{tilouineurban99}, who also give a canonical realisation of the dual representation $V(\\underline{\\pi})^*$ as a localisation of the module\n \\[ e'_B \\cdot \\varprojlim_t H^3_{\\text{\\textup{\\'et}}, c}\\left(\\mathcal{S}_{G, K^pK_{p, t}, \\overline{\\mathbf{Q}}}, \\ZZ_p\\right) \\otimes_{\\ZZ_p[[\\ZZ_p^{\\times 2}]]} \\mathcal{O}(U), \\]\n where $K_{p, t}$ is some family of subgroups of $G(\\ZZ_p)$ and $e^-_B$ is the ordinary projector associated to $\\mathcal{U}'_B$. Similarly, there is a 2-dimensional family of Galois representations over $U'$ associated to $\\underline{\\sigma}$.\n\n \\begin{remark}\n If the family $\\underline{\\pi}$ has a classical specialisation whose weight is sufficiently regular, but small relative to $p$ (and some additional hypotheses hold regarding the image of the residual Galois representation), then the results of \\cite{mokranetilouine02} and \\cite{rockwood-control} imply that $V(\\underline{\\pi})$ is free of rank 4 over $\\mathcal{O}(U)$.\n\n Without this condition, we can only deduce that $V(\\underline{\\pi})$ is locally free in a neighbourhood of each good \\emph{cohomological} weight, but not necessarily elsewhere. One can work around this by replacing $V(\\underline{\\pi})^*$ with its double dual (reflexive hull), which does not change its specialisations in cohomological weights.\n \\end{remark}\n\n\n \\begin{definition}\n We set\n \\[\\mathbb{V}^* = V(\\underline{\\pi})^* \\times V(\\underline{\\sigma})^*(-1-\\mathbf{r}_1),\\]\n which is an 8-dimensional family of Galois representations over $U \\times U'$.\n \\end{definition}\n\n \\subsection{Ordinary filtrations at $p$}\n\n The Galois representation $V(\\underline{\\pi})$ has a decreasing filtration by $\\mathcal{O}(U)$-submodules stable under $\\Gal(\\overline{\\mathbf{Q}}_p\/\\QQ_p)$ (via results of Urban \\cite{urban05}; see \\cite[Theorem 17.3.1]{LZ20} for the formulation we use). We write $\\mathcal{F}^i V(\\underline{\\pi})$ for the codimension $i$ subspace, and similarly for its dual $V(\\underline{\\pi})^*$. Note that $\\Gr^0 V(\\underline{\\pi})^*$ is unramified, with arithmetic Frobenius acting as the $U_{\\Sieg}$-eigenvalue. Abusing notation slightly\\footnote{What we really mean is that $\\Gr^1 V(\\underline{\\pi})^*$ is isomorphic to the tensor product of $\\chi_{\\mathrm{cyc}}^{(1 + \\mathbf{r}_2)}$ and an unramified character.}, we may say that $\\Gr^1 V(\\underline{\\pi})^*$ has ``Hodge--Tate weight $1 + \\mathbf{r}_2$''.\n\n Similarly, there is a 2-step filtration of $V(\\underline{\\sigma})^*$, with $\\Gr^1V(\\underline{\\sigma})^* = \\mathcal{F}^1 V(\\underline{\\sigma})^*$ having Hodge--Tate weight $1 + \\mathbf{t}_2$.\n\n \\begin{definition}\n We set\n \\[\\mathbb{V}^* = V(\\underline{\\pi})^* \\times V(\\underline{\\sigma})^*(-1-\\mathbf{r}_1);\\]\n and we let\n \\[ \\mathcal{F}^{(f)} V(\\underline{\\pi} \\times \\underline{\\sigma})^* = \\mathcal{F}^2 V(\\underline{\\pi}) \\otimes V(\\underline{\\sigma})^*,\\]\n and\n \\[ \\mathcal{F}^{(e)} V(\\underline{\\pi} \\times \\underline{\\sigma})^* = \\left(\\mathcal{F}^2 V(\\underline{\\pi})^* \\otimes V(\\underline{\\sigma})^*\\right) + \\left(\\mathcal{F}^1 V(\\underline{\\pi})^* \\otimes \\mathcal{F}^1 V(\\underline{\\sigma})^*\\right). \\]\n For a good weight $(P, Q)$ we write $\\mathbb{V}_{P, Q}^*$ for the specialisation of $\\mathbb{V}^*$ at $(P, Q)$, so $\\mathbb{V}_{P, Q}^* = V(\\pi_P)^* \\otimes V(\\sigma_Q)^*(-1-r_1)$ if $P = (r_1, r_2)$.\n \\end{definition}\n\n (For the significance of the labels (e) and (f), see Figure 2 of \\cite{LZvista}.) Thus $\\mathcal{F}^{(e)}$ has rank 5, $\\mathcal{F}^{(f)}$ has rank 4, and the quotient $\\Gr^{(e\/f)} \\cong \\left(\\Gr^1 V(\\pi)^*\\right)\\otimes\\left(\\mathcal{F}^1 V(\\underline{\\sigma})^*\\right)(-1-\\mathbf{r}_1)$ has Hodge--Tate weight $\\mathbf{t}_1' = -2-\\mathbf{t}_1$.\n\n \\begin{remark}\n Note that\n \\[ \\mathcal{E}_p(\\pi_P\\times \\sigma_Q) = \\det \\left(1 - \\varphi: \\mathbf{D}_{\\mathrm{cris}}(\\mathcal{F}^{(f)} \\mathbb{V}_P^*)\\right) \\cdot \\det\\left( 1 - p^{-1}\\varphi^{-1}: \\mathbf{D}_{\\mathrm{cris}} \\left(\\mathbb{V}_P^* \/ \\mathcal{F}^{(f)}\\right) \\right).\\qedhere\\]\n \\end{remark}\n\n\n \\subsection{P-adic periods}\n\n The representations $\\Gr^1V(\\underline{\\pi})^*(-1-\\mathbf{r_2})$ and $\\Gr^1 V(\\underline{\\sigma})^*(-1-\\mathbf{t}_2)$ are unramified, and hence crystalline as $\\mathcal{O}(U)$ (resp.~$\\mathcal{O}(U')$)-linear representations. Since $\\mathbf{D}_{\\mathrm{cris}}(\\QQ_p(1))$ is canonically $\\QQ_p$, we can therefore define $\\mathbf{D}_{\\mathrm{cris}}(\\Gr^{(e\/f)} \\mathbb{V}^*)$ to be an alias for the rank 1 $\\mathcal{O}(U \\times U')$-module\n \\[\n \\mathbf{D}_{\\mathrm{cris}}\\left(\\Gr^1 V(\\pi)^*(-1-\\mathbf{r_2})\\right) \\mathop{\\hat\\otimes} \\mathbf{D}_{\\mathrm{cris}}\\left( \\Gr^1 V(\\underline{\\sigma})^*(-1-\\mathbf{t}_2)\\right).\n \\]\n As in \\cite[\\S 8.2]{KLZ17}, we can define a Coleman\/Perrin-Riou big logarithm map for $\\Gr^{(e\/f)} \\mathbb{V}^*$, which is a morphism of $\\mathcal{O}(U \\times U')$-modules\n \\[ \\mathcal{L}^{\\mathrm{PR}}: H^1(\\QQ_p, \\Gr^{(e\/f)} \\mathbb{V}^*) \\to \\mathbf{D}_{\\mathrm{cris}}(\\Gr^{(e\/f)} \\mathbb{V}^*). \\]\n By construction, for good geometric weights $P$, this specialises to the Bloch--Kato logarithm map, up to an Euler factor; and for good critical weights it specialises to the Bloch--Kato dual exponential.\n\n \\subsection{P-adic Eichler--Shimura isomorphisms}\n\n Let $P$ be a good weight. Then the Faltings--Tsuji comparison isomorphism of $p$-adic Hodge theory gives an identification between $\\mathbf{D}_{\\mathrm{cris}}(V(\\pi_P))$ and the $\\pi_P$-eigenspace in de Rham cohomology (compatibly with the Hodge filtration); and the graded pieces of this filtration are identified with the coherent cohomology groups $S^i(\\pi_P, L)$.\n\n Since the Hodge and Newton filtrations on $\\mathbf{D}_{\\mathrm{cris}}$ must be complementary to each other (by weak admissibility), we deduce that there is an \\emph{Eichler--Shimura} isomorphism\n \\[\n \\ES^2_{\\pi_P}: S^2(\\pi_P, L)\\cong \\Gr^{(r_2 + 1)}_{\\mathrm{Hdg}} \\mathbf{D}_{\\mathrm{cris}}(V(\\pi_P)) \\cong \\mathbf{D}_{\\mathrm{cris}}(\\Gr^2 V(\\pi_P)).\n \\]\n Concretely, the isomorphism is given by mapping an element in $\\Gr^{(r_2 + 1)}_{\\mathrm{Hdg}} \\mathbf{D}_{\\mathrm{cris}}(V(\\pi_P))$ to its unique lifting to $\\Fil^{(r_2 + 1)}_{\\mathrm{Hdg}}\\mathbf{D}_{\\mathrm{cris}}(V(\\pi_P)) \\cap \\ker( (\\varphi - \\alpha_P)(\\varphi - \\beta_P))$.\n\n \\begin{remark}\n More generally, we have isomorphisms $\\ES^i: S^i(\\pi_P, L)\\cong \\mathbf{D}_{\\mathrm{cris}}(\\Gr^i V(\\pi_P))$ for each $0 \\le i \\le 3$, where $S^i(\\pi_P, L)$ is the $\\pi_P$-eigenspace in coherent $H^i$.\n\n We caution the reader that although the source and target of $\\ES^i_{\\pi_P}$ are the specialisations at $P$ of rank-one $\\mathcal{O}(U)$-modules, it is \\textbf{by no means obvious} that the isomorphisms $\\ES^i_{\\pi_P}$ for varying $P$ are the specialisations of a single $\\mathcal{O}(U)$-module isomorphism ``$\\ES^i_{\\underline{\\pi}}$''. We shall establish (a slightly weakened form of) this below, under some additional hypotheses, as a by-product of our main Euler system argument.\n\n It would be very interesting to have a direct construction of the maps $\\ES^i_{\\underline{\\pi}}$ by methods of arithmetic geometry. For $i = 0$ (corresponding to classical holomorphic Siegel modular forms) this has been achieved in the recent preprint \\cite{diao-rosso-wu21}. One can also obtain $\\ES^3_{\\underline{\\pi}}$ from this via Serre duality; but it seems to be more difficult to construct the ``intermediate'' filtration steps $i = 1, 2$.\n \\end{remark}\n\n \\subsubsection{Analogue for $\\GL_2$} Similarly, for $\\GL_2$ we have an isomorphism\n \\[ \\ES^0_{\\sigma_Q}: S^0(\\sigma_Q, L) \\cong \\mathbf{D}_{\\mathrm{cris}}(\\Gr^0 V(\\sigma_Q)). \\]\n In this setting the existence of comparison isomorphisms in families is known:\n\n \\begin{theorem}[Ohta, Kings--Loeffler--Zerbes]\n There exists an isomorphism of $\\mathcal{O}(U')$-modules\n \\[ \\ES^0_{\\underline{\\sigma}}: S^0(\\underline{\\pi}) \\cong \\mathbf{D}_{\\mathrm{cris}}(\\Gr^0 V(\\underline{\\sigma}))\\]\n interpolating the isomorphisms $\\ES^0_{\\sigma_Q}$ for varying $P$, where $\\mathcal{S}^0(\\underline{\\pi})$ is the $\\mathcal{O}(U')$-module spanned by $\\underline{\\omega} = G(\\chi_2^{-1}) \\cdot \\mathcal{G}$.\n \\end{theorem}\n \\begin{proof}\n This is a restatement of \\cite[Proposition 10.1.1(1)]{KLZ17}, where it is derived from results of Ohta \\cite{ohta00}. For an alternative derivation applying to possibly non-ordinary Coleman families, see \\cite{andreattaiovitastevens,loefflerzerbes16}.\n \\end{proof}\n\n \\subsection{Euler system classes}\n Let us suppose that the character $\\chi_0 \\chi_2$ is non-trivial (this allows us to get rid of a ``smoothing factor'' $c$ appearing in the Euler system constructions). Then, associated to the data $\\gamma_S$, we also have a family of cohomology classes\n \\[ \\mathbf{z}_{m}(\\underline{\\pi} \\times \\underline{\\sigma}, \\gamma_S) \\in H^1(\\mathbf{Q}(\\mu_{m}), \\mathbb{V}^*), \\]\n for all square-free integers coprime to some finite set $T \\supseteq S \\cup \\{p\\}$.\n By construction, the image of $ \\mathbf{z}_{m}(\\underline{\\pi} \\times \\underline{\\sigma}, \\gamma_S)$ under localisation at $p$ lands in the image of the (injective) map from the cohomology of $\\mathcal{F}^{(e)} \\mathbb{V}^*$. So we may make sense of\n \\[ \\mathcal{L}^{\\mathrm{PR}}\\left( \\mathbf{z}_{m}(\\underline{\\pi} \\times \\underline{\\sigma}, \\gamma_S) \\right) \\in \\mathbf{D}_{\\mathrm{cris}}(\\Gr^{(e\/f)} \\mathbb{V}^*). \\]\n We denote its image under specialisation at $(P,Q)$ by $ \\mathcal{L}^{\\mathrm{PR}}\\left( \\mathbf{z}_{m}(\\underline{\\pi} \\times \\underline{\\sigma}, \\gamma_S) \\right)(P,Q)$.\n Combining \\cref{prop:coherentperiod} with the main result of \\cite{LZ20b-regulator}, which relates the periods $\\operatorname{Per}_\\eta(\\dots)$ to the Euler system classes, we have the following result:\n\n \\begin{theorem}\\label{prop:geomreg}\n For each $P$ in the good geometric range, we have\n \\[ \\left\\langle \\mathcal{L}\\left( \\mathbf{z}_{1}(\\underline{\\pi} \\times \\underline{\\sigma}, \\gamma_S) \\right)(P,Q), \\mathrm{ES}^2_{\\pi_P}(\\eta_P) \\otimes \\mathrm{ES}^0_{\\sigma_Q}(\\omega_P)\\right\\rangle = \\mathcal{L}_{p,\\gamma_S}(\\underline{\\pi} \\times \\underline{\\sigma}; \\underline{\\eta})(P, Q). \\]\n \\end{theorem}\n\n \\subsection{Reciprocity laws and meromorphic Eichler--Shimura}\n\n \\begin{definition}\n Let $\\mathfrak{S}(\\underline{\\pi}; \\underline{\\sigma})$ denote the set of points $P = (r_1, r_2) \\in U \\cap \\mathbf{Z}^2$ which are good for $\\underline{\\pi}$, and satisfy the following condition: there exists some $t_2 \\in U' \\cap \\mathbf{Z}_{\\ge 0}$, and some local data $\\gamma_S$, such that $(P, Q) = (r_1, r_2, t_2)$ is good geometric and $\\mathcal{L}_{p, \\gamma_S}(\\underline{\\pi} \\times \\underline{\\sigma};\\underline{\\eta})$ is non-vanishing at $(P, Q)$.\n \\end{definition}\n\n \\begin{lemma}\n Let $\\underline{\\sigma}, \\underline{\\sigma}'$ be two Hida families satisfying our running hypotheses (possibly of different tame levels and characters). Then the set $\\mathfrak{S}(\\underline{\\pi},\\underline{\\sigma}) \\cap \\mathfrak{S}(\\underline{\\pi},\\underline{\\sigma}')$ is Zariski-dense. In particular, $\\mathfrak{S}(\\underline{\\pi},\\underline{\\sigma})$ is itself Zariski-dense.\n \\end{lemma}\n\n \\begin{proof}\n We first note that there exists $\\gamma_S$ for which $\\mathcal{L}_{p, \\gamma_S}(\\underline{\\pi} \\times \\underline{\\sigma};\\underline{\\eta})$ is not identically zero. To see this, we choose some good \\emph{critical} point $(P, Q)$ having $t_1 = r_1 - r_2 - t_2 - 2 \\ge 0$, so that $\\Lambda(\\pi_P \\times \\sigma_Q, 1 + \\tfrac{t_1}{2})$ lies outside the strip $0 < \\Re(s) < 1$ and hence cannot vanish. We can then choose $\\gamma_S$ such that $Z_S(\\pi_P \\times \\sigma_Q, \\gamma_S) \\ne 0$ (which is always possible). Thus $\\mathcal{L}_{p, \\gamma_S}(\\underline{\\pi} \\times \\underline{\\sigma})$ is non-vanishing at $(P, Q)$, and hence generically non-vanishing on $U \\times U'$.\n\n Repeating the construction, we can find local data $\\gamma_{S}'$ for $\\underline{\\pi} \\times \\underline{\\sigma}'$ such that $\\mathcal{L}_{p, \\gamma_S'}(\\underline{\\pi} \\times \\underline{\\sigma}')$ is generically non-vanishing. So there is an open subset $V \\subset U$ such that for all $v \\in V$, neither $\\mathcal{L}_{p, \\gamma_S}(\\underline{\\pi} \\times \\underline{\\sigma})$ nor $\\mathcal{L}_{p, \\gamma_S'}(\\underline{\\pi} \\times \\underline{\\sigma}')$ vanishes identically along $\\{ v \\} \\times U'$.\n\n Since $V$ is open, it must contain some $(r_1, r_2) \\in V \\cap \\mathbf{Z}^2$; and we can therefore find an integer $t$ such that both $p$-adic $L$-functions are non-vanishing at $P = (r_1, r_2, t)$. We consider the sequence of weights $P_k = (r_1 + 3(p-1)p^{k}, r_2 + (p-1)p^{k}, t_2 + 2(p-1)p^k)$ for $k \\to \\infty$. For all but finitely many $k$ the weight $P_k$ will be good geometric, and $P_k$ tends to $P$, so $\\mathcal{L}_{p, \\gamma_S}(P_k) \\ne 0$ for sufficiently large $k$. Thus the projection of $P_k$ to $U$ lies in $\\mathfrak{S}(\\underline{\\pi} \\times \\underline{\\sigma})$, and also in $\\mathfrak{S}(\\underline{\\pi} \\times \\underline{\\sigma}')$. It follows that $(r_1, r_2)$ is a limit point of $\\mathfrak{S}(\\underline{\\pi} \\times \\underline{\\sigma}) \\cap \\mathfrak{S}(\\underline{\\pi} \\times \\underline{\\sigma}')$ in the analytic topology. Thus the Zariski-closure of this intersection contains all points of $U \\cap \\mathbf{Z}^2$ outside a proper closed subset, and hence must be all of $U$.\n \\end{proof}\n\n Let us write $\\mathcal{Q}(U)$ for the fraction field of $\\mathcal{O}(U)$ (and similarly for $U \\times U'$ etc).\n\n \\begin{theorem}\n There exists an isomorphism of $\\mathcal{Q}(U)$-modules\n \\[ \\ES^2_{\\underline{\\pi}}: S^2(\\underline{\\pi}) \\otimes_{\\mathcal{O}(U)} \\mathcal{Q}(U) \\cong \\mathbf{D}_{\\mathrm{cris}}(\\Gr^2 V(\\underline{\\pi})) \\otimes_{\\mathcal{O}(U)} \\mathcal{Q}(U), \\]\n depending only on $\\underline{\\pi}$, characterised uniquely by the following property: for all Hida families $\\underline{\\sigma}$ as above, and all $P = (r_1, r_2) \\in \\mathfrak{S}(\\underline{\\pi}, \\underline{\\sigma})$, the morphism $\\ES^2_{\\underline{\\pi}}$ is non-singular at $P$ and its fibre at $P$ coincides with the Eichler--Shimura morphism $\\ES^2_{\\pi_P}$. Moreover, we have the explicit reciprocity law\n \\[\n \\left\\langle\n \\mathcal{L}\\left( \\mathbf{z}_{m}(\\underline{\\pi} \\times \\underline{\\sigma}, \\gamma_S)\\right),\n \\mathrm{ES}^2_{\\underline{\\pi}}(\\underline{\\eta}) \\otimes \\mathrm{ES}^0_{\\underline{\\sigma}}(\\underline{\\omega})\\right\\rangle = \\mathcal{L}_{p,\\gamma_S}(\\underline{\\pi} \\times \\underline{\\sigma}; \\underline{\\eta}).\n \\]\n \\end{theorem}\n\n \\begin{proof}\n We start by choosing a ``random'' isomorphism $\\jmath$ between $S^2(\\underline{\\pi})$ and $\\mathbf{D}_{\\mathrm{cris}}(\\Gr^2 V(\\underline{\\pi}))$, which is possible since both are free rank 1 $\\mathcal{O}(U)$-modules.\n\n As in the proof of the preceding lemma, we choose local data $\\gamma_S$ such that $\\mathcal{L}_{p, \\gamma_S}(\\underline{\\pi}, \\underline{\\sigma}; \\underline{\\eta})$ is not identically zero, and consider the ratio\n \\[ \\mathsf{R} = \\frac{1}{\\mathcal{L}_{p, \\gamma_S}(\\underline{\\pi} \\times \\underline{\\sigma}, \\underline{\\eta})} \\left\\langle\\mathcal{L}\\left( \\mathbf{z}_{m}(\\underline{\\pi} \\times \\underline{\\sigma}, \\gamma_S)\\right),\n \\jmath(\\underline{\\eta}) \\otimes \\mathrm{ES}^0_{\\underline{\\sigma}}(\\underline{\\omega})\\right\\rangle \\in \\mathcal{Q}(U \\times U').\n \\]\n\n If we now take a $(P, Q)$ that is good geometric, and such that $\\mathcal{L}_{p, \\gamma_S}(\\underline{\\pi}, \\underline{\\sigma}; \\underline{\\eta})$ does not vanish at $(P, Q)$, it follows from the \\cref{prop:geomreg} that $\\mathsf{R}$ is regular at $(P, Q)$ and its value there is equal to the ratio $\\jmath_P \/ \\ES^2_{\\pi_P}$ (independent of $Q$).\n\n We claim that $\\mathsf{R} \\in \\mathcal{Q}(U)$; that is, as a meromorphic function on $U \\times U'$, it is independent of the $U'$ variable. To justify this, we argue as in Proposition 17.7.3 of \\cite{LZ20}: we consider the meromorphic function $\\mathsf{R}(\\mathbf{r}_1, \\mathbf{r}_2, \\mathbf{t}_2) - \\mathsf{R}(\\mathbf{r}_1, \\mathbf{r}_2, \\hat{\\mathbf{t}}_2)$ on $U \\times U' \\times U'$, where $\\hat{\\mathbf{t}}_2$ is the coordinate on a second copy of $U'$. Because of \\cref{prop:geomreg}, this function has to vanish at all points $(r_1, r_2, t_2, \\hat{t}_2)$ such that $(r_1,r_2, t_2)$ and $(r_1, r_2, \\hat{t}_2)$ are both good geometric and neither is in the vanishing locus of $\\mathcal{L}_{p, \\gamma_S}(\\underline{\\pi} \\times \\underline{\\sigma}, \\underline{\\eta})$; this set is easily seen to be Zariski-dense in $U \\times U' \\times U'$. The same argument also shows that $\\mathsf{R}$ doesn't depend on $\\gamma_S$.\n\n Thus $\\mathsf{R}$ is an element of $\\mathcal{Q}(U)^\\times$, regular at all points $P \\in \\mathfrak{S}(\\underline{\\pi}, \\underline{\\sigma})$ and coinciding at each such point with the ratio $j_P \/ \\ES^2_{\\pi_P}$. So if we define $\\ES^2_{\\underline{\\pi}} = \\mathsf{R}^{-1} \\jmath$, then $\\ES^2_{\\underline{\\pi}}$ is regular at all points in $\\mathfrak{S}(\\underline{\\pi}, \\underline{\\sigma})$ and coincides at such points with $\\ES^2_{\\pi_P}$. By the preceding lemma, this interpolating property uniquely determines $\\ES^2_{\\underline{\\pi}}$, and is independent of $\\underline{\\sigma}$; and the reciprocity law holds by construction.\n \\end{proof}\n\n \\begin{remark}\n Note that there could, \\emph{a priori}, be points where $\\ES^2_{\\underline{\\pi}}$ is 0 or $\\infty$; or where it is a well-defined isomorphism but this isomorphism does not coincide with $\\ES_{\\pi_P}^2$.\n \\end{remark}\n\n \\subsection{Application to the Bloch--Kato conjecture}\n\n Let us now consider the following situation:\n \\begin{itemize}\n \\item $\\pi$ and $\\sigma$ are cohomological cuspidal automorphic representations of $\\GSp_4 \\times \\GL_2$, with $p$-stabilisations which are ordinary and $p$-regular, which are ``deformable'' in the above sense.\n\n \\item If $t_2 = r_1 - r_2 - 1$ (so that $t_1 = -1$), then we suppose that $L(\\Pi \\times \\Sigma, \\tfrac{1}{2}) \\ne 0$. (In all other cases the non-vanishing of $L(\\Pi \\times \\Sigma, 1+\\tfrac{t_1}{2})$ is automatic.)\n\n \\item The Galois representation $V = V_p(\\pi)^* \\otimes V_p(\\sigma)^*(-1-r_1)$ satisfies the ``big image'' conditions of \\cite[\\S 3.5]{mazurrubin04}.\n\n \\item None of the eight characters appearing as graded pieces of $V$ as a $\\Gal(\\overline{\\mathbf{Q}}_p \/ \\QQ_p)$-representation are congruent mod $p$ to the trivial character, or to the $p$-adic cyclotomic character (``$p$-distinction'').\n \\end{itemize}\n\n (Note that the ``big image'' hypothesis can only be satisfied if $\\chi_0\\chi_2 \\ne 1 \\bmod p$, but is frequently satisfied when this condition does hold; compare the discussion in \\S 11.1 of \\cite{KLZ17} in the Rankin--Selberg case.)\n\n\n \\begin{theorem}\n \\label{thm:BKconj}\n In the above setting, we have\n \\[ H^1_{\\mathrm{f}}(\\mathbf{Q}, V(\\pi)^* \\otimes V(\\sigma)^*(-1-r_1)) = 0,\\]\n as predicted by the Bloch--Kato conjecture.\n \\end{theorem}\n\n \\begin{proof}\n If $(r_1, r_2)$ is in the set $\\mathfrak{S}(\\underline{\\pi}, \\underline{\\sigma})$ defined above (or more generally in $\\mathfrak{S}(\\underline{\\pi}, \\underline{\\sigma}')$ for some possibly different Hida family $\\underline{\\sigma}'$), then the theorem of the previous section implies that we have an Euler system for $V(\\pi)^* \\otimes V(\\sigma)^*(-1-r_1)$ whose bottom class is non-zero. Hence we may apply the machinery of ``Euler systems with local conditions'' developed in \\cite[\\S 12]{KLZ17} to deduce the finiteness of the Selmer group.\n\n The exceptional case which we need to deal with is when the ``family'' Eichler--Shimura isomorphism degenerates at $(r_1, r_2)$. We expect that this never occurs, but we cannot yet rule it out. In this situation, we use a version of the ``leading term argument'' from \\cite{LZ20, LZ20-yoshida}). The construction of the $p$-adic $L$-function (and the proof of the reciprocity law) extend immediately to equivariant $p$-adic $L$-functions over $\\mathbf{Q}(\\zeta_m)$, for all $m$ coprime to $T$. If the Eichler--Shimura isomorphism degenerates at $(r_1, r_2)$, then not only the class $\\mathbf{z}_1(\\pi \\times \\sigma)$, but all the classes $\\mathbf{z}_m$, must satisfy the stronger local condition defined by $\\mathcal{F}^{(f)}$; and this forces all the classes to be zero, as in \\ and this forces all of the classes to be zero. So we may replace the whole Euler system by its first derivative (in some arbitrarily chosen direction in weight space) and rescale the Eichler--Shimura isomorphism accordingly. Proceeding inductively, we eventually obtain an Euler system with non-trivial bottom class, and the argument proceeds as before.\n\n (A slight complication here is that in the exceptional case, the Euler system we obtain for $V$ does not necessarily extend to classes over the $p$-cyclotomic tower satisfying the extra-strong local condition $\\mathcal{F}^{(f)}$, since our explicit reciprocity law does not ``see'' the cyclotomic variable. Hence we cannot use the arguments of \\cite[\\S 12]{KLZ17} to prove the crucial lemma that this local condition is preserved by the passage from Euler to Kolyvagin systems, as these arguments rely on the presence of the $p$-cyclotomic tower. This is the reason for imposing the rather stringent $p$-distinction hypothesis, which allows us to use the alternative, slightly more direct approach given in the appendix of \\cite{leiloefflerzerbes14b}, in which the cyclotomic extension is not needed.)\n \\end{proof}\n\n\n\n\\newlength{\\bibitemsep}\n\\setlength{\\bibitemsep}{0.75ex plus 0.05ex minus 0.05ex}\n\\newlength{\\bibparskip}\n\\setlength{\\bibparskip}{0pt}\n\\let\\oldthebibliography\\thebibliography\n\\renewcommand\\thebibliography[1]{%\n \\oldthebibliography{#1}%\n \\setlength{\\parskip}{\\bibparskip}%\n \\setlength{\\itemsep}{\\bibitemsep}%\n}\n\n\\newcommand{\\noopsort}[1]{}\n\n\\providecommand{\\bysame}{\\leavevmode\\hbox to3em{\\hrulefill}\\thinspace}\n\\providecommand{\\MR}[1]{}\n\\renewcommand{\\MR}[1]{%\n MR \\href{http:\/\/www.ams.org\/mathscinet-getitem?mr=#1}{#1}.\n}\n\\providecommand{\\href}[2]{#2}\n\\newcommand{\\articlehref}[2]{\\href{#1}{#2}}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nThe extensive study of Kummer surfaces is explained by their rich\ngeometry and their multiple roles in the theory of K3 surfaces and\nbeyond \\cite{H1,torelli, B3}.\n\nLet $A$ be an abelian surface and consider the involution which\nmaps $a$ to $-a$ for any $a$ in $A$. This involution has sixteen\nfixed points, namely the sixteen two-torsion points of $A$. The\nquotient surface has sixteen ordinary double points and its\nminimal resolution is a smooth K3 surface called the Kummer\nsurface associated to $A$ and denoted by $\\mathrm{Kum}(A)$. Nikulin\nproved that any K3 surface containing sixteen disjoint smooth\nrational curves is a Kummer surface \\cite{N2}.\n\nGiven a Kummer surface $\\mathrm{Kum}(A)$, there is a natural way of\nconstructing new Kummer surfaces from it. One takes the minimal\nmodel of the double cover of $\\mathrm{Kum}(A)$ branched along eight\ndisjoint smooth rational curves $C_1, \\dots, C_8,$ that are even\n(see section 2) and that are orthogonal in $\\mathrm{Pic(Kum}(A))$ to\neight other smooth rational curves. We obtain in this way a new\nKummer surface $\\mathrm{Kum}(B)$ together with a rational map\n$\\mathrm{Kum}(B) \\stackrel{\\tau}\\dashrightarrow \\mathrm{Kum}(A)$.\n\nIn the second section of the paper, we explain this construction\nin details and show that the abelian surface associated to the new\nKummer surface $\\mathrm{Kum}(B)$ is isogenous to $A$. In fact we prove\nthat the map $\\tau$ is induced by an isogeny of degree two on the\nassociated abelian surfaces.\n\nIn section 3, we describe the geometry of a generic jacobian\nKummer surface and explain its classical double plane model. We\nalso recall a theorem of Naruki \\cite{Naruki} giving explicit\ngenerators of the N\\'eron-Severi lattice of a generic jacobian\nKummer surface.\n\nIn section 4, we apply the construction of section 2 to the\ngeneric jacobian Kummer surface. We obtain in this way, fifteen\nnon isomorphic Kummer surfaces which are associated to\n$(1,2)$-polarized abelian surfaces.\n\nFinally in section 5, we show that the Kummer surfaces of section\n4 admit an elliptic fibration with twelve singular fibers of the\ntype $I_2$. We also prove that these Kummer surfaces are double\ncover of a week Del Pezzo surface (i.e. the blowup of $\\mathbb\nP^2$ at seven points) and that for each of our Kummer surfaces\nthere exists a decomposition of a very degenerate sextic $\\mathcal\nS$ (see figure \\ref{plane sextic}) into a quartic $Q$ and a conic\n$C$ for which we have the theorem\n\n\\begin{theorem}\\label{San11}\nThe rational double cover $\\mathrm{Kum}(B) \\stackrel{\\tau}\\dashrightarrow \\mathrm{Kum}(A)$ decomposes as\n$$\\xymatrix{\\mathrm{Kum}(B) \\ar@{-->}[d]^{\\tau}\\ar[r]^{\\varphi}&\nT \\ar@{->}[d]^{\\zeta}\\\\ \\mathrm{Kum}(A) \\ar@{->}[r]^{\\phi}& \\mathbb P^2}$$ where\n$\\phi$ is the canonical resolution of the double cover of $\\mathbb\nP^2$ branched along $\\mathcal S$. The maps $\\zeta$ and $\\varphi$\nare the canonical resolutions of the double covers branched along $Q$ and $\\zeta^*(C)$ respectively.\n\\end{theorem}\n\n\n\\section{Even Eight and Kummer surface}\n\nWe will now introduce the notion of an even eight and the double\ncover construction associated to it. By applying this construction\nto special even eights of a Kummer surface, we obtain new Kummer\nsurfaces.\n\\begin{definition}\\label{eveneight}\nLet $Y$ be a K3 surface, an \\textit{even eight} on $Y$ is a set of\neight disjoint smooth rational curves $C_1, \\dots, C_8,$ for which\n$C_1 + \\cdots + C_8 \\in 2S_Y.$ Here $S_Y$ denotes the\nN\\'eron-Severi group of $Y.$\n\\end{definition}\n\n\nIf $C_1, \\dots, C_8,$ is an even eight on a K3 surface $Y$, then\nthere is a double cover $Z \\stackrel{p}\\to Y$, branched on $C_1 +\n\\dots +C_8$. If $E_i$ denotes the inverse image of $C_i$, then\n$p^*(C_i)=2E_i$ and $E_i^2=-1$. Hence, we may blowdown the $E_i$'s\nto the surface $X$ and obtain the diagram $$\\xymatrix{Z\n\\ar@{->}[d]_{p }\\ar[r]^{\\epsilon} & X \\ar@{-->}^{2:1}[ld]\n\\\\Y & }$$\nIt turns out that the surface $X$ is again a K3 surface and the\ncovering involution $\\iota: X \\to X$ is symplectic with eight\nfixed points \\cite{N2}.\n\n\nSuppose now that the K3 surface $Y$ is a Kummer surface, we want\nto exhibit natural even eights lying on it. For this purpose, we\nrecall a central lemma of Nikulin.\n\\begin{lemma}\\cite{N2}\\label{nikulin}\nLet $Y$ be a Kummer surface and let $E_1, \\dots, E_{16} \\subset Y$\nbe sixteen smooth disjoint rational curves. Denote by $I=\\{ 1,\n\\dots, 16 \\}$ the set of indices for the curves $E_{i}$'s and by\n$Q = \\{ M \\subset I \\vert \\quad \\frac{1}{2} \\sum_{i\\in M}E_{i}\\in\nS_Y\\}$; then for every $M$ in $Q$, we have $\\# |M|=8 \\textrm{ or }\n16$ and there exists on $I$ a unique 4-dimensional affine geometry\nstructure over $\\mathbb F_{2}$, whose hyperplanes consist of the\nsubsets $M \\in Q$ containing eight elements.\n\\end{lemma}\n\nThe existence of such a 4-dimensional affine geometry implies that\n$I \\in Q $ or equivalently that $\\sum_{i=1}^{16}E_{i}\\in 2 S_Y.$\nWe can proceed exactly as for an even eight and take the double\ncover $V \\stackrel{p}\\to Y$ branched along $E_1+ \\dots + E_{16}$.\nAgain we blowdown the preimage of the $E_i$'s to a surface $A$ and\nobtain the diagram $$\\xymatrix{V \\ar@{->}[d]_{p }\\ar[r]^{\\epsilon}\n& A \\ar@{-->}^{\\pi_A }[ld]\n\\\\Y & }$$The difference with this diagram and the one above is\nthat now the surface $A$ is an abelian surface and that the map\n$\\pi_A$ realizes $Y$ as the Kummer surface associated to $A$. We\npoint out that by uniqueness, the affine geometry on $I$\ncorresponds to the one existing on $A_{2}$, the set of 2-torsion\npoints on $A$. \\cite{N2}.\n\nIt follows also from the lemma that there exist on $Y$ ($\\simeq\n\\mathrm{Kum}(A)$) thirty even eights, denoted by $M_1, \\cdots,\nM_{30}$, i.e. the thirty affine hyperplanes of $I$.\n\nLet $M \\in \\{ M_1, \\dots, M_{30} \\}$ be one of these even eights.\nWe can assume that $M$ consists of the curves $E_{1}, \\dots,\nE_{8}$. The curves $E_{9}, \\dots, E_{16}$ are then orthogonal to\n$M$, i.e. $$E_i \\cdot E_j =0 \\textrm{ if } 1 \\leq i \\leq 8\n\\textrm{ and } 9\\leq j \\leq 16.$$ If $X\n\\stackrel{\\tau}\\dashrightarrow \\mathrm{Kum}(A) $ is the double cover\nassociated to $M$, then the K3 surface $X$ contains again sixteen\ndisjoint smooth rational curves. Indeed since the curves $E_{9},\n\\dots, E_{16}$ do not intersect the branch locus of the double\ncover $p: Z \\to Y$, they split under $p$ and define sixteen\ndisjoint smooth rational curves on $Z$. These sixteen curves are\nthen isomorphically mapped by the blowdown $Z \\stackrel{\\epsilon}\n\\to X$ to sixteen curves on $X$. It follows that $X$ contains\nsixteen disjoint smooth rational curves and hence it is a Kummer\nsurface.\n\n\\begin{prop}\\label{prop}\nLet $M$ be an even eight on a Kummer surface $\\mathrm{Kum}(A)$ such as\nabove, then the K3 surface $X$ associated to $M$ is a Kummer\nsurface. Moreover there is an abelian surface $B$ associated to\n$X$ for which we have the commutative diagram\n$$\\xymatrix{B\\ar@{-->}[d]_{\\pi_B } \\ar[r]^{p} & A\n\\ar@{-->}[d]_{\\pi_A}\n\\\\X=\\mathrm{Kum}(B) \\ar@{-->}[r]^{\\tau}& \\mathrm{Kum}(A)}$$where $B\\stackrel{p}\\to\nA$ is an isogeny of degree two.\n\\end{prop}\n\n\\begin{proof}\n\\noindent Since we have already shown that $X$ is a Kummer\nsurface, we only have to prove that $B$ is degree two isogenous to\n$A$. Write the abelian surface $A$ as the complex torus $\\mathbb\nC^2 \/ \\Lambda$ and let $E_{9}, \\dots, E_{16}\\subset \\mathrm{Kum}(A)$\nbe the eight disjoint smooth rational curves orthogonal to $M$.\nThese curves also form an even eight and hence they correspond to\nan affine hyperplane $H$ in $A_2$. Up to translation we can fix\nthe origin on $A$ in $H$. Let $\\frac{[v]}{2}$ be the generator of\n$A_2\/H$, it defines a sublattice $\\Lambda' \\subset \\Lambda$.\nExplicitly we have that $\\Lambda'= \\mathbb Z h_1 \\oplus \\mathbb Z\nh_2 \\oplus \\mathbb Z h_3 \\oplus \\mathbb Z 2v$, where $H=\\langle\n\\frac{[h_1]}{2}, \\frac{[h_2]}{2}, \\frac{[h_3]}{2} \\rangle \\subset\nA_2$.\nThe canonical inclusion $\\Lambda' \\hookrightarrow \\Lambda$,\ninduces the following commutative diagram : $$\\xymatrix{\\mathbb\nC^2\/\\Lambda'\\ar@{-->}[d]_{\\pi'} \\ar[r]^{p} & \\mathbb C^2\/\\Lambda\n\\ar@{-->}[d]_{\\pi}\\\\\n \\mathrm {\\kum(\\mathbb C^2\/\\Lambda')} \\ar@{-->}[r]^{q}& \\mathrm {\\kum(\\mathbb C^2\/\\Lambda)}}$$\nwhere $p$ is an isogeny of degree two. The covering involution of\n$p$ is given by the translation by the 2-torsion point $[v]$ in\n$\\mathbb C^2\/\\Lambda'$.\nIt induces the symplectic involution on $\\mathrm {\\kum(\\mathbb\nC^2\/\\Lambda')}$ $$\\sigma: \\mathrm {\\kum(\\mathbb C^2\/\\Lambda')} \\to \\mathrm\n{\\kum(\\mathbb C^2\/\\Lambda')}$$which has exactly eight fixed points\n\\cite{N5}, namely\nthe projection of the sixteen points on $\\mathbb C^2\/\\Lambda'$ satisfying$$[z]+[v]=-[z] \\textrm{, or equivalently } 2[z]=[v].$$ \nThe isogeny $p$ maps the set $\\{ [z] \\in \\mathbb C^2\/\\Lambda'\n\\textrm { }| \\textrm { }2[z]=[v]\\}$ to $\\mathrm {A_2-H}$. In other\nwords, the affine hyperplane $A_2-H$ corresponds to the even\neight $M$ in $\\mathrm {Kum}(\\mathbb C^2\/\\Lambda)$. Hence the\nresolution of the rational map $q$ is exactly the double cover of\n$\\mathrm{Kum}(A)$ branched along $M$ and the abelian surface $\\mathbb\nC \/\\Lambda'$ is $B$.\n\n \n\\end{proof}\n\n\n\\section{Jacobian Kummer surface}\nIn this section we briefly expose the classical geometry of a\njacobian Kummer surface and its beautiful $16_6$-configuration. We\ndescribe its double plane model and give explicit generators for\nits N\\'eron-Severi lattice. This description follows a paper of N\naruki \\cite{Naruki}.\n\nA Kummer surface $\\kum(A)$ is said to be a jacobian Kummer surface\nif the surface $A$ is the jacobian of a curve $C$ of genus two.\nMoreover, it is a generic jacobian Kummer surface if its Picard\nrank is 17.\n\nRecall that the degree two map given by the linear system $|2C|$,\n$A \\stackrel{|2C|} \\to \\mathbb P^3$, factors through the\ninvolution $a \\stackrel{i} \\mapsto -a$, and hence defines an\nembedding $A\/\\{ 1, i\\} \\hookrightarrow \\mathbb P^3$. The image of\nthis map is a quartic $Y_0 \\subset \\mathbb P^3$ with sixteen\nnodes. Denote by $L_0$ the class of a hyperplane section of $Y_0$.\nProjecting $Y_0$ from a node defines a rational map $Y_0\n\\stackrel{2:1}\\dashrightarrow \\mathbb P^2$. We blowup the center\nof projection\n\n$$\\xymatrix{Y_1\\ar@{->}[d] \\ar[rd] & \\\\\n Y_0 \\ar@{-->}[r]& \\mathbb P^2}$$and we call $E_1\\subset Y_1$ the\n exceptional divisor and $L_1\\subset\n Y_1$ the pullback of a line in $\\mathbb P^2$.\n Finally we resolve the remaining fifteen\n singularities of $Y_1$ and obtain the Kummer surface\n $\\mathrm{Kum}(A)$ and a map of degree two\n$\\mathrm{Kum}(A) \\stackrel{\\phi} \\to \\mathbb P^2$. The map $\\phi$ is\ngiven by the linear system $|L-E_0|$, where $L$ and $E_0$ are the\npullback of $L_1$ and $E_1$ respectively.\n\nThe branch locus of the map $\\phi$ is a reducible\n plane sextic $\\mathcal S$, which is the union of six lines, $l_1, \\cdots, l_6$, all tangent to a conic $W$\n\n\\begin{figure}[h]\n$$\n \\begin{xy}\n <0cm,0cm>;<1.5cm,0cm>:\n (2,-.3)*++!D\\hbox{$l_1$},\n (1.1,0.25)*++!D\\hbox{$l_2$},\n (1.1,1.3)*++!D\\hbox{$l_3$},\n (2,1.8)*++!D\\hbox{$l_4$},\n (2.8,1.3)*++!D\\hbox{$l_5$},\n (2.9,0.2)*++!D\\hbox{$l_6$},\n (1.5,1)*++!D\\hbox{$W$},\n \n (.5,0.18);(3.5,0.18)**@{-},\n (0.5,2);(1.6,0)**@{-},\n (3.1,2);(2.6,0)**@{-},\n (0.7,.5);(2,2.5)**@{-},\n (0.5,1.82);(3.5,1.82)**@{-},\n (2,2.7);(3.45,0)**@{-},\n \n \n (2,1)*\\xycircle(.8,.8){},\n \\end{xy}\n $$\n\\caption{The sextic $\\mathcal S$\\label{plane sextic}}\n\\end{figure}\n\n\\noindent Let $p_{ij}=l_i \\cap l_j \\in \\mathbb P^2$, where $1\\leq\ni < j \\leq 6$. Index the ten $(3,3)$-partitions of the set $\\{ 1,\n2, \\dots ,6 \\}$, by the pair $(i,j)$ with $2 \\leq i < j \\leq 6$.\nEach pair $(i,j)$ defines a plane conic $l_{ij}$ passing through\nthe sixtuplet $p_{1i}, p_{1j}, p_{ij},p_{lm},p_{ln},p_{mn}$, where\n$\\{ l,m,n \\}$ is the complement of $\\{ 1,i,j\\}$ in $\\{ 1, 2, \\dots\n,6 \\}$ and where $l < m < n$. The map $\\phi$ factors as\n$$\\mathrm{Kum}(A) \\stackrel{\\tilde{\\phi}} \\longrightarrow\n\\tilde{\\mathbb P}^2\\stackrel{\\eta}\\longrightarrow \\mathbb\nP^2$$where $\\eta$ is the blowup of $\\mathbb P^2$ at the $p_{ij}$'s\nand where $\\tilde{\\phi}$ is the double cover of $\\tilde{\\mathbb\nP}^2$ branched along the strict transform of the plane sextic\n$\\mathcal S$ in $\\tilde{\\mathbb P}^2$. Denote by $E_{ij} \\subset\n\\mathrm{Kum}(A)$ the preimage of the exceptional curves of\n$\\tilde{\\mathbb P}^2$. The ramification of the map $\\tilde{\\phi}$\nconsists of the union of six disjoint smooth rational curves $C_0+\nC_{12} + C_{13}+ C_{14}+ C_{15}+ C_{16}$. The preimage of the ten\nplane conics $l_{ij}$ defines ten more smooth disjoint rational\ncurves $C_{ij}\\subset \\mathrm{Kum}(A), 2 \\leq i < j \\leq 6$. Finally,\nnote that $\\phi(E_0)=W$. The sixteen curves $E_0, E_{ij} \\quad 2\n\\leq i < j \\leq 6$ are called the \\textit{nodes} of $ \\mathrm{Kum}(A)$\nand the sixteen curves $C_0, C_{ij}$, $2 \\leq i < j \\leq 6$ are\ncalled the \\textit{tropes} of $ \\mathrm{Kum}(A)$. These two sets of\nsmooth rational curves satisfy a beautiful configuration called\nthe $16_6$-configuration, i.e. each node intersects exactly six\ntropes and vice versa.\n\n\\noindent It is now possible to fully describe the N\\'eron-Severi\nlattice $S_{ \\mathrm{Kum}(A)}$ of a general jacobian Kummer surface.\n\n\\begin{theorem}\\cite{Naruki} Let $\\mathrm{Kum}(A)$ be a generic jacobian Kummer surface. Its N\\'eron-Severi lattice $S_{ \\mathrm{Kum}(A)}$\nis generated by the classes of $E_{0}, E_{ij}$, $C_{0}, C_{ij}$\nand $L$, with the relations:\n\n\\begin{enumerate}\n\\item $C_{0}= \\frac{1}{2}(L- E_{0} -\n\\sum_{i=2}^{6} E_{1i}),$\n\\item $C_{1j}= \\frac{1}{2}(L - E_{0} - E_{1j}- \\cdots - E_{j-1j}- E_{jj+1}- \\cdots E_{j6}),$ where $2\\leq j \\leq 6,$\n\\item $C_{jk}= \\frac{1}{2}(L - E_{1j} -E_{1k}- E_{jk}- E_{lm}-E_{ln}-\nE_{mn})$ where $2\\leq i < j \\leq 6,$ and $\\{ l, m, n\\}$ are as\ndescribed above.\n\n\\end{enumerate}\n\n\\noindent The intersection pairing is given by:\n\n\\begin{enumerate}\n\n\\item the $E_{0}, E_{ij}$ are mutually orthogonal,\n\n\\item $\\langle L, L \\rangle =4, \\langle L, E_{0} \\rangle =\\langle L, E_{ij}\\rangle =0,$\n\n\\item $\\langle E_{0}, E_{0} \\rangle= \\langle E_{ij}, E_{ij} \\rangle=-2$,\n\n\\item the $C_{0}, C_{ij}$ are mutually orthogonal,\n\n\\item $\\langle L, C_{0} \\rangle= \\langle L, C_{ij} \\rangle=2$.\n\n\\end{enumerate}\n\n\n\\noindent The action on $S_{ \\mathrm{Kum}(A)}$ of the covering\ninvolution $\\alpha$ of the map $\\phi$ is given by:\n\n\\begin{tabular}{lrrlr}\n\n$\\alpha (C_{0})=C_{0}$ & &&\n\n$\\alpha (C_{1j})=C_{1j}$ & $2 \\leq j \\leq 6$\\\\\n\n$\\alpha (E_{ij})=E_{ij}$ & $1 \\leq i < j \\leq 6$ &&\n\n$\\alpha (L)=3 \\mathrm L - 4 E_{0}$ & \\\\\n\n$\\alpha (E_{0})=2 L - 3E_{0}$& &&\n\n$\\alpha (C_{ij})= C_{ij} + L - 2E_{0}$ & $2 \\leq i < j \\leq 6$.\\\\\n\n\\end{tabular}\n\n\\end{theorem}\n\\begin{remark}\nThe minimal resolution of the double cover of $\\mathbb P^2$\nbranched along the sextic $\\mathcal S$ in figure \\ref{plane\nsextic} is a Kummer surface (see \\cite{H1} for a proof).\\\\\n\\end{remark}\n\n\\section{(1,2)-polarized Kummer surfaces}\n\nIn this section, we apply the construction of section 2 to a\ngeneric jacobian Kummer surface. We identify all the even eights\nmade out of its nodes and study the associated Kummer surfaces.\nFirst we recall some standard facts about the polarization of\nabelian varieties.\n\nA polarization on a complex torus $\\mathbb C^g\/ \\Lambda$ is the\nclass of an ample line bundle $L$ in its the N\\'eron-Severi group.\nAs the latter group is equal, for abelian varieties, to the group\nof hermitian forms $H$ on $\\mathbb C^g$, satisfying\n$E=\\mathrm{Im}H(\\Lambda, \\Lambda) \\subset \\mathbb Z$, the ample line\nbundle $L$ corresponds to a positive definite hermitian forms\n$E_L$. According to the elementary divisor theorem, there exists a\nbasis $\\lambda_1, \\dots, \\lambda_g, \\mu_1, \\dots, \\mu_g$ of\n$\\Lambda$, with respect to which $E_L$ is given by the matrix\n$$\\left(\n\\begin{array}{cc}0 & D \\\\ -D & 0\\end{array}\\right) \\textrm{ with } D=\\left(\n\\begin{array}{cccc} d_1 & 0 & 0 & \\ldots \\\\ 0& d_2 & 0& \\ldots \\\\ \\vdots & 0& \\ddots& 0 \\\\ \\vdots & \\vdots & 0 &d_g \\end{array}\\right)$$\nwhere $d_i \\ge 0$ and $d_i | d_{i+1}$ for $i=1, \\dots, g-1.$ The\nvector $(d_1, d_2, \\dots, d_g)$ is the type of the line bundle\n$L$.\n\n\\begin{example}\\cite{BL}\\label{example}\n\n\\begin{enumerate}\n\\item If $J(C)$ is the Jacobian of a curve $C$ of genus two,\nthen\nthe line bundle associated to the divisor $C$ is a polarization of\ntype $(1,1)$.\n\n\\item If $L$ is a polarization of type $(d_1, \\dots, d_g)$ on a complex torus, then\n$\\chi(L)=d_1 \\cdot \\cdot \\cdot d_g$.\n\n\\item If $X_1 \\stackrel{p} \\to X_2$ is an isogney of degree 2 of\nabelian surfaces and $L$ is a polarization of type $(1,1)$ on\n$X_2$, then $\\chi(p^*(L))=2 \\chi(L)=2\\cdot 1$. Hence $p^*(L)$ is a\npolarization of type $(1,2)$ on $X_1$.\n\\end{enumerate}\n\\end{example}\n\n\n\n\\begin{prop}\\label{fifteen}\nLet $\\mathrm{Kum}(A)$ be a generic jacobian Kummer surface and let\n$E_0, E_{ij},$ $1\\leq i}[r]^{g^*}\\ar[d]^{i^*_1}&\nH^2(X_2, \\mathbb Z) \\ar@{->}[d]^{i^*_2}\\\\ H^2(X_1, \\mathbb Z)\n\\ar@{->}[r]^{g^*}& H^2(X_2, \\mathbb Z).}$$\n\n\n\\noindent \\textit{Proof of the claim:} Suppose that the above\ndiagram does not commute. Then the surface $X_1$ would admit two\ndistinct symplectic involutions, namely $i_1$ and $g \\circ\ni_2\\circ g^{-1}$. Moreover the quotient of $X_1$ by both of these\ninvolutions would be birational to the same Kummer surface $Y$. In\n\\cite{AM}, it is shown that the rational double cover of a Kummer\nsurface $\\mathrm{Kum}(A)$ is determined by an embedding $T_X\n\\hookrightarrow T_A$ preserving the Hodge decomposition of $T_X$\nand $T_A$. Since there is an unique embedding of $T_X$ into $T_A$\nwhich preserves the Hodge decomposition, it follows that\n$i_1=g^{-1} \\circ i_2\\circ g.$\n\nHence $i_2 \\circ g = g \\circ i_1$ and the isomorphism $g$ descends\nto an isomorphism on the quotients $$X_2\/ i_2 \\stackrel{g}\\to\nX_1\/i_1.$$ Since this isomorphism maps the eight singular points\nof $X_2\/ i_2$ to the eight singular points of $X_1\/ i_1$, it\nextends to an automorphism $Y \\stackrel{f}\\to Y$, for which\n$f^*(N_1)=N_2.$\n\nConversely, let $Y \\stackrel{f} \\to Y$ be an automorphism of $Y$\nfor which $ f^*(N_1)=N_2$. Denote by $Z_i \\stackrel{p_i}\\to Y$ the\ndouble cover of $Y$ branched along the even eight $N_i$ for\n$i=1,2$. Consider the fiber product $$\\xymatrix{Z_1 \\times_Y Y\n\\ar@{->}[r]^{q}\\ar[d]^{p}& Z_1 \\ar@{->}[d]^{p_1}\\\\Y\n\\ar@{->}[r]^{f}&Y.}$$ The map $Z_1 \\times_Y Y \\stackrel{p} \\to Y$\nis a double cover of $Y$ branched along the even set $N_2$ or\nequivalently $Z_1 \\times_Y Y=Z_2$. Similarly, by considering the\nfiber product $$\\xymatrix{Z_2 \\times_Y Y\n\\ar@{->}[r]^{h}\\ar[d]^{r}& Z_2 \\ar@{->}[d]^{p_2}\\\\Y\n\\ar@{->}[r]^{f}&Y,}$$ we see that $Z_2 \\times_Y Y =Z_1$. The maps\n$h$ and $q=h^{-1}$ define an isomorphism between $Z_1$ and $Z_2$\nwhich induces the required isomorphism between $X_1$ and $X_2$.\n\\end{proof}\n\nUsing the same notation as in the proposition \\ref{fifteen}, we\nprove the following theorem\n\n\\begin{prop}\nLet $\\Delta_{ij}$ and $\\Delta_{i'j'}$ be two even eights defined\nas in propostion \\ref{fifteen}. $$\\mathrm{Kum}(B_{ij}) \\simeq\n\\mathrm{Kum}(B_{i'j'}) \\Leftrightarrow \\{i,j\\}=\\{i',j'\\}.$$\n\\end{prop}\n\\begin{proof}\nIt is clear that if $\\{i,j\\}=\\{i',j'\\}$, then the corresponding\nKummer surfaces are equal. Thus we only have to prove the other\ndirection. Without loss of generality, we may assume that\n$\\Delta_{i'j'}=\\Delta_{12}$ and we suppose that there exists $f$\nan automorphism of $\\mathrm{Kum}(A)$ for which\n$f^*(\\Delta_{12})=\\Delta_{ij}$.\n\n\\noindent \\textit{Claim}: $$\\{\nf^*(E_{13}),f^*(E_{14}),f^*(E_{15}),f^*(E_{16}),f^*(E_{23}),f^*(E_{24}),f^*(E_{25}),f^*(E_{26})\\}=$$\n$$ \\{ E_{1i},\\cdots, \\hat{E_{ij}}, \\cdots ,E_{i6},E_{1j},\\cdots,\n\\hat{E_{ij}},\\cdots, E_{j6}\\}$$\n\n\\noindent\\textit{Proof of the claim}: Let $N$ be a Nikulin lattice\nand let $D \\in N$ be a divisor represented by a smooth rational\ncurve. Note that since $D$ is an effective reduced divisor and\n$N$ is negative definite, then $D^2=-2$. It is therefore\nsufficient to show that the only $-2$-classes in $N$ are the\n$c_i$'s and the claim will follow. We write $D$ as $D=\n\\sum_{i=1}^8 \\lambda_i c_i+\\epsilon d$ where $\\lambda_j \\in\n\\mathbb Z$ and $\\epsilon=0$ or $1$. If $\\epsilon=1$, then the\nequality $$D^2=-2\\sum_{i=1}^8 \\lambda_i^2-2\\sum_{i=1}^8\n\\lambda_i-4=-2$$implies that $\\sum_{i=1}^8\n\\lambda^2_i+\\lambda_i=-1.$ Since the latter equation has no\ninteger solution, we conclude that $\\epsilon=0$. Hence\n$$D^2=-2\\sum_{i=1}^8 \\lambda^2_i=-2$$or equivalently,\n$\\sum_{i=1}^8 \\lambda_i^2=1.$ Therefore there exists an unique\n$\\lambda_k$ for which $\\lambda_k=1$ and $\\lambda_i=0$ for $i\\ne\nk$.\n\nIn \\cite{Keum2}, it is proven that any automorphism of a jacobian\ngeneric Kummer surface induces $\\pm \\textrm{identity}$ on\n$D_{S_{\\mathrm{Kum}(A)}}$ where $D_{S_{\\mathrm{Kum}(A)}}$ is the\ndiscriminant group $S_{\\mathrm{Kum}(A)}^*\/S_{\\mathrm{Kum}(A)}$. We want to\napply this fact to the automorphism $f$. We consider the action of\n$f^*$ on the following two independent elements of\n$D_{S_{\\mathrm{Kum}(A)}}$ $$\\frac{1}{2}(E_{13}+E_{14}+E_{23}+E_{24})\n\\textrm{ and } \\frac{1}{2}(E_{12}+E_{23}+E_{15}+E_{35}).$$ From\nthe claim, we deduce that $$f^*(E_{13}+E_{14}+E_{23}+E_{24})=\nE_{i_1i}+E_{i_2i}+E_{j_1j}+E_{j_2j}$$for some classes\n$E_{i_1i},E_{i_2i},E_{j_1j},E_{j_2j} \\in \\Delta_{ij}$.\n\n\\noindent From the identity $f^*_{D_{S_{\\mathrm{Kum}(A)}}}=\\pm\n\\textrm{id}_{D_{S_{\\mathrm{Kum}(A)}}},$ we also deduce that\n$$f^*(\\frac{1}{2}(E_{13}+E_{14}+E_{23}+E_{24}))= \\pm\n\\frac{1}{2}(E_{13}+E_{14}+E_{23}+E_{24}).$$ Putting these two\ninformations together we find that $$E_{13}+E_{14}+E_{23}+E_{24}+\nE_{i_1i}+E_{i_2i}+E_{j_1j}+E_{j_2j} \\in 2S_Y.$$Since the only even\neights containing $E_{13}, E_{14},E_{23},E_{24}$ are $\\Delta_{12}$\nand $\\Delta_{34}$, we deduce that $\\Delta_{ij}=\\Delta_{34}$. We\nproceed similarly for $f^*(E_{12}+E_{23}+E_{13}+E_{35})$ and find\nthat $\\Delta_{ij}$ must be equal to $\\Delta_{25}$ which yields to\na contradiction.\n\\end{proof}\n\n\\begin{corollary}\nThe fifteen Kummer surfaces $\\mathrm{Kum}(B_{ij})$ are not isomorphic.\n\\end{corollary}\n\n\\section{Elliptic Fibration and weak del Pezzo surface}\n\nIn this section, we provide an alternate description of the Kummer\nsurfaces $\\mathrm{Kum}(B_{ij})$ as the double covers of a weak del\nPezzo surface. We relate this construction to the projective\ndouble plane model of the generic jacobian Kummer surface of\nsection 3. First we note the existence on $\\mathrm{Kum}(B_{ij})$ of an\nelliptic fibration that will be useful later. For simplicity, we\nwill always argue for the Kummer surface $\\mathrm{Kum}(B_{12})$.\n\n\\begin{prop}\\label{fibration}\nLet $\\mathrm{Kum}(B_{12})$ be the Kummer surface constructed in the\nproposition \\ref{fifteen}. The surface $\\mathrm{Kum}(B_{12})$ admits a\nWeierstrass elliptic fibration with exactly twelve singular fibers\nof the type $I_2$.\n\\end{prop}\n\\begin{proof}\nLet $\\mathrm{Kum}(A) \\stackrel{\\phi} \\to \\mathbb P^2$ be the double\nplane model of the generic jacobian Kummer surface introduced in\nsection 3. Consider the pencil of lines passing through the point\n$p_{12}$ in $\\mathbb P^2$. Its preimage in $\\mathrm{Kum}(A)$ defines\nan elliptic fibration, given by the divisor class\n$F=L-E_0-E_{12}.$ The divisors $$F_1=E_{15}+ E_{16}+2C_{0}+\nE_{13}+ E_{14}, \\quad \\textrm{and} \\quad F_2=E_{25}+\nE_{26}+2C_{12}+ E_{23}+ E_{24}$$ define two fibers of type $I^*_0$\nof this fibration. Moreover, the six divisors\n\\begin{center}\n$F_3= L-E_0-E_{12}-E_{45}+E_{45}$,\n\n$F_4= L-E_0-E_{12}-E_{46}+E_{46}$,\n\n$F_5= L-E_0-E_{12}-E_{35}+E_{35}$,\n\n$F_6= L-E_0-E_{12}-E_{36}+E_{36}$,\n\n$F_7= L-E_0-E_{12}-E_{34}+E_{34}$,\n\n$F_8= L-E_0-E_{12}-E_{56}+E_{56}$\n\\end{center}\n\\noindent define six $I_2$ fibers. Since the Euler characteristics\nof the $F_i$'s add up to 24, which is equal to the Euler\ncharacteristic of a K3 surface, we conclude by Shioda's formula\n\\cite{Shioda} that the $F_i$'s are the only singular fibers of the\nelliptic fibration defined by the linear system $|F|$. Note also\nthat the curves $C_{13}$, $C_{14}$, $C_{15}$ and $C_{16}$ are\nsections of this fibration.\n\nWe now analyze the induced fibration $\\tau^*F$ on\n$\\mathrm{Kum}(B_{12})$, where\n$\\mathrm{Kum}(B_{12})\\stackrel{\\tau}\\dashrightarrow \\mathrm{Kum}(A)$ is\nthe rational double cover defined by the even eight $\\Delta_{12}.$\nWe remark that the even eight $\\Delta_{12}$ satisfies\n$$\\Delta_{12} = F_1 +F_2 -2(C_0 +C_{12})$$ which mean that the\neight components of $\\Delta_{12}$ are exactly the eight components\nof the fibers $F_1$ and $F_{2}$ that appear with multiplicity one.\nHence $\\tau^*F_1$ and $\\tau^*F_2$ are just smooth elliptic curves.\nHowever the six fibers $F_3, \\dots, F_8$ split under the cover and\ndefine twelve $I_2$ fibers of the elliptic fibration on\n$\\mathrm{Kum}(B_{12})$ defined by $\\tau^*F$. Again a computation of\nEuler characteristics shows that these twelve $I_2$ fibers are the\nonly singular fibers of the linear system $|\\tau^*F|$. Also the\nsections $C_{13}$, $C_{14}$, $C_{15}$ and $C_{16}$ of $|F|$ pull\nback to sections of $\\tau^*F$, which is therefore a Weierstrass\nelliptic fibration.\n\n\\end{proof}\n\nWe now proceed to the realization of the surface\n$\\mathrm{Kum}(B_{12})$ as a double cover of a weak del Pezzo surface.\nWe decompose the sextic $\\mathcal S$ (see figure \\ref{plane\nsextic}) into the quartic $Q=l_3+l_4+l_5+l_6$ and the conic\n$C=l_1+l_2$.\n\\begin{theorem}\\label{San11}\nThe rational double cover associated to $\\Delta_{12}$,\n$\\mathrm{Kum}(B_{12}) \\stackrel{\\tau}\\dashrightarrow Y$ decomposes as\n$$\\xymatrix{\\mathrm{Kum}(B_{12}) \\ar@{-->}[d]^{\\tau}\\ar[r]^{\\varphi}&\nT \\ar@{->}[d]^{\\zeta}\\\\ Y \\ar@{->}[r]^{\\phi}& \\mathbb P^2}$$ where\n$\\phi$ is the canonical resolution of the double cover of $\\mathbb\nP^2$ branched along $\\mathcal S$. The maps $\\zeta$ and $\\varphi$\nare the canonical resolutions of the double covers branched along\n$Q$ and $\\zeta^*(C)$ respectively.\n\\end{theorem}\n\\begin{proof}\n\nLet $T_0 \\to \\mathbb P^2$ be the double cover of $\\mathbb P^2$\nramified over the reducible quartic $Q$. Its canonical resolution\ninduces the diagram $$\\xymatrix{T \\ar@{->}[d]\n\\ar[r]\\ar[rd]^{\\zeta}& T_0 \\ar@{->}[d]\\\\ \\tilde{\\mathbb P}^2\n\\ar@{->}[r]& \\mathbb P^2}$$ where $\\tilde{\\mathbb P}^2 \\to \\mathbb\nP^2$ is the the blowup of $\\mathbb P^2$ at the six singular points\nof $Q$. The surface $T$ is a non-minimal rational surface\ncontaining six disjoint smooth rational curves. Indeed by Hurwitz\nformula, the canonical divisor of $T$ is given by\n\n$$K_{T}= \\zeta^*(K_{\\mathbb\nP^2}+\\frac{1}{2}(l_3+l_4+l_5+l_6))=-\\zeta^*(H)$$where $H$ is a\nhyperplane section. Thus $K_T^2=2, H^2 =2$ and $P_2(T)=0$. Denote\nby $\\tilde{Q}$ the proper transform of $Q$ in $T$. Using the\nadditivity of the topological Euler characteristic and the Noether\nformula, we have that $$e(T)= e(T- \\tilde{Q}) + e(\\tilde{Q})=10\n\\Rightarrow \\mathcal X(\\mathcal O_T)=1 \\Rightarrow q(T)=0$$ By\nCastelnuovo's rationality criterion, $T$ is a rational surface. In\nfact, we show that $T$ is a weak del Pezzo surface of degree two,\ni.e. the blow up of $\\mathbb P^2$ at seven points with nef\ncanonical divisor. Indeed we successively blown down the preimages\nin $T$ of the four lines $l_3, l_4, l_5$ and $l_6$ as well as the\npreimages in $T$ of the three ``diagonals'' of the complete\nquadrangle formed by $l_3, l_4, l_5, l_6$. The surface obtained\nafter these seven blow down is a projective plane.\n\nConsider the following curves of $T$\n$$\\zeta^*(C)=\\zeta^*(l_1+l_2)= E_{1} + E_{2}, \\textrm{ where } E_1\n\\textrm{ and } E_2 \\textrm{ are smooth elliptic curves}$$ and\n$$\\zeta^*(W)=W_1+W_2 \\textrm{ where } W_1 \\textrm{ and } W_2\n\\textrm{ are smooth rational curves}$$ with the following\nintersection properties:\n$$E_i^2=2, \\quad W_i^2=0, \\quad E_1 \\cdot E_2=2, \\quad W_1 \\cdot\nW_2=4, \\quad W_i \\cdot E_j=2 \\quad \\textrm{ for}\\quad i\\neq j.$$\n(recall that $W$ is the plane conic tangent to the six lines $l_1,\n\\cdots,l_6$). The linear system $|E_1|$ defines an elliptic\nfibration on $T$ with six singular fibers of type $I_2$. Take the\ndouble cover branched along the two fibers $E_1 + E_2 \\in\n2\\mathrm{Pic(T)}$. It induces the canonical resolution commutative\ndiagram: $$\\xymatrix{X \\ar@{->}[d]\\ar[r] \\ar[dr]^{\\varphi}& X_0\n\\ar@{->}[d]\\\\ \\tilde{T} \\ar@{->}[r]& T}$$ where $\\tilde{T} \\to T$\nis the blowup of $T$ at the two singular points of $E_1+E_2$.\n\n\\textit{Claim:} X is a Kummer surface.\n\n\\textit{Proof of the Claim:} Clearly $\\mathcal\nK_{X}=\\varphi^*(\\zeta^*(-H)+ \\frac{1}{2}(E_1+E_2))=\\mathcal O_X$.\n\n\n1) The pullback by $\\varphi$ of the six exceptional curves on $T$ define\ntwelve disjoint smooth rational curves on $X$.\n\n2) The two exceptional curves of $X$ give two more rational curves\ndisjoint from 1).\n\n3) Let $\\varphi^*(W_1)=W'_1+W''_1$ and $\\varphi^*(W_2)=W'_2+W''_2$\nand let $\\sigma$ be the lift on $X$ of the covering involution of\n$\\zeta$, then $\\sigma(W'_1)=W'_2$ or $\\sigma(W'_1)=W''_2$. Without\nloss of generality, we can assume that $\\sigma(W'_1)=W'_2$ and\nhence get the following intersection numbers $$W'^2_i=W''^2_i=-2,\n\\quad W'_i\\cdot W''_i=2 \\quad \\textrm{for } i=1,2 $$ $$ W'_1\\cdot\nW'_2=W''_1\\cdot W''_2=4 \\quad \\textrm{and} \\quad W'_1\\cdot\nW''_2=W''_1\\cdot W'_2=0.$$ One easily checks that $W'_1$ and\n$W''_2$ do not intersect the fourteen curves from 1) and 2).\n\n\\noindent In particular, the $K3$ surface $X$ contains sixteen\ndisjoint smooth rational curves. Consequently $X$ is a Kummer\nsurface.\n Moreover, the surface $X$ contains an elliptic fibration with twelve $I_2$ fibers. It\n also admits two non symplectic involutions $\\theta$ and $\\sigma$ where $\\theta$ is the covering involution of\n the map $\\varphi$ and $\\sigma$ is the lift of the covering involution of $\\zeta$ on $T$ encountered earlier.\n The composition $\\iota = \\varphi \\circ \\sigma$ defines a symplectic involution on $X$ whose quotient is a $K3$\n surface admitting an elliptic fibration with singular fibers identical to the one defined by $F$ on $Y$ in the proposition\n \\ref{fibration}.\n\n In fact, we can now recover sixteen disjoint rational curves on the quotient and conclude that\n it is our original general Kummer surface $Y$ and that $X \\simeq \\mathrm{Kum}(B_{12})$.\n\n\\end{proof}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nLow power consumption is a key requirement for modern computational devices.\nNon-volatility is one of the core concepts to reduce \npower consumption for logics and memories\nin normally-off computing\n\\cite{ando_fed_2001, ando_spin-transfer_2014, nakada_normally-off_2017}. \nMagnetoresistive random access memory (MRAM) is a promising\nnon-volatile memory that stores information \nassociated with the direction of\nmagnetization in magnetic tunnel junctions (MTJs)\n\\cite{yuasa_giant_2004, parkin_giant_2004, djayaprawira_230_2005, yuasa_giant_2007,\nkishi_lower-current_2008, kitagawa_impact_2012,\napalkov_magnetoresistive_2016, sbiaa_recent_2017, cai_high_2017}. \nIn order to reduce\npower consumption of MRAM, \nseveral types of writing schemes have been\ndeveloped. The currently used writing scheme is based on the\nspin-transfer-torque (STT) switching phenomena, which were\nproposed by\nSlonczewski \\cite{slonczewski_conductance_1989,slonczewski_current-driven_1996}\nand independently by Berger \\cite{berger_emission_1996}. The write\nenergy of STT-MRAM is of the order of 100 fJ\/bit\n\\cite{kitagawa_impact_2012, cai_high_2017}, which is\nstill 2 orders of magnitude larger than that of static random-access\nmemory.\n\nDiscovery of the voltage control of magnetic anisotropy (VCMA) effect\n\\cite{weisheit_electric_2007, maruyama_large_2009, duan_surface_2008,\n nakamura_giant_2009, tsujikawa_finite_2009, nozaki_voltage-induced_2010,\n endo_electric-field_2010,\n nozaki_magnetization_2014, skowronski_perpendicular_2015, nozaki_large_2016,\n li_enhancement_2017}\npaved the way for further reduction of write energy in MRAM.\nThe mechanism of VCMA in an MgO-based MTJ is considered to be the\ncombination of the selective electron or hole doping into the\n$d$-electron orbitals and the induction of a magnetic dipole moment,\nwhich affect the electron spin through spin-orbit interaction\n\\cite{duan_surface_2008,nakamura_giant_2009,tsujikawa_finite_2009,miwa_voltage_2017}.\nThe MRAM which uses the VCMA effect to switch magnetization is called\nthe voltage controlled MRAM (VC-MRAM)\n\\cite{shiota_induction_2012, shiota_pulse_2012,\nkanai_electric_2012,shiota_evaluation_2016, grezes_ultra-low_2016, kanai_electric-field-induced_2016,\nshiota_reduction_2017, \nmatsumoto_voltage-induced_2018, yamamoto_thermally_2018, yamamoto_write-error_2019, \nmatsumoto_voltage-induced_2019, imamura_impact_2019, matsumoto_methods_2019}.\nThe writing procedure of a conventional VC-MRAM is as follows.\nThe perpendicularly magnetized MTJ is subjected to an in-plane\nexternal magnetic field ($H_{\\rm ext}$) as shown in Fig. \\ref{fig:schem}(a).\nThe magnetic anisotropy (MA) constant of the free layer can be controlled by\napplying voltage ($V$) as shown in Fig. \\ref{fig:schem}(b).\nHere, $K_{\\rm eff}$ is the effective perpendicular anisotropy constant\nwhere the demagnetization energy is subtracted from the perpendicular anisotropy constant.\nThroughout the paper, the superscript (0) indicates the quantities at $V = 0$.\nThe voltage pulse with critical amplitude $V_{\\rm c}$\neliminates the MA and induces the precession of the magnetization around\nthe external magnetic field. By turning off the voltage at one half\nperiod of precession, the magnetization switching completes.\n\n\\begin{figure}[t]\n\\centerline{\\includegraphics [width=\\columnwidth] {fig1.eps}}\n \\caption{\n \\label{fig:schem} \n (a) Magnetic tunnel junction with circular cylinder shape,\n and definitions of Cartesian coordinates $(x, y, z)$, polar angle\n ($\\theta$), and azimuthal angle ($\\phi$).\n The $x-$axis is parallel to the direction of \n the external in-plane magnetic field, ${\\bm H}_{\\rm ext}$.\n The unit vector ${\\bm m} = (m_{x}$, $m_{y}$, $m_{z})$ \n represents the direction of the magnetization in the free layer.\n The magnetization in the reference layer (ref.) is fixed to align in the\n positive $z-$direction.\n (b) The voltage ($V$) dependence of the effective perpendicular anisotropy constant ($K_{\\rm eff}$).\n The effective anisotropy constant at $V=0$ is represented by\n $K_{\\rm eff}^{(0)}$. It takes the value of\n $K_{\\rm eff} = K_{\\rm eff}^{\\rm p}$ at $V=V_{\\rm p}$.\n }\n\\end{figure}\n\nThe write energy of VC-MRAM is estimated from the Joule heating energy loss\nduring the pulse. Assuming that the voltage\npulse with amplitude $V$ and duration $t_{\\rm p}$ is applied to the MTJ with\nresistance $R$, the write energy is given by \n\\begin{equation}\n E_{\\rm J} = \\frac{V^{2}}{R}t_{\\rm p},\n\\end{equation}\nTo reduce the write energy, \nthe VC-MRAM should be designed to have large\nresistance and short pulse duration. The pulse duration is given by\na half period of precession as\n\\begin{align}\n \\label{eq:period_Hext}\n t_{\\rm p} = \\frac{\\pi (1+\\alpha^{2}) }{\\gamma H_{\\rm ext}},\n\\end{align}\nwhere $\\alpha$ is the Gilbert damping constant and $\\gamma$ is the\ngyromagnetic ratio. For example, $t_{\\rm p}$ = 0.18 ns for $\\alpha$ =\n0.1 and $\\mu_{0} H_{\\rm ext}$ = 100 mT, where $\\mu_{0}$ is the vacuum\npermeability. Recently, Grezes $et$ $al$. demonstrated \na very small write energy of 6 fJ\/bit for the VC-MRAM with $R=330$ k$\\Omega$ \nat $V$ = 1.96 V and $t_{\\rm p}$ = 0.52 ns\n\\cite{grezes_ultra-low_2016}. The similar results were also obtained\nindependently by Kanai $et$\n$al$. \\cite{kanai_electric-field-induced_2016}.\n\n\nIt is difficult to use MTJ with huge $R$ to further reduce\nwrite energy because the read time of the VC-MRAM increases \nwith increase of $R$. \nAdopting a scheme of decreasing \npulse duration\nby increasing external magnetic field \nshould also be avoided \nsince the application of a\nstrong in-plane magnetic field $H_{\\rm ext}$ deteriorates the thermal stability\nfactor defined as\n\\begin{align}\n \\label{eq:delta}\n \\Delta^{(0)}\n =\n \\frac{\n \\left(2 K_{\\rm eff}^{(0)}\n - \\mu_{0}M_{\\rm s} H_{\\rm ext}\n \\right)^{2}\n V_{\\rm F}\n }{4K_{\\rm eff}^{(0)} k_{\\rm B}T},\n\\end{align}\nwhere \n$k_{\\rm B}$ is the Boltzmann constant, $T$ is the temperature,\n$M_{\\rm s}$ is the saturation magnetization, \nand $V_{\\rm F}$ is the volume of the free layer.\n\n\n\n\nIn this paper, we propose another switching scheme which could reduce the pulse\nduration and therefore the write energy of a VC-MRAM. The main\ndifference between the conventional scheme and the proposed switching scheme is\nthe polarity of the voltage pulse. Application of the voltage pulse\nwith the polarity that is opposite to the conventional switching can\nenhance the magnetic anisotropy and induce the precession around the\naxis close to the easy axis. After turning off the voltage \nproximately at a\nhalf of precession period, the magnetization relaxes to the opposite\nequilibrium direction and the switching completes.\nWe perform numerical simulations and demonstrate that the pulse\nduration of the proposed switching scheme is as short as a few tens\nof pico seconds. We also evaluate the write error rate (WER) and\nshow that the WER is minimized if the pulse duration is about half the\nperiod of precession similar to the conventional switching scheme.\n\n\n\\section{Theoretical model}\nThe system we consider is schematically shown in Fig. \\ref{fig:schem}(a).\nThe macrospin model is employed to describe the magnetization dynamics.\nThe direction of the magnetization in the free layer is\nrepresented by the unit vector ${\\bm m} = (m_{x}$, $m_{y}$, $m_{z})\n= (\\sin \\theta \\cos \\phi$, $\\sin \\theta \\sin \\phi$, $\\cos \\theta$), \nwhere $\\theta$ and $\\phi$ are the polar and azimuthal angles.\nThe $x$ axis is parallel to the direction of external in-plane\nmagnetic field ${\\bm H}_{\\rm ext}$.\n\nThe energy density of the free layer is given by\n\\begin{align}\n \\label{eq:energy_density}\n {\\cal E} (m_{x}, m_{y}, m_{z})\n = \n -K_{\\rm eff} m_{z}^{2} - \\mu_{0} M_{\\rm s} H_{\\rm ext} m_{x},\n\\end{align}\nThe first term of Eq. \\eqref{eq:energy_density} is the sum of the\nshape, the bulk crystalline and the interfacial\nanisotropies. Owing to the VCMA effect, \n$K_{\\rm eff}$ can be controlled\nby application of $V$ as shown in Fig. \\ref{fig:schem}(b).\nHere $K_{\\rm eff}^{(0)}$ represents the effective anisotropy constant\nwithout the voltage application. We assume that $K_{\\rm eff}$\ndecreases with increase of $V$ and vanishes at $V=V_{\\rm c}$.\nApplying the voltage $V_{\\rm p} ( < 0)$ increases\n$K_{\\rm eff}$ to $K_{\\rm eff}^{\\rm p}$ and induces\nthe precessional motion of $\\bm{m}$ around the effective magnetic\nfield. The effective field is given by \n$H_{\\rm eff} = (H_{\\rm ext},$ $0,$ $H_{\\rm\n K} m_{z})$, where ${H}_{\\rm K} = 2 K_{\\rm eff}^{\\rm p} \/ ( \\mu_{0}\nM_{\\rm s} )$ is the anisotropy field.\n\n\nThe magnetization dynamics is simulated by solving \nthe following Landau-Lifshitz-Gilbert equation \\cite{brown_thermal_1963},\n\\begin{equation}\n \\label{eq:LLG}\n \\frac{{\\rm d} {\\bm m}}{{\\rm d}t}\n = -\\gamma_{0} {\\bm m}\\times\n \\left(\\bm{H}_{\\rm eff} + \\bm{h}\\right)\n +\\alpha\n \\bm{m}\\times\n \\frac{{\\rm d} {\\bm m}}{{\\rm d}t},\n\\end{equation}\nwhere $\\bm{h}$ represents the thermal agitation field satisfying the\nfollowing relations:\n\\begin{align}\n &\\langle h_{\\iota}(t)\\rangle=0\n \\\\\n & \\langle\nh_{\\iota}(t)h_{\\kappa}(t') \n\\rangle\n= \\frac{2\\alpha k_{\\rm B} T }{ \\gamma_{0} \\mu_{0} M_{\\rm s} V_{\\rm F} }\\delta_{\\iota\\kappa}\\delta(t-t').\n\\end{align}\nHere $\\iota,\\kappa=x,y,z$, \nand $\\langle X \\rangle$ denotes the statistical average of $X$.\n\nThroughout this paper, we assume that the external field is $\\mu_{0}\nH_{\\rm ext}$ = 100 mT and the saturation magnetization of the free\nlayer is $M_{\\rm s}= 1400$ kA\/m.\nAlso the radius of the junction area is assumed as $r=50$ nm and the\nthickness of the free layer, $t_{\\rm F}=1$ nm, and therefore the volume of\nthe free layer as $V_{\\rm F}=\\pi r^{2} t_{\\rm F}=7854$ nm$^{3}$. \nThe initial states are prepared by 10 ns relaxation from the\nequilibrium direction at $T=0$, that is \n$(\\theta^{(0)},$ $\\phi^{(0)}) = \\left( \\sin^{-1} \\left[ \\mu_{0} M_{\\rm s} H_{\\rm ext} \/ (2 K_{\\rm eff}^{(0)} ) \\right] \\right.,$ \n$ 0 \\Bigr)$\n\\cite{matsumoto_voltage-induced_2019}. \nThe write error rates are calculated from\n$10^{6}$ trials with 10 ns relaxation after the pulse.\n\n\n\n\\section{Results and discussions}\nFirst we show the difference between the mechanisms of the conventional\nvoltage controlled switching and the proposed switching that utilizes\nthe enhancement of the magnetic anisotropy.\nThis can be accomplished by analyzing the switching\ntrajectories at $T=0$.\nFigures \\ref{fig:t0}(a) and (b) show the shape of the voltage\npulse and the corresponding time dependence of the effective\nanisotropy constant for the conventional voltage controlled switching.\nThe induced switching dynamics of $\\bm{m}$ at $T=0$ is shown in\nFigs. \\ref{fig:t0}(c) together with the color map of the energy\ndensity of Eq. \\eqref{eq:energy_density} at $V=0$.\nThin black dotted curves represent energy contours. Thick black curves\nrepresent the energy contour crossing\n$\\bm{m}=(1,$ $0,$ $0)$. The initial direction of the magnetization is the\nequilibrium direction with $m_{z}>0$ indicated by the black circle,\nwhich we call as the ``up state''. \n\nIn Figs. \\ref{fig:t0}(a), (b) and (c),\napplication of the voltage pulse with $V_{\\rm c}$\neliminates the magnetic anisotropy and induce the precession of $\\bm{m}$\naround the external magnetic field as represented by the red\ncurve. After turning off the voltage at one-half period of precession, \nthe magnetization starts to relax from the point indicated by the orange\ncircle\nto the other equilibrium\ndirection with $m_{z}<0$, i. e. the ``down state'', indicated by the\nblack circle. \nNote that the black circle at $m_{z}<0$ is illustrated \nunder the green curve. \nThe switching is thus completed as represented by the green\ncurve.\n\n\\begin{figure}[H]\n \\centerline{\n \\includegraphics [width=0.8\\columnwidth] {fig2.eps}\n }\n \\caption{\n \\label{fig:t0} \n (a) The shape of the voltage pulse for the {\\em conventional switching\n scheme}. The amplitude including the polarity of the pulse and duration of the pulse are \n $V_{\\rm c}$ (positive value) and $t_{\\rm p}$, respectively.\n (b) The corresponding time dependence of the effective anisotropy\n constant $K_{\\rm eff}$. At $V=0$, it takes the value $K_{\\rm eff}^{\\rm (0)}$. \n During the pulse, $K_{\\rm eff} = 0$ because $V=V_{\\rm c}$.\n (c) The color map of the energy density at $V=0$ on the\n $\\phi-m_{z}$ plane. Thin black dotted curves represent energy\n contours. Thick black curves represent the energy contour crossing\n $\\bm{m}=(1,$ $0,$ $0)$.\n The trajectory of $\\bm{m}$ during and after the pulse\n are shown by the red and green curves, respectively. The direction\n of the trajectory is indicated by the triangle. The orange circle\n represents the direction of ${\\bm m}$ at the end of the pulse.\n We assume that $\\alpha$ = 0.1.\n (d) The shape of the voltage pulse for the {\\em proposed switching scheme}.\n The polarity is negative, i. e. $V_{\\rm p}<0$, to enhance $K_{\\rm eff}$.\n (e) The corresponding time dependence of the effective anisotropy\n constant. During the pulse, it is enhanced to $K_{\\rm eff}^{\\rm p}$.\n (f) The color map of the energy density at $V=0$ on the\n $\\phi-m_{z}$ plane. We assume that $K_{\\rm eff}^{\\rm p}$ = 400\n kJ\/m$^{3}$ and $\\alpha$ = 0.21. The symbols are the same as those\n in Panel (c).\n Please note that the left and right boundaries at $\\phi =\n \\pm \\pi$ represent the same direction of $\\bm{m}$.\n}\n\\end{figure}\n\nFigures \\ref{fig:t0}(d) and (e) show the shape of the voltage pulse\nand the corresponding time dependence of the effective anisotropy\nconstant for the switching using the enhanced $K_{\\rm eff}$.\nThe induced switching dynamics of $\\bm{m}$ at $T=0$ is shown in\nFig. \\ref{fig:t0}(f) together with the color map of the energy\ndensity at $V$ = 0. \nThe initial state is the up state indicated by the\nblack circle at $m_{z}>0$. Application of the voltage pulse with $V_{\\rm p} (< 0)$ \nenhances the effective anisotropy constant from $K_{\\rm eff}^{(0)}$ to\n$K_{\\rm eff}^{\\rm p}$ and induce the precession of $\\bm{m}$\naround the effective magnetic field as represented by the red curve.\nThe value of $K_{\\rm eff}^{\\rm p}$ is assumed to be 400\nkJ\/m$^{3}$, which gives the anisotropy field of $\\mu_{0} H_{\\rm K}$ = 570\nmT. The effective field is nearly parallel to the easy axis or the $z$\naxis because the directional cosine of the effective field relative to the\neasy axis is 0.98. \nThe voltage is turned off at about a half period\nof the precession, and the magnetization reaches the point, \n$\\phi \\simeq \\pi$ indicated by the orange circle. As will be shown later, \nthe write error rate (WER) is minimized if the pulse duration is set about\nhalf the period of precession. After turning off the pulse, the\nmagnetization relaxes to the down state and completes the switching as\nshown by the green curve. The proposed switching scheme does not\nreduce the thermal stability factor of Eq. \\eqref{eq:delta} \nbecause it\njust enhances $K_{\\rm eff}$ during the voltage pulse.\n\n\n\\begin{figure}[H]\n\\includegraphics [width=\\columnwidth] {fig3.eps}\n\\caption{\\label{fig:mm} \n (a) The Cartesian components of ${\\bm m} = (m_{x}$, $m_{y}$,\n $m_{z})$ of a typical switching trajectory \n are plotted as a function of time\n during the\n pulse at $T$ = 300 K.\n $K_{\\rm eff}^{\\rm p}$ = 400 kJ \/m$^{3}$ and $\\alpha$ = 0.21.\n The unit of the horizontal axis is ps. \n (b) The same as (a) after the pulse. The unit of the horizontal axis is ns. \n (c) The pulse duration dependence of the WER at $T$ = 300 K for\n $K_{\\rm eff}^{\\rm p}$ = 300 kJ \/m$^{3}$ and $\\alpha$ = 0.18.\n (d) The same as (c) for $K_{\\rm eff}^{\\rm p}$ = 400 kJ \/m$^{3}$ and\n $\\alpha$ = 0.21.\n }\n\\end{figure}\n\nNext we discuss the switching properties of the proposed switching\nscheme at $T$ = 300 K by analyzing the numerical simulations results.\nThe time evolution of the Cartesian components of ${\\bm m}$ for a\ntypical switching trajectory during the pulse are\nshown in Fig. \\ref{fig:mm}(a). The value of $K_{\\rm eff}^{(0)} = 100$ kJ\/m$^{3}$\nand $\\alpha$ are \nthe same as in Fig. \\ref{fig:t0}(f),\n$K_{\\rm eff}^{\\rm p}$ = 400 kJ\/m$^{3}$ and $\\alpha$ = 0.21.\nDuring the pulse duration, \n$m_{z}$ increases with the\nincrease of time because the effective anisotropy constant is\nenhanced. The shapes of $m_{x}$ and $m_{y}$ are very similar to\nthe cosine and sine functions, respectively, \nbecause \n$\\bm{m}$ precesses around the effective field which is almost parallel\nto the $z$ axis. \nFigure \\ref{fig:mm}(b) shows the time evolution of $m_{x}$, $m_{y}$\nand $m_{z}$ after the pulse. Please note that the horizontal axis is\nin unit of ns. $m_{z}$ monotonically decreases with the increase of time\nand the switching completes at around 0.4 ns.\n\n\nFigures \\ref{fig:mm}(c) and (d) show the pulse duration, $t_{\\rm p}$,\ndependence of the write error rate (WER) for different\nvalues of $K_{\\rm eff}^{\\rm p}$ and $\\alpha$.\nThe parameters are $K_{\\rm eff}^{\\rm p}$ = 300 kJ \/m$^{3}$ and\n$\\alpha$ = 0.18 for Fig. \\ref{fig:mm}(c), \nand $K_{\\rm eff}^{\\rm p}$ =\n400 kJ \/m$^{3}$ and $\\alpha$ = 0.21 for Fig. \\ref{fig:mm}(d).\nIn Fig. \\ref{fig:mm}(c), the WER takes a minimum value of $7.6 \\times\n10^{-3}$ at $t_{\\rm p}$ = 46 ps.\nIn Fig. \\ref{fig:mm}(d), the WER takes a minimum value of $3.2 \\times\n10^{-3}$ at $t_{\\rm p}$ = 36 ps. These optimal values of $t_{\\rm p}$ at which\nthe WER is minimized are almost the same as one half period of precession around\n$H_{\\rm eff}$.\n\n\n\n\\section{conclusion}\nIn summary, we propose a low power switching scheme of magnetization using\nenhanced magnetic anisotropy by applying a short voltage pulse.\nThe proposed switching scheme can reduce the pulse duration and\ntherefore the write energy substantially without deteriorating \nthermal stability.\nWe perform numerical simulations and show that the pulse duration of\nthe proposed switching scheme is as short as a few\ntens of pico seconds. We also calculated the pulse duration\ndependence of the WER, \nand showed that the optimal values of $t_{\\rm\n p}$ at which the WER is minimized are nearly half the period of\nprecession around the effective field.\n\n\\acknowledgements\nThis work was partly supported by JSPS KAKENHI Grant No. JP19K05259\nand 19H01108.\n\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzallf b/data_all_eng_slimpj/shuffled/split2/finalzzallf new file mode 100644 index 0000000000000000000000000000000000000000..695ba7cf3125b8621a6d4d886bd9a889a067fe0a --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzallf @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\nNetwork models are nowadays ubiquitous in the natural, information, social,\nand engineering sciences. The last 15 years or so have seen the emergence of the\nvast, multidisciplinary field of Network Science, with contributions\nfrom a wide array of researchers including physicists, mathematicians, \ncomputer scientists, engineers, biologists, and social scientists \n\\cite{Linked,EstradaBook,NewmanBook}. Applications of Network\nScience range from biology to public health, from social network analysis to \nhomeland security, from economics to the humanities, from marketing\nto information retrieval. Network analysis is\nalso an essential ingredient in the design of information, communication,\nand transportation networks, as well as in energy-related disciplines\nsuch as power grid maintenance, control, and optimization \\cite{Pinar2010}.\nGraph theory and linear algebra provide abstractions and quantitative \ntools that can be employed in the analysis and design of large and\ncomplex network models.\n\nReal-world networks are characterized by structural properties \nthat make them very different from both regular graphs on one hand, and \ncompletely random graphs on the other. Real networks frequently exhibit\na highly skewed degree distribution (often following a power law), small\ndiameter, high clustering coefficient (the two last properties together are\noften referred to as the {\\em small world} property), the presence of\nmotifs, communities, and other signatures of complexity. \n\nSome of the basic questions in network analysis concern node and edge centrality, \ncommunity detection, communicability, and diffusion \n\\cite{Brandes,EstradaBook,NewmanBook}. Related to these are the\nimportant notions of network robustness (or its opposite, vulnerability) and \nconnectivity \\cite{Cohen}. These latter properties refer to the degree of resiliency\ndisplayed by the network in the face of random accidental failures or \ndeliberate, targeted attacks, which can be modeled in terms of edge or node\nremoval. Generally speaking, it is desirable to design networks that are at\nthe same time highly sparse (in order to reduce costs) and highly connected,\nmeaning that disconnecting or disrupting the network would require the removal of a large\nnumber of edges. Such networks should not contain bottlenecks, and they\nshould allow for the rapid exchange of communication between nodes.\nExpander graphs \\cite{E06b,HLW06} are an important class of graphs \nwith such properties. \n\n\nIn this paper we describe some techniques that can be brought to bear on the\nproblems described above and related questions.\nOur approach is based on the notion of {\\em total communicability} of a\nnetwork, introduced in \\cite{Benzi2013} on the basis of earlier work\nby Estrada and Hatano \\cite{EH08,EHB12}. Total communicability, defined\nas the (normalized) sum of the entries in the exponential of the adjacency\nmatrix of the network, provides a global measure of how well the nodes\nin a graph can exchange information. \nCommunicability is based on the number and length\nof graph walks connecting pairs of nodes in the network. Pairs of nodes $(i,j)$\nwith high communicability correspond to large entries $[e^{A}]_{ij}$ in the\nmatrix exponential of $A$, the adjacency matrix of the network.\n\nTotal network communicability can also be used to measure the \nconnectivity of the network as a whole. For instance, given two alternative\nnetwork designs (with a similar ``budget\" in terms of number\nof candidate edges), one can compare the two designs by computing the\nrespective total communicabilities and pick the network with the highest one,\nassuming that a well-connected network with high node communicability is\nthe desired goal. \nIt is important to stress that\nthe total communicability of a network can be efficiently computed or estimated\neven for large networks using Lanczos or Arnoldi based algorithms without\nhaving to compute any individual entry of $e^A$ (only the ability to perform\nmatrix-vector products with $A$ is required).\n\n\nIn this paper we consider three different problems. \nLet $G=(V,E)$ be a connected, undirected and sparse graph. \nThe {\\it downdating problem} consists of selecting an edge $(i,j)$ to \nbe removed from the network so as to minimize the decrease in its \ntotal communicability while preserving its connectedness.\n\nThe goal when tackling the {\\it updating problem}, on the other hand, is to select a pair of \nnodes $i\\neq j$ such that $(i,j)\\not\\in E$ in such a way that the increase in \nthe total communicability of the network is maximized. \n\nFinally, the {\\it rewiring problem} has the same goal as the updating problem, but it requires \nthe selection of two modifications which constitute the downdate-then-update step to be performed.\n\nThe importance of the first two problems for network analysis and\ndesign is obvious.\nWe note that an efficient solution to the second problem would also \nsuggest how to proceed if the goal was to identify existing edges whose\nremoval would {\\em maximize} the decrease in communicability, which could\nbe useful, e.g., in planning anti-terrorism operations or public health policies \n(see, e.g., \\cite{Tong, VM2011}).\nThe third problem is motivated by the observation that\nfor transportation networks (e.g., flight routes) it is sometimes \ndesirable to redirect edges in order to improve the performance \n(i.e., increase the number of travellers) without increasing too much \nthe costs. \nHence, in such cases, one wants to eliminate a route used only by a few \ntravellers and to add a route that may be used by a lot of people.\n\nThe above problems may arise not only in the design of infrastructural networks\n(such as telecommunication or transportation networks), but also in other\ncontexts. For instance, in social networks the addition of a friendship\/collaborative\ntie may change dramatically the structure of the network, leading\nto a more cohesive\ngroup, and hence preventing the splitting of the community into smaller\nsubgroups.\n \n\n\nThe work is organized as follows. \nSection \\ref{sec:background} contains some basic facts from linear \nalgebra and graph theory, and introduces the modifications of the adjacency \nmatrix we will perform. \n In this section we also provide further justification\nfor the use of the total network communicability as the objective\nfunction.\nIn section \\ref{sec:bounds} we describe bounds for the \ntotal communicability \nvia the Gauss--Radau quadrature rule and we show how these bounds \nchange when a rank-two modification of the adjacency matrix is performed.\nSection \\ref{sec:modification} is devoted to the introduction of the \nmethods to controllably modify the graph in order to adjust the value of its \ntotal communicability.\nNumerical studies to assess the effectiveness and performance of the \ntechniques introduced are provided in section \\ref{sec:test_tc} for \nboth synthetic and \nreal-world networks.\nIn section \\ref{sec:nc+gap} we discuss the evolution of \na popular measure of network connectivity, known as\nthe {\\em free energy} (or {\\em natural connectivity}), \nwhen the same modifications are performed. \nThis section provides further evidence that motivates the use of the total \ncommunicability as a measure of connectivity. \nFinally, in section \\ref{sec:conclusions} we draw conclusions and we describe \nfuture directions.\n\n\n\n\\section{Background and definitions}\n\\label{sec:background}\nIn this section we provide some basic\ndefinitions, notations, and properties associated with graphs.\n\nA {\\itshape graph} or {\\itshape network}\n$G=(V,E)$ is defined by a set of $n$ nodes (vertices) \n$V$ and a set of $m$ edges $E=\\{(i,j)|i,j\\in V\\}$ between the nodes. \nAn edge is said to be {\\itshape incident} to a vertex $i$ if there exists \na node $j\\neq i$ such that either $(i,j)\\in E$ or $(j,i)\\in E$.\nThe {\\itshape degree} of a vertex, denoted by $d_i$, is the number of \nedges incident to $i$ in $G$. \nThe graph is said to be {\\itshape undirected} if the edges are formed by \nunordered pairs of vertices. \nA {\\itshape walk} of length $k$ in $G$ is a set of nodes $i_1, i_2,\\ldots,i_k, \ni_{k+1}$ such that for all $1\\leq l\\leq k$, $(i_l,i_{l+1})\\in E$.\nA {\\itshape closed walk} is a walk for which $i_1=i_{k+1}$. \nA {\\itshape path} is a walk with no repeated nodes. \nA graph is {\\itshape connected} if there is a path connecting every pair of nodes.\nA graph with unweighted edges, no self-loops (edges from a node to itself), \nand no multiple edges is said to be {\\itshape simple}. \nThroughout this work, we will consider undirected, simple, and connected networks.\n\nEvery graph can be represented as a matrix \n$A=\\left(a_{ij}\\right)\\in\\mathbb{R}^{n\\times n}$, called the \n{\\itshape adjacency matrix} of the graph. \nThe entries of the adjacency matrix of an unweighted graph $G=(V,E)$ are \n\\begin{equation*}\na_{ij}=\\left\\{\n\\begin{array}{ll}\n1 & \\mbox{if } (i,j)\\in E\\\\\n0 & \\mbox{otherwise}\n\\end{array}\n\\right.\\qquad \\forall i,j\\in V.\n\\end{equation*}\nIf the network is simple, the diagonal elements of the adjacency matrix \nare all equal to zero.\nIn the special case of an undirected network, the associated adjacency matrix \nis symmetric, and thus its eigenvalues are real.\n\nWe label the eigenvalues in non-increasing order: \n$\\lambda_1\\geq\\lambda_2\\geq\\cdots \\geq \\lambda_n$.\nSince $A$ is a real-valued, symmetric matrix, we can decompose $A$ \ninto $A=Q\\Lambda Q^T$ where $\\Lambda$ is a diagonal matrix containing the \neigenvalues of $A$ and $Q=[\\mathbf{q}_1,\\ldots,\\mathbf{q}_n]$ is orthogonal, \nwhere $\\mathbf{q}_i$ is an eigenvector associated with $\\lambda_i$.\nMoreover, if $G$ is connected, $A$ is irreducible and from the \nPerron--Frobenius Theorem \\cite[Chapter 8]{Meyer00} we deduce that $\\lambda_1>\\lambda_2$ \nand that the leading eigenvector $\\mathbf{q}_1$, \nsometimes referred to as the {\\itshape Perron vector}, \ncan be chosen such that its components $q_1(i)$ are positive \nfor all $i\\in V$.\n\nWe can now introduce the basic operations which will be performed \non the adjacency matrix $A$ associated with the network $G=(V,E)$.\nWe define the {\\itshape downdating} of the edge $(i,j)\\in E$ as the \nremoval of this edge from the network.\nThe resulting graph $\\widehat{G}=(V,\\widehat{E})$, which may be disconnected, \nhas adjacency matrix \n\\begin{equation*}\n\\widehat{A}=A-UW^T, \\qquad U=[\\mathbf{e}_i,\\mathbf{e}_j],\n\\quad W=[\\mathbf{e}_j,\\mathbf{e}_i],\n\\end{equation*}\nwhere here and in the rest of this work the vectors \n$\\mathbf{e}_i$, $\\mathbf{e}_j$ represent\nthe $i$th and $j$th vectors of the standard basis of $\\mathbb{R}^n$, \nrespectively.\n\nSimilarly, let $(i,j)\\in\\overline{E}$ be an element in the complement of $E$. \nWe will call this element a {\\itshape virtual edge} for the graph $G$. \nWe can construct a new graph $\\tilde{G}=(V,\\tilde{E})$ obtained from $G$ \nby adding the virtual edge $(i,j)$ to the graph.\nThis procedure will be referred to as the {\\itshape updating} of the \nvirtual edge $(i,j)$.\nThe adjacency matrix of the resulting graph is\n\\begin{equation*}\n\\tilde{A}=A+UW^T, \\qquad U=[\\mathbf{e}_i,\\mathbf{e}_j],\n\\quad W=[\\mathbf{e}_j,\\mathbf{e}_i].\n\\end{equation*}\nHence, these two operations can both be described as \nrank-two modifications of the adjacency matrix of the original graph.\n\nThe operation of downdating an edge and successively updating a virtual \nedge will be referred to as {\\itshape rewiring}. \n\\begin{rem}\n{\\rm These operations are all performed in a symmetric fashion, since in this paper we \nconsider exclusively undirected networks.}\n\\end{rem}\n\n\\subsection{Centrality and total communicability}\n\\label{subsec:centrality}\nOne of the main goals when analyzing a network is to identify the\nmost influential nodes in the network. \nOver the years, various measures of the importance, or centrality, \nof nodes have been developed \\cite{Brandes,EstradaBook,NewmanBook}. \nIn particular the {\\itshape (exponential) subgraph centrality} \nof a node $i$ (see \\cite{Estrada2005}) is defined as the $i$th diagonal \nelement of the matrix exponential \\cite{Higham2008}:\n$$e^A=I+A+\\frac{A^2}{2!}+\\ldots=\\sum_{k=0}^\\infty\\frac{A^k}{k!},$$\nwhere $I$ is the $n\\times n$ identity matrix.\nAs it is well known in graph theory, given an adjacency matrix $A$ \nof an unweighted network and $k\\in\\mathbb{N}$, \nthe element $\\left(A^k\\right)_{ij}$ counts the total number of walks \nof length $k$ starting from node $i$ and ending at node $j$.\nTherefore, the subgraph centrality of node $i$ counts the total number \nof closed walks centered at node $i$, weighting walks of length $k$ by a factor \n$\\frac{1}{k!}$, hence giving more importance to shorter walks. \nThe subgraph centrality then accounts for the returnability of the \ninformation to the node which was the source of this same information. \nLikewise, the off-diagonal entries of the adjacency matrix \n$\\left(e^A\\right)_{ij}$ ({\\itshape subgraph communicability} of nodes $i$ and $j$) \naccount for the ability of nodes $i$ and $j$ to exchange information \n\\cite{EH08,EHB12}.\n\nStarting from these observations and with the aim of reducing the cost \nof the computation of the rankings, in \\cite{Benzi2013} it was suggested to use\nas a centrality measure \nthe \\emph{total communicability of a node} $i$, defined as\nthe $i$th entry of the vector\n$e^A\\mathbf{1}$, where $\\mathbf{1}$ denotes the vector of all ones:\n\\begin{equation}\\label{tnc}\nTC(i):= [e^A\\mathbf{1}]_i = \\sum_{j=1}^n \\left[e^A\\right]_{ij}.\n\\end{equation}\nThis measure of centrality is given by a weighted sum of walks from\nevery node in the network (including node $i$ itself), and thus quantifies\nboth the ability of a node to\nspread information across the network and the returnability of the information to \nthe node itself.\n\nThe value resulting from summing these quantities over all the nodes\ncan be interpreted as a global measure of how effectively the \ncommunication takes place across the whole network.\nThis index is called {\\itshape total (network) communicability} \n\\cite{Benzi2013} and can be written as\n\\begin{equation}\\label{tc_spec}\nTC(A):=\\mathbf{1}^Te^A\\mathbf{1}=\\sum_{i=1}^n\\sum_{j=1}^n(e^A)_{ij} = \n\\sum_{k=1}^n e^{\\lambda_k}({\\bf q}_k^T{\\bf 1})^2.\n\\end{equation} \nThis value can be efficiently computed, e.g., by means of a \nKrylov method as implemented in S.~G\\\"uttel's Matlab toolbox \\texttt{funm\\_kryl} \nsee \\cite{Krylov1,Guettel} or by Lanczos-based techniques \nas discussed below. In the toolbox \\cite{Guettel}\nan efficient algorithm for evaluating $f(A)\\mathbf{v}$ is implemented; \nwith this method the vector $e^A\\mathbf{1}$ can be constructed in \nroughly $O(n)$ operations (note that the prefactor can vary\nfor different types of networks) and the total communicability is easily derived.\n\nAs it is clear from its definition, the value of $TC(A)$ may be very large. \nSeveral normalizations have been proposed; the simplest is the normalization \nby the number of nodes $n$ (see \\cite{Benzi2013}), which we \nwill use throughout the paper.\nIt is easy to prove that the normalized \ntotal communicability satisfies\n\\begin{equation}\\label{coarse_bounds}\n\\frac{1}{n}\\sum_{i=1}^n\\left(e^A\\right)_{ii}\\leq \\frac{TC(A)}{n}\\leq e^{\\lambda_1},\n\\end{equation}\nwhere the lower bound is attained by the graph with $n$ nodes and no \nedges and the upper bound is attained by the complete graph with $n$ nodes. \n\n\\begin{rem}\\label{rem:lambda1}\n{\\rm The last equality in equation \\eqref{tc_spec} shows that \nthe main contribution to the value of $TC(A)$ is likely to come from \nthe term $e^{\\lambda_1}\\|{\\bf q}_1\\|_1^2$.} \n\\end{rem}\n\n\n\\subsection{Rationale for targeting the total communicability} \\label{why_TM}\nAs already mentioned, the total communicability provides a good measure\nof how efficiently information (in the broad sense \nof the term) is diffused across the network. Typically, very\nhigh values of $TC(A)$ are observed\nfor highly optimized infrastructure networks \n(such as airline routes or computer networks) and for highly cohesive social and\ninformation networks (like certain type of collaboration networks). \nConversely, the total network communicability is relatively low for spatially\nextended, grid-like networks (such as many road networks) or for \nnetworks that consist of two or more communities with poor communication\nbetween them (such as the Zachary network).\\footnote{ Numerical\nvalues of the normalized total network communicability for a broad\ncollection of networks are reported in\nthe experimental sections of this paper, in the Supplementary Material,\nand in \\cite{Benzi2013}.}\nAs a further example,\nreduced values of the communicability between different brain regions have\nbeen detected in stroke patients compared to healthy individuals \n\\cite{CH09}.\nWe refer to \\cite{EHB12} for an extensive survey on communicability,\nincluding applications for which it has been found to be useful.\n\nAnother reason in support of the use of the total communicability as\nan objective function is that it is closely related to the {\\em natural\nconnectivity} (or {\\em free energy}) of the network, while being\ndramatically easier to compute; see section \\ref{sec:nc+gap} below. \nSparse networks with high\nvalues of $TC(A)$ are very well connected and thus less likely to \nbe disrupted by either random failures or targeted attacks leading\nto the loss of edges. This justifies trying to design sparse networks with\nhigh values of the total communicability.\n\nAn important observation is that the total network communicability $TC(A)$\ncan be interpreted in at least two different ways. Since it is given by the\nsum of all the pairwise communicabilities $C(i,j)=[e^A]_{ij}$, it is a\nglobal measure of the ability of the network to diffuse information.\nHowever, recalling the definition (\\ref{tnc}) of total node communicability,\nthe normalized total communicability can also be seen as ``the average\ntotal communicability\" of the nodes in the network: \n$$\\frac{TC(A)}{n} = \\frac{1}{n}\\sum_{i=1}^n TC(i).$$\nSince the total node\ncommunicability is a centrality measure \\cite{Benzi2013}, our goal \ncan then be rephrased as the problem of constructing sparse networks\nhaving high average node centrality, where the node centrality is given\nby total node communicability. Since this is merely one of a large number\nof centrality measures proposed in the literature, it is a legitimate \nquestion to ask why the total node communicability should be used instead of\na different centrality index. In other words, given any node\ncentrality function $f:V \\longrightarrow \\mathbb R_+$, we could consider \ninstead the problem of, say, adding a prescribed number of edges\nso as to maximize the increase in the global average centrality\n$$\\bar f = \\frac{1}{n}\\sum_{i=1}^n f(i).$$\n\nAs it turns out, most other centrality indices are either computationally\ntoo expensive to work with (at least for large networks), or lead to objective\nfunctions which do not make much sense. The following is a brief discussion\nof some of the most popular centrality indices used in the field of\nnetwork science.\n\n\\vspace{0.1in}\n\n\\begin{enumerate}\n\\item {\\bf Degree:} Consider first the simplest centrality index, the degree. \nObviously, adding $K$ edges according to {\\em any} criteria will\nproduce exacty the same variation in the average degree of a network.\nHence, one may as well add edges at random. Doing so, however,\ncannot be expected to be greatly beneficial if the goal is to \nimprove the robustness or efficiency of the network.\n\\item {\\bf Eigenvector centrality:} Let ${\\bf q}_1$ be the principal\neigenvector of $A$, normalized so that $\\|{\\bf q}_1\\|_2 = 1$. The eigenvector\ncentrality of node $i\\in V$ is the $i$th component of ${\\bf q}_1$, denoted\nby $q_1(i)$. It is straightforward to see that the problem of maximizing\nthe average eigenvector centrality\n$$\\frac{q_1(1) + q_1(2) + \\cdots + q_1(n)}{n}$$\nsubject to the constraint $\\|{\\bf q}_1\\|_2 = 1$ has as its only solution\n$$q_1(1) = q_1(2) = \\cdots = q_1(n) = \\frac{1}{\\sqrt n}.$$\nThis implies that $A$ has constant row sums or, in other words, that the\ngraph is regular --- every node in $G$ has the same degree. Hence, any\nheuristic aimed at maximizing the average eigenvector centrality will\nresult in graphs that are close to being regular. However, regular \ngraph topologies are not, {\\em per se}, endowed with any especially good \nproperties when it comes to diffusing information or being robust: think\nof a cycle graph, for example. Regular graphs {\\em can} be very well connected\nand robust (this is the case of\nexpander graphs), but there is no reason to think that\nsimply making the degree distribution of a given network more regular will\nimprove its expansion properties.\n\\item {\\bf Subgraph centrality}: the average subgraph centrality of a\nnetwork is known in the literature as the normalized {\\em Estrada index}:\n$$\\frac{1}{n}EE(A) = \\frac{1}{n}{\\text Tr} (e^A) = \n\\frac{1}{n}\\sum_{i=1}^n [e^A]_{ii} = \\frac{1}{n}\\sum_{i=1}^n \ne^{\\lambda_i}.$$\nIt can also be interpreted as the average self-communicability of the\nnodes. As we mentioned, this is a lower bound for the average total\ncommunicability. Evaluation of this quantity requires knowledge\nof all $n$ diagonal entries of $e^A$, or of all the eigenvalues of $A$ \nand is therefore much more expensive to compute. The heuristics we\nderive in this paper have a similar effect on $TC(A)$ and on the\nEstrada index, as we demonstrate in section \\ref{sec:nc+gap}. So, \nusing subgraph centrality instead of total communicability centrality\nwould lead to exactly the same heuristics and results, with the\ndisadvantage that evaluating the objective function, if necessary, would\nbe much more expensive.\n\\item {\\bf Katz centrality}: the Katz centrality of node $i\\in V$ is\ndefined as the $i$th row sum of the matrix resolvent\n $(I - \\alpha A)^{-1}$, where\nthe parameter $\\alpha$ is chosen in the interval $(0,\\frac{1}{\\lambda_1})$,\nso that the power series expansion\n$$(I - \\alpha A)^{-1} = I + \\alpha A + \\alpha^2 A^2 + \\cdots$$\nis convergent \\cite{Katz}.\nSince this centrality measure can be interpreted in terms of walks,\nusing it instead of the total communicability\nwould lead to the same heuristics and very similar\nresults, especially when\n$\\alpha$ is sufficiently close to $\\frac{1}{\\lambda_1}$ or if the\nspectral gap $\\lambda_1 - \\lambda_2$ is large; see \\cite{Benzi2015}.\nUsing Katz centrality, however, requires the careful selection of the\nparameter $\\alpha$, which leads to some complications. For example,\nafter each update one needs to recompute the dominant eigenvalue\nof the adjacency matrix in order to check whether the value of\n$\\alpha$ used is still within the range of permissible values or if\nit has to be reduced, making\nthis approach computationally very expensive. This\nproblem does not arise if the matrix exponential is used instead\nof the resolvent. \n\\item {\\bf Other centrality measures}: So far we have only discussed\ncentrality measures that can be expressed in terms of the adjacency matrix $A$.\nThese centrality measures are all connected to the notion of walk in a graph,\nand they can often be understood in terms of spectral graph theory.\nOther popular centrality measures, such as betweenness centrality and\ncloseness centrality (see, e.g., \\cite{NewmanBook}) do not have a simple\nformulation in terms of matrix properties. They are based on the assumption\nthat all communication in a graph tends to take place along shortest paths,\nwhich is not always the case (this was a major motivation for the\nintroduction of walk-based measures, which postulate that communication\nbetween nodes can take place along walks of any length, with a preference\ntowards shorter ones). A further disadvantage is that they are quite\nexpensive to compute, although randomized approximations can bring the\ncost down to acceptable levels \\cite{Brandes}. For these reasons we do not\nconsider them in this paper, where the focus is on linear algebraic\ntechniques. It remains an open question whether heuristics for\nmanipulating graph edges so as to tune some gloabl average of these\ncentrality measures can lead to networks with desirable connectivity\nand robustness properties.\n\\end{enumerate}\n\n\\vspace{0.1in}\n\nFinally, in view of the bounds (\\ref{coarse_bounds}), the evolution of\nthe total communicability under network modifications is closely tied\nto the evolution of \nthe dominant eigenvalue $\\lambda_1$. This quantity plays a\ncrucial role in network analysis, for example in the definition\nof the {\\em epidemic threshold}; see, for instance, \\cite[p.~664]{NewmanBook} and\n\\cite{VM2011}. In particular, a decrease in the total network \ncommunicability can be expected to lead to an increase in the\nepidemic threshold. \nThus, edge modification techniques developed for tuning $TC(A)$\ncan potentially be used to\nalter epidemics dynamics.\n\n\n\\section{Bounds via quadrature rules}\n\\label{sec:bounds}\nIn the previous section we saw the simple bounds (\\ref{coarse_bounds}) on\nthe normalized total network communicability. \nMore refined bounds for this index can be obtained by means of quadrature \nrules as described in \\cite{Benzi2010,Benzi1999,Golub2010,Fenu2013}.\n\nThe following theorem contains our result on the bounds for the normalized total communicability.\n\\begin{theorem}\\label{thm:bounds}\nLet $A$ be the adjacency matrix of an unweighted and undirected network. Then\n\\begin{equation*}\n\\Phi\\left(\\beta,\\omega_1+\\frac{\\gamma_1^2}{\\omega_1-\\beta}\\right)\\leq \n\\frac{TC(A)}{n}\\leq\\Phi\\left(\\alpha,\\omega_1+\\frac{\\gamma_1^2}{\\omega_1-\\alpha}\\right)\n\\end{equation*}\nwhere $[\\alpha,\\beta]$ is an interval containing the spectrum of $-A$ \n(i.e., $\\alpha \\le -\\lambda_1$ and $\\beta\\ge -\\lambda_n$), \n$\\omega_1=-\\mu=-\\frac{1}{n}\\sum_{i=1}^nd_i$ is the negative mean of the degrees, \n$\\gamma_1=\\sigma=\\sqrt{\\frac{1}{n}\\sum_{k=1}^n(d_k-\\mu)^2}$ is the standard deviation, and\n\\begin{equation}\\label{eq:bound}\n\\Phi(x,y)=\\frac{c \\left(e^{-x}-e^{-y}\\right)+xe^{-y}-ye^{-x}}{x-y}, \\qquad c=\\omega_1. \n\\end{equation}\n\\end{theorem}\n\n\nA proof of this result can be found in the Supplementary Materials \naccompanying the paper.\n\nAnalogous bounds can be found for the \nadjacency matrix of the graph after performing \na downdate or an update. \nThese results are summarized in the following Corollaries.\n\n\\begin{corollary}\\label{Dwdt} [Downdating]\nLet $\\widehat{A}=A-UW^T$, where $U=[\\mathbf{e}_i,\\mathbf{e}_j]$ and\n$W=[\\mathbf{e}_j,\\mathbf{e}_i]$ be the adjacency matrix of an unweighted and \nundirected network obtained after the downdate of the edge $(i,j)$ from the matrix $A$.\nLet $\\omega_1=-\\mu=-\\frac{1}{n}\\sum_{i=1}^nd_i$ and $\\gamma_1=\\sigma=\n\\sqrt{\\frac{1}{n}\\sum_{k=1}^n(d_k-\\mu)^2}$, where $d_i$ is the degree of node $i$ in the \noriginal graph. \nThen\n\\begin{equation*}\n\\Phi\\left(\\beta_{-},\\omega_{-}+\\frac{\\gamma_{-}^2}{\\omega_{-}-\\beta_{-}}\\right)\n\\leq\\frac{TC(\\widehat{A})}{n}\\leq\n\\Phi\\left(\\alpha_-,\\omega_-+\\frac{\\gamma_-^2}{\\omega_--\\alpha_-}\\right)\n\\end{equation*}\nwhere\n\\begin{equation*}\n\\left\\{\n\\begin{array}{l}\n\\omega_{-}=\\omega_1+\\frac{2}{n};\\\\[6pt]\n\\gamma_{-}=\\sqrt{\\gamma_1^2-\\frac{2}{n}\\left(d_i+d_j-1+2\\omega_1+\n\\frac{2}{n}\\right)}\n\\end{array}\n\\right. ,\n\\end{equation*}\n$\\alpha_-$ and $\\beta_-$ are approximation of the smallest and largest \neigenvalues of $-\\widehat{A}$ respectively, \nand $\\Phi$ is defined as in equation \\eqref{eq:bound} with $c=\\omega_-$.\n\\end{corollary}\n\nNote that if bounds $\\alpha$ and $\\beta$ for the extremal eigenvalues of \nthe original matrix are known, we can then use $\\alpha_-=\\alpha$ and $\\beta_-=\\beta+1$.\nIndeed, if we order the eigenvalues of $\\widehat{A}$ in non--increasing order \n$\\widehat{\\lambda}_1>\\widehat{\\lambda}_2\\geq \\cdots \\geq\\widehat{\\lambda}_n$ we\nobtain, as a consequence of Weyl's Theorem\n(see \\cite[Section 4.3]{Horn}), that\n\\begin{equation*}\n\\alpha-1 \\leq -\\lambda_1-1 < -\\widehat{\\lambda}_1 < \n-\\widehat{\\lambda}_2 \\leq \\cdots \\leq -\\widehat{\\lambda}_n < -\\lambda_n+1 \\leq \\beta+1.\n\\end{equation*}\n\nFurthermore, the Perron--Frobenius Theorem ensures that, when performing a \ndowndate, the largest eigenvalue of the adjacency matrix cannot increase; \nhence, we deduce the more stringent bounds $\\alpha\\leq-\\widehat{\\lambda}_1\\leq\n-\\widehat{\\lambda}_2\\leq\\cdots\\leq -\\widehat{\\lambda}_n\\leq \\beta +1.$\n\nSimilarly, we can derive bounds for the normalized total communicability \nof the matrix $\\tilde{A}$ \nobtained from the matrix $A$ after performing the update of the virtual edge $(i,j)$.\n\n\\begin{corollary}\\label{Updt} [Updating]\nLet $\\tilde{A}=A+UW^T$, where $U=[\\mathbf{e}_i,\\mathbf{e}_j]$ and\n$W=[\\mathbf{e}_j,\\mathbf{e}_i]$ be the adjacency matrix of an unweighted and \nundirected network obtained after the update of the virtual edge \n$(i,j)$ in the matrix $A$.\nLet $\\omega_1=-\\mu=-\\frac{1}{n}\\sum_{i=1}^nd_i$ and $\\gamma_1=\\sigma=\n\\sqrt{\\frac{1}{n}\\sum_{k=1}^n(d_k-\\mu)^2}$, where $d_i$ is \nthe degree of node $i$ in the \noriginal graph.\nThen\n\n\\begin{equation*}\n\\Phi\\left(\\beta_{+},\\omega_{+}+\\frac{\\gamma_{+}^2}{\\omega_{+}-\\beta_{+}}\\right)\\leq \n\\frac{TC(\\tilde{A})}{n}\n\\leq\\Phi\\left(\\alpha_+,\\omega_++\\frac{\\gamma_+^2}{\\omega_+-\\alpha_+}\\right)\n\\end{equation*}\nwhere\n\\begin{equation*}\n\\left\\{\n\\begin{array}{l}\n\\omega_{+}=\\omega_1-\\frac{2}{n};\\\\[6pt] \n\\gamma_{+}=\\sqrt{\\gamma_1^2+\\frac{2}{n}\\left(d_i+d_j+1+2\\omega_1-\\frac{2}{n}\\right)}\n\\end{array}\n\\right. ,\n\\end{equation*}\n$\\alpha_+$ and $\\beta_+$ are bounds for the smallest and largest eigenvalues of \n$-\\tilde{A}$ respectively, and $\\Phi$ is defined as in equation \n\\eqref{eq:bound} with $c=\\omega_+$.\n\\end{corollary}\n\nNotice that again, if bounds $\\alpha$ and $\\beta$ for the extremal \neigenvalues of $-A$ are known, we can then take $\\alpha_+=\\alpha-1$ and $\\beta_+=\\beta$.\nIn fact, the spectrum of the rank-two symmetric perturbations $UW^T$ and $-UW^T$ is \n$\\{\\pm 1,0\\}$ and hence we can \nuse Weyl's Theorem as before and then improve the upper bound\nusing the Perron--Frobenius Theorem.\n\n\nIn the next section we will see how the new bounds can be used to \nguide the updating and downdating process.\n\n\n\n\\section{Modifications of the adjacency matrix}\n\\label{sec:modification}\nIn this section we develop techniques that allow us to tackle \nthe following problems.\n\n\\begin{itemize}\n\\item[(P1)] Downdate: select $K$ edges that can be downdated from \nthe network without disconnecting it and that cause the smallest\ndrop in the total \ncommunicability of the graph;\n\\item[(P2)] Update: select $K$ edges to be added to the network \n(without creating self--loops or multiple edges) so as to increase as much as \npossible the total communicability of the graph;\n\\item[(P3)] Rewire: select $K$ edges to be rewired in the network \nso as to increase as much as possible the value of $TC(A)$. The\nrewiring process must not disconnect the network or \ncreate self--loops or multiple edges in the graph.\n\\end{itemize}\n\nAs we will show below, (P3) can be solved using combinations of methods \ndeveloped to solve (P1) and (P2).\nHence, we first focus on the downdate and the update separately. \nNote that to decrease as little as possible the total communicability \nwhen removing an edge it would suffice to select $(i^*,j^*)\\in E$ so as\nto minimize the quantities\n$$\\mathbf{1}^TA^k\\mathbf{1} -\n\\mathbf{1}^T(A-UW^T)^k\\mathbf{1}\\qquad \\forall k=1,2,\\ldots ,$$\nsince $TC(A)=\\sum_{k=0}^\\infty\\frac{\\mathbf{1}^TA^k\\mathbf{1}}{k!}$.\nSimilarly, to increase as much as possible $TC(A)$ by addition of a virtual edge, \nit would suffice to select $(i^*,j^*)\\in\\overline{E}$ \nthat maximizes the differences \n$$\\mathbf{1}^T(A+UW^T)^k\\mathbf{1}-\\mathbf{1}^TA^k\\mathbf{1}\\qquad \\forall k=1,2,\\ldots $$\nHowever, it is easy to show that in general one can not find a choice for\n $(i^*,j^*)$ that works for all such $k$. \nIndeed, numerical experiments on small synthetic graphs \n(not shown here) show that in general the optimal edge selection \nfor $k=2$ is different from the one for $k=3$.\nBecause of this, \nit is unlikely that one can find a simple\n``closed form solution\" to the problem, and\nwe need to develop approximation techniques.\n\nThe majority of the heuristics we will develop are based on new edge centrality measures. \nThe idea underlying these is that it seems reasonable to assume that an edge is more likely used as communication channel if its adjacent \nnodes are given a lot of information to spread. \nWe thus introduce three new centrality measures for edges based on this principle: edges connecting important nodes are themselves \nimportant. \n\n\n\n\\begin{definition}\nFor any $i,j\\in V$ we define the {\\rm edge subgraph centrality} of \nan existing\/virtual edge $(i,j)$ as\n\\begin{equation}\n^eSC(i,j)=\\left(e^A\\right)_{ii}\\left(e^A\\right)_{jj}.\n\\label{eq:edge_subgraph}\n\\end{equation}\n\\label{def:subgraph}\n\\end{definition}\n\nThis definition, based on the subgraph centrality of nodes, exploits the fact that the matrix exponential is symmetric positive definite \nand hence $(e^A)_{ii}(e^A)_{jj}>(e^A)_{ij}^2$. \nTherefore, the diagonal elements of $e^A$ somehow control its off-diagonal entries, \nhence they may contain \nenough information to infer the ``payload'' of the edges or of the virtual edges of interest. \n\n\\begin{definition}\nFor any $i,j\\in V$ we define the {\\rm edge total communicability centrality}\nof an existing\/virtual\nedge $(i,j)$ as\n\\begin{equation}\n^eTC(i,j) = [e^A{\\bf 1}]_i [e^A{\\bf 1}]_j.\n\\end{equation}\n\\label{def:tc_edge}\n\\end{definition}\n\nIt is important to observe that when\nthe spectral gap $\\lambda_1-\\lambda_2$ is ``large enough'', \nthen the subgraph centrality $\\left(e^A\\right)_{ii}$ \nand the total communicability centrality $[e^A{\\bf 1}]_i$ are\nessentially \ndetermined by \n$e^{\\lambda_1}q_1(i)^2$ and $e^{\\lambda_1}q_1(i)\\|{\\bf q}_1\\|_1$, respectively (see, e.g., \\cite{Benzi2013,Benzi2015,E06b}); it \nfollows that in this case \nthe two centrality measures introduced and a centrality measure based on the eigenvector centrality for nodes can be expected to provide similar rankings. \nThis is especially true when attention is restricted to the top edges (or nodes).\nThis observation motivates the introduction of the following edge centrality measure.\n\n\\begin{definition}\nFor any $i,j\\in V$ we define the {\\rm edge eigenvector centrality} of an existing\/virtual \nedge $(i,j)$ as\n\\begin{equation}\n^eEC(i,j)=q_1(i)q_1(j).\n\\label{eq:edge_eigenvector}\n\\end{equation}\n\\label{def:eigenvector}\n\\end{definition}\nAs a further justification for this definition, note that \n\n$$\\lambda_1-2 \\left( ^eEC(i,j)\\right)\\leq\\widehat{\\lambda}_1\\leq\\lambda_1,\\qquad\n\\tilde{\\lambda}_1\\geq\\lambda_1+2 \\left( ^eEC(i,j)\\right),$$\n\nwhere $\\widehat{\\lambda}_1$ is the leading eigenvalue of the matrix $\\widehat{A}$ and $\\tilde{\\lambda}_1$ \nis the leading eigenvalue of the matrix $\\tilde{A}$, as defined in \nsection \\ref{sec:background}.\nThese inequalities show that the edge eigenvector centrality \nof an existing\/virtual edge $(i,j)$ is strictly connected \nto the change in the value of the leading eigenvalue of the adjacency matrix, \nwhich influences\nthe evolution of the total communicability when we modify $A$ (see Remark \\ref{rem:lambda1}). \n\n\\begin{rem}\n{\\rm The edge eigenvector centrality has been used in \\cite{Tong, VM2011} to \ndevise edge removal techniques aimed to reduce significantly $\\lambda_1$, so as \nto increase the {\\em epidemic threshold} of networks.}\n\\end{rem}\n\n\nNote that we defined these measures of centrality for \nboth existing and virtual edges (as in \\cite{Berry2013}). \nThe reason for this as well as the justification for these definitions \nwill become clear in the next subsections. \n\n\nWe now discuss\n how to use these definitions to tackle the problems previously described. \nThe computational aspects concerning the implementation of the heuristics we are about to introduce \nand the derivation of their computational costs are described in the \nSupplementary Materials. \n\n\\subsection*{(P1) Downdate}\nThe downdate of any edge in the network will result in a reduction of \nits total communicability. \nNote that since we are focusing on the case of connected networks, we \nwill only perform downdates that keep the resulting graph connected.\nIn practice, it is desirable to further\nrestrict the choice of downdates to a subset of\nall existing edges, on the basis of criteria to be discussed shortly.\n\nAn ``optimal\" approach would select at each step of the downdating \nprocess a candidate edge corresponding to the minimum decrease of \ncommunicability.\\footnote{Strictly speaking, this would correspond to\na greedy algorithm which is only locally optimal. In general, this\nis unlikely to result in ``globally optimal\" network communicability.\nIn this paper, the term ``optimal\" will be understood in this limited\nsense only.} \nNote that for large networks this method is too costly to be practical.\nFor this reason we aim to develop inexpensive techniques that will hopefully\ngive close--to--optimal results.\nNevertheless, for small networks we will use the\n``optimal\" approach (where we systematically try all feasible\nedges and delete the one causing the least drop in total communicability) \nas a baseline method against which we compare the various\nalgorithms discussed below. This method will be henceforth\nreferred to as {\\tt optimal}.\n\nThe next methods we introduce perform the downdate of the lowest ranked existing edge according to \nthe edge centrality measures previously introduced whose removal does not disconnect the network. \nWe will refer to these methods as {\\tt subgraph}, {\\tt nodeTC}, and {\\tt eigenvector}, which are \nbased on definitions \\ref{def:subgraph}, \\ref{def:tc_edge}, and \\ref{def:eigenvector}, respectively. \nFrom the point of view of the communicability,\nthese methods downdate an edge connecting two nodes which are peripheral (i.e., have low centrality)\nand therefore are not expected to \ngive a large contribution to the spread of information along the network.\nHence, the selected edge is connecting two nodes whose ability to\nexchange information is already very low, and\nwe do not expect the total communicability to suffer too much from this edge removal.\nThis observation also suggests that such downdates \ncan be repeatedly applied without the need to recompute the ranking\nof the edges after each downdate. \nAs long as the number of downdates performed remains small compared to the total number of edges, \nwe expect good results at a greatly reduced total cost. \nNote also that such downdates can be performed simultaneously rather than sequentially.\nWe will refer to these variants as {\\tt subgraph.no}, {\\tt nodeTC.no}, and {\\tt eigenvector.no}.\n\n\n\nFinally, we consider a technique motivated by the bounds \nobtained via quadrature rules derived in section \\ref{sec:bounds}.\nFrom the expression for the function $\\Phi$ in the special case of the downdate \n(cf.~Corollary \\ref{Dwdt}),\nwe infer that a potentially good choice may be to remove the edge\nhaving incident nodes $i,j$ for which the sum $d_i+d_j$ is minimal, if its \nremoval does not disconnect the network.\nIndeed, this choice reduces the upper bound only slightly and the total \ncommunicability may mirror this behavior.\nAnother way to justify this strategy is to observe that it is indeed\nthe optimal strategy if we approximate $e^A$ with its second-order\napproximation $I + A + \\frac{1}{2}A^2$ in the definition of total\ncommunicability.\nThis technique will be henceforth referred to as {\\tt degree}. \nWe note that a related measure, namely, the average \nof the out-degrees $\\frac{d_i+d_j}{2}$, \nwas proposed in \\cite{Berry2013} as a measure for the centrality of \nan edge $(i,j)$ in directed graphs.\n\n\\subsection*{(P2) Update}\nMost real world networks are characterized by low average degree. \nAs a consequence, the adjacency matrices of such networks are sparse ($m=O(n)$). \nFor the purpose of selecting a virtual edge to be updated, this implies \nthat we have approximately $\\frac{1}{2}\\left(n^2-cn\\right)$ possible choices \nif we want to avoid the formation of multiple edges or self--loops (here $c$ is\na moderate constant).\nEach one of these possible updates will result in an increase of the total \ncommunicability of the network, but not every one of these will result in a\nsignificant increment. \n\nOne natural updating technique is to connect two nodes having high \ncentralities, i.e., add the virtual \nedge having the highest ranking according to the corresponding edge centrality. \nIts incident nodes, being quite central, can be expected to have an \nimportant role in the spreading of information along the network; \non the other hand, the communication between them may be relatively poor \n(think for example of the case where the two nodes sit in two distinct\ncommunities). \nHence, giving them a preferential communication channel, such as an \nedge between them, should result in a better spread of information along the \nwhole network. \nAgain, we will use the labels {\\tt subgraph}, {\\tt nodeTC}, and {\\tt eigenvector} to describe these \nupdating strategies. \nAs before, in order to reduce the computational cost, we also test the effectiveness \nof \nthese techniques\nwithout the recomputation of the \nranking of the virtual edges after each update.\nThese variants (referred to as {\\tt subgraph.no}, {\\tt nodeTC.no}, and {\\tt eigenvector.no}) are \nexpected to return good results as well, since \nthe selected update should not radically change the ranking of the edges. \nIndeed, they make central nodes even more central, and the ranking of the \nedges remains consequently almost unchanged.\nNote again that these updates can be performed simultaneously \nrather than sequentially.\n\n\nAs for the case of downdating, \nthe bounds via quadrature rules derived in section \\ref{sec:bounds} \nsuggest an updating technique, i.e., adding the virtual edge $(i,j)$ for which $d_i+d_j$ is maximal. Indeed,\nsuch a choice would maximize the lower bound on the total communicability, see\nCorollary \\ref{Updt}. Again, this choice can also be justified by noting that it\nis optimal if $e^A$ is replaced by its quadratic Maclaurin approximant.\nWe will again use the label {\\tt degree} to refer to this updating strategy.\n\nAll these techniques will be compared with the {\\tt optimal} one, \nbased on systematically trying all feasible virtual edges and selecting\nat each step the one resulting in the largest increase of the\ntotal communicability. Due to the very high cost of this brute force\napproach, we will use it only on small networks.\n\n\\begin{table}\n\\footnotesize\n\\centering\n\\caption{Brief description of the techniques introduced in the paper.}\n\\begin{tabular}{lcc}\n\\hline\n Method & Downdate: $(i,j)\\in E$ & Update: $(i,j)\\not\\in E$\\\\\n\\hline\n {\\tt optimal} & $\\arg\\min\\{TC(A)-TC(\\widehat{A})\\}$ & $\\arg\\max\\{TC(\\tilde{A})-TC(A)\\}$ \\\\\n {\\tt subgraph(.no)} & $\\arg\\min\\{^eSC(i,j)\\}$ & $\\arg\\max\\{^eSC(i,j)\\}$\\\\\n {\\tt eigenvector(.no)} & $\\arg\\min\\{^eEC(i,j)\\}$ & $\\arg\\max\\{^eEC(i,j)\\}$\\\\\n {\\tt nodeTC(.no)} & $\\arg\\min\\{^eTC(i,j)\\}$ & $\\arg\\max\\{^eTC(i,j)\\}$\\\\\n {\\tt degree} & $\\arg\\min\\{d_i+d_j\\}$ & $\\arg\\max\\{d_i+d_j\\}$ \\\\\n\\hline\n\\end{tabular}\n\\label{tab:description}\n\\end{table}\n\nThe heuristics introduced to tackle (P1) and (P2) are summarized in Table \\ref{tab:description}.\n\n\\subsection*{(P3) Rewire}\n\\label{subsec:rewire}\nAs we have already noted, there are situations in which\nthe rewire of an edge may be preferable \nto the addition of a new one.\nThere are various possible choices for the rewiring strategy to follow. \nThe greatest part of those found in literature are variants of random rewiring \n(see for example \\cite{Beygelzimer2005,Louzada2013}). \nIn this paper, on the other hand, we are interested in devising\nmathematically informed rewiring strategies. \nFor comparison purposes, however, we will compare our rewiring methods\nto the random rewire method, {\\tt random}, \nwhich downdates an edge (chosen uniformly at random\namong all edges whose removal does not disconnect the network) and then updates a \nvirtual edge, also chosen uniformly at random.\n\nCombining the various downdating and updating methods previously introduced \nwe obtain different rewiring strategies based on the centralities of edges and \non the bounds for the total communicability. \nConcerning the methods based on the edge subgraph, eigenvector,\nand total communicability centrality, \nwe note that since a single downdate does not dramatically change \nthe communication capability of the network, we do not need to recompute \nthe centralities and the ranking of the edges after each downdating step,\nat least as long as the number of rewired edges remains relatively small \n(numerical experiments not shown here support this claim).\nOn the other hand, after each update we may or may not recalculate\nthe edge centralities. As before, we use {\\tt subgraph}\/{\\tt subgraph.no},\n{\\tt eigenvector}\/{\\tt eigenvector.no} and {\\tt nodeTC}\/{\\tt nodeTC.no} to\nrefer to these three variants of rewiring.\nAdditionally,\nwe introduce another rewiring strategy, henceforth referred to\nas {\\tt node}, based on the subgraph centrality of the nodes. \nIn this method we disconnect the most central node from the least central node among \nits immediate neighbors;\nthen we connect it to the most central node among those it is not linked to.\nIt is worth emphasizing that this strategy is philosophically different from the \nprevious ones based on the edge subgraph centrality in the downdating phase \n(the updating step is the same). \nIn fact, in those methods we use information on the nodes in order to \ndeduce some information on the edges connecting them; on the other hand, \nthe {\\tt node} algorithm does not take into account the potentially \nhigh ``payload'' of the edges involved, whose removal may result in a \ndramatic drop in the total communicability.\n\n\n\n\n\n\n\n\n\n\\section{Numerical studies}\n\\label{sec:test_tc}\nIn this section we discuss the results of numerical studies performed in order\nto assess the effectiveness and efficiency of the proposed techniques.\nThe tests have been performed on both synthetic and real-world networks, \nas described below. \nWe refer to the Supplementary Materials for the results of computations\nperformed on four small social networks, aimed at comparing our heuristics with\n{\\tt optimal}. These results show that for these small networks, the resulting\ntotal communicabilities are essentially identical to those obtained with the\n{\\tt optimal} strategy.\n\n\\subsection{Real-world networks}\n\\begin{table}[t]\n\\footnotesize\n\\centering\n\\caption{Description of the Data Set.}\n\\label{tab:Datasets}\n\\begin{tabular}{cccccc\n\\hline\nNAME & $n$ & $m$ & $\\lambda_1$ & $\\lambda_2$ & $\\lambda_1-\\lambda_2$ \\\\\n\\hline\nMinnesota & 2640 & 3302 & 3.2324 & 3.2319 & 0.0005 \\\\\nUSAir97 & 332 & 2126 & 41.233 & 17.308 & 23.925 \\\\\nas--735 & 6474 & 12572 & 46.893 & 27.823 & 19.070 \\\\\nErd\\\"os02 & 5534 & 8472 & 25.842 & 12.330 & 13.512 \\\\\nca--HepTh & 8638 & 24806& 31.034 & 23.004 & 8.031 \\\\\nas--22july06 & 22963 & 48436 & 71.613 & 53.166 & 18.447 \\\\\nusroad--48 & 126146 & 161950 & 3.911 & 3.840 & 0.071 \\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\n\nAll the\nnetworks used in the tests can be found in the University of \nFlorida Sparse Matrix Collection \\cite{Davis} under different ``groups''.\nThe USAir97 and Erd\\\"os02 networks are from the Pajek group. \nThe USAir97 network describes the US Air flight routes in 1997, while \nthe Erd\\\"os02 network represents the Erd\\\"os collaboration network, \nErd\\\"os included. \nThe network as--735, from the SNAP group, is the communication network \nof a group of autonomous system (AS) measured over 735 days between \nNovember 8, 1997 and January 2, 2000. \nCommunication occurs when routers from two ASs exchange information.\nThe Minnesota network from the Gleich group represents the Minnesota road network. \nThese latter three networks are not connected, therefore the tests were \nperformed on their largest connected component.\nWe point out that the original largest connected component \nof the network as--735 has 1323 ones on the main diagonal \nwhich were retained in our tests.\nThe network ca--HepTh is from the SNAP group and represents the \ncollaboration network of arXiv High Energy Physics Theory;\nthe network as--22july06 is from the Newman group and represents the \n(symmetrized) structure of Internet routers as of July 22, 2006.\nFinally, the network usroad--48, which is from the Gleich group, \nrepresents the continental US road network. For each network,\nTable \\ref{tab:Datasets} reports the number of nodes ($n$), \nthe number of edges ($m$), the \ntwo largest eigenvalues, and the spectral gap.\nWe use the first four networks to test all methods described\nin the previous section (except for {\\tt optimal}, which is only\napplied to the four smallest networks --- see the Supplementary Materials)\n and the last three to illustrate the\nperformance of the most efficient among the methods tested.\n\n\n\n\n\n\\begin{figure}[t]\n\\centering\n\\caption{Evolution of the normalized total communicability vs.~number \nof downdates, updates and rewires\nfor networks Minnesota and as735.}\n\\label{fig:Minnesota_as735}\n\\includegraphics[width=.9\\textwidth]{Minnesota_as735.eps}\n\\end{figure}\n\n\\begin{figure}[h]\n\\centering\n\\caption{Evolution of the normalized total communicability vs.~number\nof downdates, updates and rewires for networks USAir97 and Erd\\\"os02.}\n\\label{fig:USAir97_Erdos02}\n\\includegraphics[width=.9\\textwidth]{USAir97_Erdos02.eps}\n\\end{figure}\n\n\nWe first consider the networks \nMinnesota, as735, USAir97, and Erd\\\"os02, for which we perform $K=50$ modifications. \nFor these networks the set $\\overline{E}$ \n(the complement of the set $E$ of edges) is large enough \nthat performing an extensive search for the \nedge to be updated is expensive. Hence, we form the set $S$ containing the top\n$10\\%$ of the nodes ordered according to the eigenvector centrality \nand we restrict our search to virtual edges incident to these nodes\nonly. An exception is\nthe network USAir97 where we have used the set $S$ corresponding to\nthe top $20\\%$ of the nodes, since in the case of $10\\%$ this set contained \nonly 52 virtual edges.\nIn Figures \\ref{fig:Minnesota_as735} and \\ref{fig:USAir97_Erdos02}\nwe show results for the methods {\\tt eigenvector}, {\\tt eigenvector.no},\n{\\tt subgraph}, {\\tt subgraph.no} and {\\tt degree}. \nBefore commenting on these, we want to stress the poor performance of {\\tt node} \nwhen tackling (P3); this shows that the use of edge centrality measures (as opposed to \nnode centralities alone) is indispensable in this framework. \nThe results for these networks clearly show \nthe effectiveness of\nthe {\\tt eigenvector} and {\\tt subgraph} algorithms\nand of their less expensive variants {\\tt eigenvector.no} and {\\tt subgraph.no} in\nnearly all cases; similar results were obtained with\n{\\tt nodeTC} and {\\tt nodeTC.no} (not shown). The only exception is in the downdating\nof the Minnesota network,\nwhere the eigenvector-based techniques\ngive slightly worse results.\nThis fact is easily explained in view of the tiny spectral\ngap characterizing this and similar networks\\footnote{Small spectral gaps are typical\nof large, grid-like\nnetworks such as the road networks or the graphs corresponding to\nuniform triangulations or discretizations of physical domains.}\n(see Table \\ref{tab:Datasets}).\nBecause of this property, eigenvector centrality is a poor approximation\nof subgraph centrality and cannot be expected to give\nresults similar to those obtained with {\\tt subgraph}\nand {\\tt subgraph.no}. \n\nThe results for the downdate show that the inexpensive {\\tt degree}\nmethod does not perform as well on these networks, except\nperhaps on Minnesota. The relatively poor performance of\nthis method is due to the fact that the information used by this\nmethod to select an edge for downdating is too local.\n\nNote, however, the scale on the vertical axis in Figures \n\\ref{fig:Minnesota_as735}--\\ref{fig:USAir97_Erdos02}, \nsuggesting that for these networks (excluding perhaps Minnesota)\nall the edge centrality-based methods perform well with only very small relative\ndifferences between the resulting total communicabilities. \n\n\nOverall, these results indicate that the edge centrality-based\nmethods, especially the inexpensive {\\tt eigenvector.no} and {\\tt nodeTC.no} variants, \nare an excellent choice in almost all cases and to tackle all the problems. \nIn the case of downdating\nnetworks with small spectral gaps, \n{\\tt subgraph.no} may be preferable but at a higher cost.\n\nThe behavior of the {\\tt degree} method depends strongly on the \nnetwork on which it is used. Our tests indicate that it behaves\nwell in some cases (for example, P2 for Erd\\\"os02) but poorly in others (P2 for Minnesota).\nWe speculate that this method may perform adequately when tackling (P2) on scale-free\nnetworks (such as Erd\\\"os02) where a high degree is an indication\nof centrality in spreading information across the network.\n\nSome comments on the difference in the results for updating as compared to those \nfor rewiring (downdating followed by updating) are in order.\nRecall that our downdating strategies aim to reduce as little as \npossible the decrease in the value of the total communicability, whereas the \nupdating techniques aim to increase this index as much as possible.\nWith this in mind, it is not surprising to see that the \ntrends of the evolution of the total communicability after rewiring reflect those \nobtained with the updating strategies.\n The values obtained\nusing the updates are in general higher than those obtained using the rewiring \nstrategies, since updating implies the addition of edges whereas in\nrewiring the number of edges remains the same. \nExperiments not reported here indicate that\nthe methods based on the edge eigenvector \nand total communicability\ncentrality are more stable than the others \nunder rewiring and to dampen the effect of the downdates\n\nIn Figures \\ref{fig:large_down}-\\ref{fig:large_up} we show results for \nthe three largest networks in our data set (ca--HepTh, as-22july06 and\nusroad-48). \nIn the case of the updating, we have selected the virtual edges among those in the subgraph \ncontaining the top $1\\%$ of nodes ranked according to the eigenvector centrality. \nWe compare the following methods:\n{\\tt eigenvector}, {\\tt eigenvector.no}, {\\tt nodeTC}, {\\tt nodeTC.no},\n{\\tt subgraph.no} and {\\tt degree}; \nrandom downdating was also tested and found to give poor results. \nNote that network\nusroad-48 behaves similarly to Minnesota; this is not surprising in view of\nthe fact that these are both road networks with a tiny spectral gap.\nLooking at the scale on the vertical axis, however, it is clear that\nthe decrease in total communicability is negligible with all the methods\ntested here.\nThe results on these networks confirm the general trend observed so far;\nin particular, we note the excellent behavior of {\\tt nodeTC} and\n{\\tt nodeTC.no}.\n\n\\begin{figure}[t]\n\\centering\n\\caption{Downdates for large networks: normalized total communicability vs.~number of modifications.}\n\\includegraphics[width=.9\\textwidth]{Large_down_new.eps}\n\\label{fig:large_down}\n\\end{figure}\n\n\\begin{figure}[t]\n\\centering\n\\caption{Updates for large networks: normalized total communicability vs.~number of modifications.}\n\\includegraphics[width=.9\\textwidth]{Large_up_new.eps}\n\\label{fig:large_up}\n\\end{figure}\n\n\n\n\\subsection{Synthetic networks}\nThe synthetic examples used in the tests were produced using \nthe CONTEST toolbox for Matlab (see \\cite{Contest,Taylor2009}).\nWe tested two types of graphs: the preferential attachment \n(Barab\\'asi--Albert) model and the small world (Watts--Strogatz) model. \n\n\\begin{figure}[t]\n\\centering\n\\caption{Evolution of the total communicability when 50 downdates,\nupdates or rewires are performed on two synthetic networks with $n=1000$ nodes.}\n\\includegraphics[width=.9\\textwidth]{synthetic.eps}\n\\label{fig:synthetic}\n\\end{figure}\n\nThe preferential attachment model \n\\cite{prefattach} was designed to produce networks with \nscale--free degree distributions as well as the small world property,\ncharacterized by short average path length and relatively high clustering\ncoefficient.\nIn CONTEST, preferential attachment networks are constructed using \nthe command \\texttt{pref(n,d)} where $n$ is the number of nodes and $d\\geq 1$ is the \nnumber of edges each new node is given when it is first introduced to the network.\nThe network is created by adding nodes one by one (each new node with $d$ edges).\nThe edges of the new node connect to nodes already in the network with \na probability proportional to the degree of the already existing nodes.\nThis results in a scale--free degree distribution.\n\n\nThe second class of synthetic test matrices used in our experiments \ncorresponds to Watts--Strogatz small world networks.\nThe small world model was developed as a way to impose a high \nclustering coefficient onto classical random graphs \\cite{Watts1998}.\nThe function used to build these matrices takes the form \\texttt{smallw(n,k,p)}.\nHere $n$ is the number of nodes in the network, originally arranged in a\nring and connected to their $k$ nearest neighbors. Then each node is\nconsidered independently and, with probability $p$, an edge is added\nbetween the node and one of the other nodes in the graph, chosen uniformly\nat random (self-loops and multiple edges are not allowed). \nIn our tests, we have used matrices with $n=1000$ nodes which were \nbuilt using the default values for the functions previously described. \nWe used $d=2$ in the Barab\\'asi--Albert model \nand $k=2$, $p=0.1$ in the Watts--Strogatz model. \n\n\nThe results for our tests are presented in Figure \\ref{fig:synthetic}.\nThese results agree with what we have seen previously on real-world networks. \nInterestingly, {\\tt degree} does not perform well for the downdate when \nworking on the preferential attachment model; \nthis behavior reflects what we have seen for the networks USAir97, as--735, \nand Erd\\\"os02,\nwhich are indeed scale--free networks.\n\n\\begin{figure}[t]\n\\centering\n\\caption{Timings in seconds for scale-free graphs of increasing size (500 modifications).} \n\\includegraphics[width=.9\\textwidth]{time_nest_new2.eps}\n\\label{fig:timings_nest}\n\\end{figure}\n\n\n\n\n\\subsection{Timings for synthetic networks}\\label{sec:ltime_synth}\nWe have performed some experiments with synthetic networks of increasing\nsize in order to assess the scalability of the various methods introduced\nin this paper. A sequence of seven adjacency matrices corresponding to\nBarab\\'asi--Albert scale-free graphs was generated using the CONTEST\ntoolbox. The order of the matrices ranges from 1000 to 7000; the average\ndegree is kept constant at 5. A fixed number of modifications ($K=500$)\nwas carried out on each network.\nAll experiments were performed using Matlab Version 7.12.0.635 (R2011a) \non an IBM ThinkPad running Ubuntu 12.04.5 LTS, a 2.5 GHZ Intel Core i5 processor, and \n3.7 GiB of RAM. \nWe used the built-in Matlab function {\\tt eigs} (with the default settings) to\napproximate the dominant eigenvector of the adjacency matrix $A$, \nthe Matlab toolbox {\\tt mmq} \\cite{mmq} to estimate the diagonal \nentries of $e^A$ (with a fixed number of five nodes in the Gauss--Radau\nquadrature rule, hence five Lanczos steps per estimate),\nand the toolbox {\\tt funm\\_kryl} to compute the vector \n$e^A{\\bf 1}$ of total communicabilities, \nalso with the default parameter settings.\n\nThe results are shown in Figure \\ref{fig:timings_nest}. The approximate\n(asymptotic) linear scaling\nbehavior of the various methods (in particular of {\\tt nodeTC.no} and {\\tt eigenvector.no},\nwhich are by far the fastest, see the insets) is clearly displayed \nin these plots. \n\n\n\n\\subsection{Timings for larger networks}\\label{sec:large}\n\nIn Tables \\ref{tab:timings_down}--\\ref{tab:timings_up} we \nreport the timings for various methods \nwhen $K=2000$ downdates and updates are selected for the three largest networks listed \nin Table \\ref{tab:Datasets}. \n\nThe timings presented refer to the selection of the edges to be downdated or updated, which\ndominates the computational effort. For the method {\\tt subgraph.no} in the case\nof downdates, we restricted the search of\ncandidate edges to a subset of $E$ in order to reduce\ncosts. For the three test networks we used $40\\%$, $45\\%$ and $15\\%$ of the nodes,\nrespectively,\nchosen by taking those with lowest eigenvector centrality, and the corresponding\nedges. We found the results to be very close to those obtained working with the\ncomplete set $E$, but at a significantly lower cost (especially for the largest\nnetwork).\n\nThese results clearly show that algorithms\n{\\tt nodeTC.no} and {\\tt eigenvector.no} are orders of magnitude\nfaster than the other methods; method {\\tt subgraph.no}, while significantly\nmore expensive, is still\nreasonably \nefficient\\footnote{It is worth mentioning that in principle it is possible to \ngreatly reduce the cost of this method using parallel processing, since each \nsubgraph centrality can be computed independently of the others.}\nand can be expected to give better results in \nsome cases (e.g.,\non networks with a very small spectral gap). The {\\tt degree} algorithm, on the\nother hand, cannot be recommended in general since it gives somewhat inferior results.\nThe remaining methods {\\tt eigenvector}, {\\tt nodeTC} and {\\tt subgraph} (not\nshown here) are prohibitively expensive for large networks, at least when the\nnumber $K$ of modifications is high (as it is here).\n\n\\begin{table}[t]\n\\footnotesize\n\\centering\n\\caption{Timings in seconds for $K=2000$ downdates performed on the \nthree largest networks in Table \\ref{tab:Datasets}.}\n\\label{tab:timings_down}\n\\begin{tabular}{cccc}\n\\hline\n & ca--HepTh & as--22july06 & usroad--48 \\\\\n\\hline\n{\\tt eigenvector} & 278.13 & 599.83 & 11207.39 \\\\\n{\\tt eigenvector.no}& 0.07 & 1.79 & 4.08 \\\\\n{\\tt nodeTC} & 553.04 & 1234.49 & 2634.27 \\\\\n{\\tt nodeTC.no} & 0.34 & 0.83 & 1.34 \\\\\n{\\tt subgraph.no} & 107.36 & 383.34 & 1774.07 \\\\\n{\\tt degree} & 29.67 & 53.42 & 153.52 \\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\n\\begin{table}[t]\n\\footnotesize\n\\centering\n\\caption{Timings in seconds for $K=2000$ updates performed on the \nthree largest networks in Table \\ref{tab:Datasets}}\n\\label{tab:timings_up}\n\\begin{tabular}{cccc}\n\\hline\n & ca--HepTh & as--22july06 & usroad--48\\\\\n\\hline\n{\\tt eigenvector} & 192.8 & 436.9 & 1599.5 \\\\\n{\\tt eigenvector.no}& 0.19 & 0.33 & 5.85 \\\\\n{\\tt nodeTC} & 561.9 & 1218.8 & 2932. \\\\\n{\\tt nodeTC.no} & 0.30 & 0.55 & 1.59 \\\\\n{\\tt subgraph.no} & 3.13 & 7.20 & 121.4 \\\\\n{\\tt degree} & 11.1 & 12.4 & 175.8 \\\\\n\\hline\n\\end{tabular}\n\\end{table}\n\nWe also observe that downdating is generally a more\nexpensive process than updating, since in the latter case the edges are to be\nchosen among a fairly small subset of all virtual edges, whereas in the downdating process\nwe work on the whole set $E$ of existing edges (or on a large subset of $E$). \nFor some methods the difference in cost becomes significant when\nthe networks are sufficiently large and the number of modifications to be\nperformed is high.\n\nSummarizing, the method labelled {\\tt nodeTC.no} is the fastest and gives excellent\nresults, quite close to those of the more expensive methods, and therefore we can\nrecommend its use for the type of problems considered here. The methods labelled\n{\\tt eigenvector.no} and {\\tt subgraph.no} are also effective and\nmay prove useful in some settings, especially for updating.\n \n \n\n\n\n\\section{Evolution of other connectivity measures}\n\\label{sec:nc+gap}\nIn this section we want to highlight another facet of the methods we \nhave introduced for (approximately) optimizing the total communicability.\nIn particular, we\nlook at the evolution of other network properties under our updating strategies.\nWhen building or modifying a network, there are various \nfeatures that one may want to achieve.\nTypically, there are two main desirable properties: first, the network should\ndo a good job at spreading information, i.e., have a high total communicability;\nsecond, the network should be robust under targeted attacks or random failure, which \nis equivalent to the requirement that it should be difficult to ``isolate'' \nparts of the network, i.e., the network should be ``well connected''.\nThis latter property can be measured by means of various indices. \nOne such measure is the spectral gap $\\lambda_1 - \\lambda_2$. As a consequence\nof the Perron--Frobenius Theorem, adding an edge to a connected network\ncauses the dominant eigenvalue $\\lambda_1$ of $A$ to increase. Test results\n(not shown here) show that when a network is updated using one of our\ntechniques, the first eigenvalue increases rapidly with the number of updates.\nOn the other hand, the second eigenvalue $\\lambda_2$ tends to change little\nwith each update and it may even decrease (recall that the matrix\n$UW^T = {\\bf e}_i{\\bf e}_j^T + {\\bf e}_j{\\bf e}_i^T$ being added to $A$\nin an update is indefinite). Therefore, the spectral gap $\\lambda_1 - \\lambda_2$\nwidens rapidly with the number of updates.\\footnote{This fact, incidentally,\nmay serve as further justification for the effectiveness\nof algorithms like {\\tt nodeTC.no}\nand {\\tt eigenvector.no}.}\n It has been pointed out\nby various\n authors (see, e.g., \\cite{E06b,Puder2013}) that a large spectral gap is typical\nof complex networks with good expansion properties. \n\nHere we focus on a related measure, the so-called {\\em free energy}\n(also known in the literature as {\\em natural connectivity}) of the network.\nIn particular, we investigate the effect of our proposed methods of network updating\non the evolution of this index.\n\n\\subsection{Tracking the free energy (or natural connectivity)}\nIn \\cite{Jun2010} the authors discuss a measure of network connectivity which \nis based on an intuitive notion of robustness and\nwhose analytical expression has a clear meaning and can be derived from the \neigenvalues of $A$; they refer to it as the {\\em natural connectivity}\nof the network (see also \\cite{Wu2012}).\nThe idea underlying this index is that a network is more robust if there exists \nmore than one route to get from one node to another; this property \nensures that if a route becomes unusable, there is an alternative way to get \nfrom the source of information to the target.\nTherefore, intuitively a network is more robust if it has a lot of (apparently) \nredundant routes connecting its vertices \nor, equivalently, if each of its nodes is involved in a lot of closed walks.\nThe natural connectivity aims at quantifying\nthis property by using an existing measure for\nthe total number of closed walks in a graph, namely, the \n{\\em Estrada index} \\cite{Estrada2000}.\nThis index, denoted by $EE(G)$, \nis defined as the trace of the matrix exponential.\nNormalizing this value and taking the natural logarithm, \none obtains the {\\itshape natural connectivity} \nof the graph:\n$$\\overline{\\lambda}(A)=\\ln\\left(\\frac{1}{n}\\sum_{i=1}^ne^{\\lambda_i}\\right)=\\ln(EE(G))-\\ln(n).$$\n\n\n\nIt turns out, however, that essentially the same index was already\npresent in the literature. Indeed, the natural connectivity is only one \nof the possible interpretations\nthat can be given to the logarithm of the (normalized) Estrada index.\nAnother, earlier interpretation was given in \\cite{Estrada2007}, where the authors\nrelated this quantity to the Helmholtz free energy of the network $F=-\\ln\\left(EE(G)\\right)$.\nTherefore, since $\\overline{\\lambda}=-F-\\ln(n)$, the behavior of $F$ completely describes that of\n$\\overline{\\lambda}$ (and conversely) as the graph is modified by adding or removing links.\n\n\nThe natural connectivity has been recently used (see \\cite{Chan2014}) to derive manipulation \nalgorithms that directly optimize this robustness measure. \nIn particular, the updating algorithm introduced in \\cite{Chan2014} appears to be \nsuperior to existing heuristics, such as those proposed in \n\\cite{Beygelzimer2005,Frank1970,Shargel2003}. \nThis algorithm, which costs $O(mt+Kd_{max}^2t+Knt^2)$ where $d_{max}=\\max_{i\\in V}d_i$ \nand $t$ is the (user-defined) number of leading eigenpairs, \nselects $K$ edges to be added to the network\nby maximizing a quantity that involves the elements of the leading $t$ \neigenpairs of $A$.\\footnote{A description of the \nalgorithm can be found in the Supplementary Material.} \n\n \n\nWe have compared our updating techniques with that described in \\cite{Chan2014}.\nResults for four representative networks are shown in Figure \\ref{fig:TCeNC_big}.\nIn our tests, we use the value $t=50$ (as in \\cite{Chan2014}), and we select $K=500$ edges. \nNote that, when $K$ is large, the authors recommend to recompute the set of $t$ \nleading eigenpairs every $l$ iterations. \nThis operation requires an additional effort that our faster methods do not need.\nSince the authors in \\cite{Chan2014} show numerical experiments in which the methods \nwith and without the recomputation return \nalmost exactly the same results, we did not recompute the eigenpairs after \nany of the updates. \n\nFigure \\ref{fig:TCeNC_big} displays the results for both the evolution of the natural \nconnectivity and of the normalized total communicability, where\nthe latter is plotted in a semi--logarithmic scale.\nA total of 500 updates have been performed.\nThe method labelled {\\tt Chan} selects the edges according to\nthe algorithm described in \\cite{Chan2014} choosing from all the virtual edges of the graph. \nFor our methods we used, as before, the virtual edges in the subgraph obtained \nselecting the top $10\\%$ or $20\\%$ of nodes ranked according \nto the eigenvector centrality.\nAs one can easily see, our methods generally outperform the algorithm proposed in \n\\cite{Chan2014}. In particular, {\\tt nodeTC.no} and {\\tt eigenvector.no} \ngive generally better results than {\\tt Chan} and\nare much faster in practice. For instance, the execution time\nwith {\\tt Chan} on the network ca-HepTh was over 531 seconds, and \nmuch higher for the two larger networks.\nWe recall (see Table \\ref{tab:timings_up})\n that the execution times for {\\tt nodeTC.no} and {\\tt eigenvector.no}\nare about three orders of magnitude smaller.\n\n \n\n\\begin{figure}[t]\n\\caption{Evolution of the natural connectivity and of the normalized total \ncommunicability (in a semi--logarithmic scale plot) when up to 500 updates are \nperformed on four real-world networks.}\n\\centering\n\\includegraphics[width=.9\\textwidth]{TCeNC.eps}\n\\label{fig:TCeNC_big}\n\\end{figure}\n\nIt is striking to see how closely the evolution of the natural connectivity \nmirrors the behavior of the normalized total communicability. This is likely\ndue to the fact that both indices depend on the eigenvalues of $A$\n(with a large contribution coming from the terms containing $\\lambda_1$,\nsee (\\ref{tc_spec}) and the subsequent remark), and all the updating strategies used here\ntend to make $\\lambda_1$ appreciably larger.\n\nReturning to the interpretation in terms of statistical physics, \nfrom Figure \\ref{fig:TCeNC_big} we deduce that the free energy of the\ngraph decreases as we add edges to the network.\nIn particular this means that the network is evolving toward a more stable\nconfiguration and, in the limit, toward equilibrium, which is the configuration\nwith maximum entropy.\\footnote{The relation between the free energy and the \nGibbs entropy is described in more detail in the Supplementary Material.}\n\n\n\\begin{comment}\nIndeed, the free energy of the system is related to the Gibbs entropy $S$ as\n$TS=H-F$, \nwhere $T$ is the absolute temperature and $H$ is the total energy of the graph.\nTherefore, the Gibbs entropy,\nwhich measures the effective number of states sharing the same energy,\nincreases as $F$ decreases.\n\\end{comment}\n\n\nThese findings indicate\nthat the normalized total communicability is equally effective an index as\nthe natural connectivity\n(equivalently, the free energy) for the purpose of \ncharacterizing network connectivity.\nSince the network total communicability can be computed very fast (in $O(n)$ time),\nwe believe that the normalized total communicability should be used instead\nof the natural connectivity, especially for large networks. \n\nIndeed, computing the natural connectivity requires evaluating the trace of \n$e^A$; even when stochastic trace estimation is used \\cite{AT11}, this is \nseveral times more expensive, for large networks, than the total communicability. \n\n\n\\begin{comment}\nIndeed, computing\nthe natural connectivity requires evaluating all the diagonal entries of $e^A$ and is\ntherefore significantly more expensive, for large networks, than the total\ncommunicability.\n\\end{comment}\n\n\\section{Conclusions and future work}\n\\label{sec:conclusions}\nIn this paper we have introduced several techniques that can be used \nto modify an existing network so as to obtain networks that are highly \nsparse, and yet have \na large total communicability.\n\nThese heuristics make use of various measures of edge \ncentrality, a few of which have been introduced in this work. \nFar from being {\\em ad hoc}, these heuristics are widely\napplicable and mathematically justified.\nAll our techniques can be implemented using well-established tools from numerical\nlinear algebra: algorithms for eigenvector computation, Gauss-based quadrature\nrules for estimating quadratic forms, and Krylov subspace methods for computing\nthe action of a matrix function on a vector. At bottom, the Lanczos algorithm\nis the main player. High quality, public domain software exists to perform these\nmodifications efficiently.\n\nAmong all the methods introduced here, the best results are obtained by \nthe {\\tt nodeTC.no} and {\\tt eigenvector.no} algorithms, \nwhich are based on the edge total communicability\nand eigenvector centrality, respectively. These methods are extremely fast \nand returned excellent results in virtually all \nthe tests we have performed. For updating networks characterized by a small\nspectral gap, a viable alternative is the algorithm {\\tt subgraph.no}.\nWhile more expensive than {\\tt nodeTC.no} and {\\tt eigenvector.no}, this\nmethod scales linearly with the number of nodes and yields consistently\ngood results.\n\nFinally, we have shown that the total communicability can be effectively \nused as a measure of network connectivity, which plays an important role\nin designing robust networks.\n Indeed, the total communicability does a very good job at quantifying \ntwo related properties of networks: \nthe ease of spreading information, and the extent to which the network is \n``well connected''. Our results show that the total communicability\nbehaves in a manner very similar to the natural connectivity (or free\nenergy) under network\nmodifications, while it can be computed much more quickly.\n\nFuture work should include the extension of these techniques\nto other types of networks,\nincluding directed and weighted ones.\n\n\n\\section*{Acknowledgements}\nWe are grateful to Ernesto Estrada for providing some of the networks \nused in the numerical experiments and for pointing out some useful references.\nThe first author would like to thank Emory University for the hospitality \noffered in 2014, when this work was completed.\nWe also thank two anonymous referees for helpful suggestions.\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction\\label{sec:intro}}\nWith the advent of modern sky surveys that map out significant contiguous fractions of the observable Universe in ever greater detail~(e.g.,~\\cite{6dFGS,BOSS,DES,DESI,EBOSS,EUCLID,LSST,SDSS,SPHEREX,VIPERS,WFIRST}), it has become possible to investigate its least luminous and most extended constituents: cosmic voids, vast regions of relatively empty space. Voids are not only fascinating objects in their own right, they may also hold the keys to resolving some of today's open problems in cosmology, a fact that has come into focus only recently (see references~\\cite{vdWeygaert2009,vdWeygaert2016,Pisani2019} for an overview). Cosmic voids can be thought of pocket universes in which dark energy became important much earlier than elsewhere in the cosmos~\\cite{Bos2012,Pisani2015a,Verza2019}. Making up the bulk of large-scale structure, they play a major role in the formation of its web-like pattern~\\cite{Gregory1978,Zeldovich1982,deLapparent1986,Sheth2004,vdWeygaert2009}. This pattern contains a wealth of information on the fundamental properties of the Universe and voids have been shown to be sensitive probes thereof, such as its initial conditions~\\cite{vdWeygaert1993,Chan2019}, its matter~\\cite{Peebles2001,Nusser2005,Park2007,Lavaux2010,Sutter2012b,Sutter2014b,Hamaus2016,Mao2017} and energy components~\\cite{Granett2008,Biswas2010,Ilic2013,Cai2014,Planck2014,Kovacs2019}. Moreover not only cosmology, but the very nature of gravity can be investigated with voids~\\cite{Clampitt2013,Zivick2015,Cai2015,Achitouv2016,Falck2018,Paillas2019,Perico2019}, because it is gravity that gives rise to their formation and evolution in the first place. This happens via gravitational collapse of initially over-dense regions in the mass distribution into sheets, filaments, and clusters where galaxies form. The remaining space is occupied by voids that are characterized by the coherent flow of (predominantly dark) matter~\\cite{Shandarin2011,Abel2012,Hahn2015}. Baryonic matter is even more scarce inside voids~\\cite{Paillas2017,Pollina2017}, implying a significant advantage in the attempt to model their evolution when compared to the other structure types. This opens up the opportunity to use voids as laboratories for the physics of dark matter~\\cite{Yang2015,Reed2015,Baldi2018} and other elusive particles, such as neutrinos, that freely permeate their interiors~\\cite{Massara2015,Banerjee2016,Kreisch2019,Schuster2019}.\n\nOn the whole, cosmic voids offer radically novel avenues towards probing the fundamental laws of physics that govern our Universe. General Relativity (GR) relates the distribution of matter and energy to the geometry of spacetime via Einstein's field equations. Consequently, observations of the cosmic expansion history allow constraining the material components of the Universe. In this manner supernova distance measurements have inferred the existence of dark energy (in the form of a cosmological constant $\\Lambda$) that dominates the cosmic energy budget today and is responsible for the observed accelerated expansion~\\cite{Riess1998,Perlmutter1999}. Yet, the fundamental nature of dark energy remains mysterious and further efforts are necessary towards explaining its origin. This has been attempted in studying the expansion history by employing standard rulers, such as the Baryon Acoustic Oscillation~(BAO) feature imprinted in the spatial distribution of galaxies on scales of $\\sim105h^{-1}{\\rm Mpc}$~\\cite{Eisenstein2005}. Because the physics of recombination is well understood, the BAO feature can be modeled from first principles and therefore provides a scale of known extent: a standard ruler. Observations of the BAO in the pairwise distribution of galaxies have been successful in constraining the expansion history and so far consistently confirmed the $\\Lambda$CDM paradigm (e.g.,~\\cite{Alam2017,SanchezA2017,Beutler2017a}).\n\nA similar approach can be adopted for objects of known shape: standard spheres. Both methods are based on the cosmological principle, stating the Universe obeys statistical isotropy and homogeneity. A relatively novel technique is the use of cosmic voids in this context. After averaging over all orientations, their shape obeys spherical symmetry, even though individual voids may not~\\cite{Park2007,Platen2008}. Therefore, stacked voids can be considered as standard spheres~\\cite{Ryden1995,Lavaux2012,Sutter2012b,Sutter2014b,Hamaus2015,Hamaus2016,Mao2017} with sizes typically ranging from $10h^{-1}{\\rm Mpc}$ to $100h^{-1}{\\rm Mpc}$. This means that in a finite survey volume one can find a substantially larger number of such spheres than rulers in the form of BAO, allowing a significant reduction of statistical uncertainties and to probe a wider range of scales. Standard spheres can be used to constrain the expansion history: only if the fiducial cosmological model in converting redshifts to distances is correct, stacked voids appear spherically symmetric, a technique known as the Alcock-Paczynski (AP) test~\\cite{Alcock1979}. In principle this test merely involves a trivial rescaling of coordinates. However, in observational data the spherical symmetry is broken by redshift-space distortions (RSD), which are caused by the peculiar motions of galaxies along the line of sight. Therefore, a successful application of the AP test to constrain cosmological parameters from voids crucially relies on the ability to robustly model their associated RSD~\\cite{Ryden1996}. The latter are notoriously complex and difficult to model in the clustering statistics of galaxies, especially on intermediate and small scales, where non-linear clustering and shell crossing occurs. It has been shown that these limitations can be mitigated in voids, which are dominated by a laminar, single-stream flow of matter that is well described even by linear theory~\\cite{Paz2013,Hamaus2014b,Hamaus2014c,Pisani2015b,Hamaus2015,Hamaus2016}. This, and the additional virtue of enabling constraints on the growth rate of structure, has sparked the recent interest for void RSD in the literature~\\cite{Cai2016,Chuang2017,Achitouv2017a,Hawken2017,Hamaus2017,Correa2019,Achitouv2019,Nadathur2019a,Nadathur2019b,Hawken2020}.\n\nIn this paper we present a first cosmological analysis of voids from the combined galaxy sample of the final BOSS~\\cite{BOSS} data. Our model self-consistently accounts for RSD and the AP effect, without the need for any external inputs from simulations or mock catalogs. The detailed derivation of the underlying theory is outlined in section~\\ref{sec:theory}, along with a definition of all relevant observables. Section~\\ref{sec:analysis} presents the observed and simulated data sets considered and our method for the identification and characterization of voids therein. Our analysis pipeline is then validated based on mock data in the first part of section~\\ref{sec:analysis}, the second part is devoted to process the real data. We demonstrate that the AP test with voids offers cosmological constraints that are competitive with other large-scale structure probes. Section~\\ref{sec:discussion} is used to summarize our constraints and to discuss them in the light of previous works on voids (see figure~\\ref{fig:comparison}), representing the strongest such constraints in the literature. Finally, we draw our conclusions in section~\\ref{sec:conclusion}.\n\n\n\\section{Theory \\label{sec:theory}}\n\n\\subsection{Dynamic distortion \\label{subsec:dynamic}}\nIn cosmology, our observables are the redshifts $z$ and angular sky coordinates $\\boldsymbol{\\theta}=(\\vartheta,\\varphi)$ of an astronomical object. The comoving distance of this object is defined as\n\\begin{equation}\n\\chi_\\parallel(z)=\\int_0^z\\frac{c}{H(z')}\\mathrm{d}z'\\;,\n\\label{chi_par}\n\\end{equation}\nwhere $H(z)$ is the Hubble rate and $c$ the speed of light. The observed redshift $z$ can contain contributions from many different physical effects, but the most important ones are the cosmological Hubble expansion $z_h$ and the Doppler effect $z_d$. The total observed redshift $z$ is then given by~\\cite{Davis2014}\n\\begin{equation}\n1+z = (1+z_h)(1+z_d)\\;. \\label{z_tot}\n\\end{equation}\nThe Doppler effect is caused by peculiar motions along the line of sight, $z_d=v_\\parallel\/c$. Because $z_d$ is typically small compared to $z_h$, we can write\n\\begin{equation}\n\\chi_\\parallel(z)\\simeq\\chi_\\parallel(z_h) + \\frac{c(1+z_h)}{H(z_h)}z_d\\;.\n\\label{chi_rsd}\n\\end{equation}\nThe transverse comoving distance for an observed angle $\\theta\\equiv|\\boldsymbol{\\theta}|$ on the sky is defined as\n\\begin{equation}\n\\chi_\\perp(z) = D_\\mathrm{A}(z)\\,\\theta\\;,\n\\label{chi_per}\n\\end{equation}\nwhere the comoving angular diameter distance is given by\n\\begin{equation} D_\\mathrm{A}(z) = \\frac{c}{H_0\\sqrt{-\\Omega_\\mathrm{k}}}\\sin\\left(\\frac{H_0\\sqrt{-\\Omega_\\mathrm{k}}}{c}\\chi_\\parallel(z)\\right)\\;,\n\\label{D_A}\n\\end{equation}\nwith the Hubble constant $H_0\\equiv H(z=0)$ and present-day curvature parameter $\\Omega_\\mathrm{k}$. In a flat universe with $\\Omega_\\mathrm{k}=0$, equation~(\\ref{D_A}) reduces to $D_\\mathrm{A}(z)=\\chi_\\parallel(z)$. Now, given the observed coordinates $(z,\\vartheta,\\varphi)$, we can transform to the comoving space vector $\\mathbf{x}$ via\n\\begin{equation}\n\\mathbf{x}(z,\\vartheta,\\varphi) = D_\\mathrm{A}(z)\\begin{pmatrix}\\cos\\vartheta\\cos\\varphi\\\\\\sin\\vartheta\\cos\\varphi\\\\\\sin\\varphi\\end{pmatrix}\\;,\n\\label{x_comoving}\n\\end{equation}\nwhere $D_\\mathrm{A}(z)\\simeq D_\\mathrm{A}(z_h)+cz_d(1+z_h)\/H(z_h)$, analogously to equation~(\\ref{chi_rsd}). Hence, using $z_d=v_\\parallel\/c$, we can write\n\\begin{equation}\n\\mathbf{x}(z) \\simeq \\mathbf{x}(z_h) + \\frac{1+z_h}{H(z_h)}\\mathbf{v}_\\parallel\\;,\n\\label{x_rsd}\n\\end{equation}\nwhere $\\mathbf{v}_\\parallel$ is the component of the velocity vector $\\mathbf{v}$ along the line-of-sight direction. We describe the location and motion of tracers by vectors in comoving space, upper-case letters are used for void centers, lower-case letters for galaxies. The observer's location is chosen to be at the origin of our coordinate system, the void center position is denoted by $\\mathbf{X}$ with redshift $Z$ and the galaxy position by $\\mathbf{x}$ with redshift $z$. The redshift $Z$ of the void center is not a direct observable, but it is constructed via the redshifts of all the surrounding galaxies that define it (see section~\\ref{subsec:voids}). Moreover, we pick the direction of the void center as our line of sight, i.e. $\\mathbf{X}\/|\\mathbf{X}|$, and adopt the distant-observer approximation, assuming that $\\mathbf{x}$ and $\\mathbf{X}$ are parallel.\n\n\\begin{figure}[t]\n\t\\centering\n\t\\resizebox{\\hsize}{!}{\n\t\t\\includegraphics[trim= 0 0 0 40]{fig\/VoidStretch.pdf}}\n\t\\caption{Separation vector between the comoving void center location $\\mathbf{X}$ and the galaxy location $\\mathbf{x}$ in real space ($\\mathbf{r}$, left) and in redshift space ($\\mathbf{s}$, right). The peculiar line-of-sight velocity $\\mathbf{v}_\\parallel$ of every galaxy that defines the void can be decomposed into the peculiar velocity of the void center $\\mathbf{V}_\\parallel$ and the galaxy's relative velocity $\\mathbf{u}_\\parallel$ with respect to this center. For simplicity, the illustration displays $\\mu$ instead of $\\cos^{-1}(\\mu)$ to indicate line-of-sight angles and shows velocity displacements in units of $(1+z_h)\/H(z_h)$. This yields the relation $\\mathbf{s}=\\mathbf{r}+\\mathbf{u}_\\parallel$ between real- and redshift-space separations.}\n\t\\label{fig:voidstretch}\n\\end{figure}\n\nLet us first consider real space, where the Doppler effect is neglected ($z_d=0$) and hence $\\mathbf{x}(z)=\\mathbf{x}(z_h)$. The vector $\\mathbf{r}\\equiv\\mathbf{x}-\\mathbf{X}$ connects the two positions at a comoving distance of $r=|\\mathbf{r}|$. Similarly, we define the relative velocity $\\mathbf{u}$ between a void center of velocity $\\mathbf{V}$ and a galaxy of velocity $\\mathbf{v}$ as $\\mathbf{u}\\equiv\\mathbf{v}-\\mathbf{V}$. Now, if we consider redshift space with $z_d\\ne0$ and use equation~(\\ref{x_rsd}), the separation vector between void center and galaxy becomes\n\\begin{equation}\n\\mathbf{x}(z)-\\mathbf{X}(Z) \\simeq \\mathbf{x}(z_h)-\\mathbf{X}(Z_h) + \\frac{1+z_h}{H(z_h)}\\left(\\mathbf{v}_\\parallel-\\mathbf{V}_\\parallel\\right) = \\mathbf{r} + \\frac{1+z_h}{H(z_h)}\\mathbf{u}_\\parallel \\equiv \\mathbf{s}\\;.\n\\label{s(r)}\n\\end{equation}\nHere we have used the approximation $z_h\\simeq Z_h$ for the Doppler term, which is accurate to $\\mathcal{O}(10^{-3})$ on scales of $r\\sim\\mathcal{O}(10^{1})h^{-1}{\\rm Mpc}$ and velocities of $u_\\parallel\\sim\\mathcal{O}(10^2)\\frac{\\rm km}{\\rm s\\,Mpc}$. This approximation becomes exact when we consider the average of this vector over many different voids -- a so-called void stack. In that case the cosmological principle ensures statistical homogeneity and isotropy such that $\\langle\\mathbf{V}\\rangle=\\langle\\mathbf{V}_\\parallel\\rangle=0$, since there is no preferred observer (or void, for that matter) in the Universe. In other words: the average void moves with the background Hubble flow. Thus, the vector $\\mathbf{s}$, which connects void centers and galaxies in redshift space, does not depend on the individual motions of galaxies or void centers, but only on their relative velocity $\\mathbf{u}_\\parallel$ along the line of sight. Of course this only applies to those galaxies that are part of the same void, not to the relative velocities of galaxies that are associated with distinct voids at larger separation (see~\\cite{Hamaus2014a,Chan2014,Hamaus2014c,Liang2016,Chuang2017,Voivodic2020} for an account on large-scale void-galaxy cross-correlations and~\\cite{Sutter2014c,Ruiz2015,Lambas2016,Ceccarelli2016,Wojtak2016,Lares2017b} on the motions and pairwise velocity statistics of voids). An illustration of this is shown in figure~\\ref{fig:voidstretch}: voids experience a translation and a deformation between real and redshift space, but the translational component does not enter in the separation vector $\\mathbf{s}$ for galaxies that belong to the void. As long as voids can be considered as coherent extended objects, their centers move along with the galaxies that define them from real to redshift space. This property distinguishes voids from galaxies, which are typically treated as point-like particles in the context of large-scale structure. The same reasoning applies to galaxy clusters, the overdense counterparts of voids: the center of mass is defined by the cluster member galaxies, which are observed in redshift space. Their relative (virial) motion with respect to the center of mass results in an elongated stacked cluster shape, irrespective of the movement of the entire cluster~\\cite{Croft1999,Zu2013,Marulli2017,Farahi2016,Cai2017b}.\n\n\\subsection{Geometric distortion \\label{subsec:geometric}}\nGiven the observed angular sky-coordinates and redshifts of galaxies and void centers, the comoving distance $s = (s_\\parallel^2 + s_\\perp^2)^{1\/2}$ in redshift space between any void-galaxy pair is determined by equations~(\\ref{chi_par}) and~(\\ref{chi_per}),\n\\begin{equation}\ns_\\parallel = \\frac{c}{H(z)}\\delta z\\quad\\mathrm{and}\\quad s_\\perp = D_\\mathrm{A}(z)\\delta\\theta\\;, \\label{comoving}\n\\end{equation}\nwhere $\\delta z$ and $\\delta\\theta$ are the redshift and angular separation of the pair, respectively. This transformation requires the Hubble rate $H(z)$ and the comoving angular diameter distance $D_\\mathrm{A}(z)$ as functions of redshift, so a particular cosmological model has to be adopted. We assume a flat $\\Lambda$CDM cosmology with\n\\begin{equation}\nH(z) = H_0\\sqrt{\\Omega_\\mathrm{m}(1+z)^3+\\Omega_\\Lambda}\\;, \\label{H(z)}\n\\end{equation}\nwhere $\\Omega_\\mathrm{m}$ and $\\Omega_\\Lambda=1-\\Omega_\\mathrm{m}$ are today's density parameters for matter and a cosmological constant, respectively. For the low redshift range considered in this paper, we can neglect the radiation density for all practical purposes. Given a cosmology with parameters $\\boldsymbol{\\Omega}$ we can now convert angles and redshifts into comoving distances according to equation~(\\ref{comoving}). However, the precise value of these parameters is unknown, as is the underlying cosmology, so we can only estimate the true $H(z)$ and $D_\\mathrm{A}(z)$ with a fiducial model and parameter set $\\boldsymbol{\\Omega}'$, resulting in the estimated distances\n\\begin{equation}\ns_\\parallel' = \\frac{H(z)}{H'(z)}s_\\parallel\\equiv q_\\parallel^{-1} s_\\parallel\\;\\mathrm{,}\\qquad s_\\perp' = \\frac{D_\\mathrm{A}'(z)}{D_\\mathrm{A}(z)}s_\\perp\\equiv q_\\perp^{-1} s_\\perp\\;,\n\\end{equation}\nwhere the primed quantities are evaluated in the fiducial cosmology and $(q_\\parallel, q_\\perp)$ are defined as the ratios between true and fiducial values of $H^{-1}(z)$ and $D_\\mathrm{A}(z)$, respectively~\\cite{SanchezA2017}. Therefore, both magnitude and direction of the separation vector $\\mathbf{s}$ may differ from the truth when a fiducial cosmology is assumed. Defining the cosine of the angle between $\\mathbf{s}$ and the line of sight $\\mathbf{X}\/|\\mathbf{X}|$ as\n\\begin{equation}\n\\mu_s\\equiv\\frac{\\mathbf{s}\\cdot\\mathbf{X}}{|\\mathbf{s}||\\mathbf{X}|}=\\frac{s_\\parallel}{s}\\;,\n\\label{mu_s}\n\\end{equation}\none can obtain the true $s$ and $\\mu_s$ from the fiducial $s'$ and $\\mu_s'$ via\n\\begin{gather}\ns = \\sqrt{q_\\parallel^2 s_\\parallel'^2+q_\\perp^2s_\\perp'^2} = s'\\mu_s'q_\\parallel\\sqrt{1+\\varepsilon^2(\\mu_s'^{-2}-1)}\\;,\n\\label{s_fid}\n\\\\\n\\mu_s = \\frac{\\mathrm{sgn}(\\mu_s')}{\\sqrt{1+\\varepsilon^2(\\mu_s'^{-2}-1)}}\\;,\n\\label{mu_s_fid}\n\\end{gather}\nwhere\n\\begin{equation}\n\\varepsilon \\equiv \\frac{q_\\perp}{q_\\parallel} = \\frac{D_\\mathrm{A}(z)H(z)}{D_\\mathrm{A}'(z)H'(z)}\\;.\n\\label{epsilon}\n\\end{equation}\nIf the fiducial cosmology agrees with the truth, $\\varepsilon=q_\\parallel=q_\\perp=1$ and $s=s'$, $\\mu_s=\\mu_s'$. Conversely, if one of these parameters is measured to be different from unity, one may iteratively vary the fiducial cosmology until the true parameter values $\\boldsymbol{\\Omega}$ are found. As apparent from equations~(\\ref{s_fid}) and~(\\ref{mu_s_fid}), absolute distances $s$ in redshift space depend on both $q_\\parallel$ and $q_\\perp$, whereas angles $\\mu_s$ merely depend on their ratio~$\\varepsilon$. Exploiting the spherical symmetry of stacked voids via the AP effect therefore constrains $\\varepsilon$, but $q_\\parallel$ and $q_\\perp$ remain degenerate without calibration of $s$ with a known scale (such as the BAO scale, for example). However, void-centric distances are typically expressed in units of the effective void radius $R$, which is defined via the cubic root of the void volume in redshift space (see section~\\ref{subsec:voids}). The observed volume is proportional to $s'_\\parallel s'^2_\\perp$, implying $R=q_\\parallel^{1\/3}q_\\perp^{2\/3}R'$ to relate true with fiducial void radii. Then, the ratio $s\/R$ only depends on $\\varepsilon$, as it is the case for $\\mu_s$,\n\\begin{equation}\n\\frac{s}{R}=\\frac{s'}{R'}\\mu_s'\\varepsilon^{-2\/3}\\sqrt{1+\\varepsilon^2(\\mu_s'^{-2}-1)}\\;.\n\\label{s_R_fid}\n\\end{equation} \n\n\n\\subsection{Void-galaxy cross-correlation function \\label{subsec:correlation}}\nThe probability of finding a galaxy at comoving distance $r$ from a void center in real space is given by $1+\\xi(r)$, where $\\xi(r)$ is the void-galaxy cross-correlation function. Due to statistical isotropy, it only depends on the magnitude of the separation vector $\\mathbf{r}$, not its orientation. This is no longer the case in redshift space, where peculiar motions break isotropy via the Doppler effect. However, since this causes RSD exclusively along the line-of-sight direction, we can eliminate their impact by projecting the correlation function onto the plane of the sky. This yields the projected correlation function $\\xi_p$,\n\\begin{equation}\n1+\\xi_p(r_\\perp) = \\frac{\\int\\left[1+\\xi(r)\\right]\\mathrm{d}r_\\parallel}{\\int\\mathrm{d}r_\\parallel} = \\frac{\\int\\left[1+\\xi^s(\\mathbf{s})\\right]\\mathrm{d}s_\\parallel}{\\int\\mathrm{d}s_\\parallel} = 1+\\xi^s_p(s_\\perp)\\;,\n\\label{xi_p}\n\\end{equation}\nwhere $r=(r_\\parallel^2+r_\\perp^2)^{1\/2}$ and $\\xi^s(\\mathbf{s})$ is the redshift-space correlation function, which can now be expressed as\n\\begin{equation}\n1+\\xi^s(\\mathbf{s}) = \\left[1+\\xi(r)\\right]\\frac{\\mathrm{d}r_\\parallel}{\\mathrm{d}s_\\parallel}\\;.\n\\label{xi^s}\n\\end{equation}\nEquation~(\\ref{s(r)}) provides the relation between $\\mathbf{s}$ and $\\mathbf{r}$. In particular, its line-of-sight component can be obtained via taking the dot product with $\\mathbf{X}\/|\\mathbf{X}|$,\n\\begin{equation}\ns_\\parallel = r_\\parallel + \\frac{1+z_h}{H(z_h)}u_\\parallel\\;,\n\\label{s_par(r_par)}\n\\end{equation}\nand hence\n\\begin{equation}\n\\frac{\\mathrm{d}r_\\parallel}{\\mathrm{d}s_\\parallel} = \\left(1 + \\frac{1+z_h}{H(z_h)}\\,\\frac{\\mathrm{d}u_\\parallel}{\\mathrm{d}r_\\parallel}\\right)^{-1}\\;.\n\\label{dr_par\/ds_par}\n\\end{equation}\nThe relative peculiar velocity $\\mathbf{u}$ between void centers and their surrounding galaxies can be derived by imposing local mass conservation. At linear order in the matter-density contrast~$\\delta$ and assuming spherical symmetry in real space, the velocity field is given by~\\cite{Peebles1980}\n\\begin{equation}\n\\mathbf{u}(\\mathbf{r}) = -\\frac{f(z_h)}{3}\\frac{H(z_h)}{1+z_h}\\Delta(r)\\,\\mathbf{r}\\;,\n\\label{u(r)}\n\\end{equation}\nwhere $f(z)\\equiv-\\frac{\\mathrm{d}\\!\\ln D(z)}{\\mathrm{d}\\!\\ln(1+z)}$ is the linear growth rate, defined as the logarithmic derivative of the linear growth factor $D(z)$ with respect to the scale factor. In $\\Lambda$CDM, the linear growth rate is well approximated by\n\\begin{equation}\nf(z)=\\left[\\frac{\\Omega_\\mathrm{m}(1+z)^3}{H^2(z)\/H_0^2}\\right]^\\gamma,\n\\label{growth_rate}\n\\end{equation}\nwith a growth index of $\\gamma\\simeq0.55$~\\cite{Lahav1991,Linder2005}. Furthermore, $\\Delta(r)$ is the average matter-density contrast inside a spherical region of comoving radius~$r$,\n\\begin{equation}\n\\Delta(r) = \\frac{3}{r^3}\\int_0^r\\delta(r')r'^2\\,\\mathrm{d}r'\\;. \\label{Delta(r)}\n\\end{equation}\nAlthough the matter-density contrast in the vicinity of void centers is not necessarily in the linear regime (i.e., $|\\delta|\\ll1$), contrary to over-dense structures (such as galaxy clusters and their dark matter halos) it is bounded from below by the value of $-1$. In simulations it has been shown that equation~(\\ref{u(r)}) provides an extremely accurate description of the local velocity field in and around most voids~\\cite{vdWeygaert1993,Hamaus2014b}. While peculiar velocities at the void boundaries can be due to very non-linear structures, spherical averaging over large sample sizes helps to restore the validity of linear theory to a high degree. Only the smallest and most underdense voids exhibit a non-linear behavior close to their centers and may in fact collapse anisotropically under their external mass distribution~\\cite{vdWeygaert1993,Sheth2004}. We can now evaluate the derivative term in equation~(\\ref{dr_par\/ds_par}) as\n\\begin{equation}\n\\frac{1+z_h}{H(z_h)}\\frac{\\mathrm{d}u_\\parallel}{\\mathrm{d}r_\\parallel} = -\\frac{f(z_h)}{3}\\Delta(r)-f(z_h)\\mu_r^2\\left[\\delta(r)-\\Delta(r)\\right]\\;,\n\\label{du_par\/dr_par}\n\\end{equation}\nwhere $\\mu_r=r_\\parallel\/r$ and the identity $\\frac{\\mathrm{d}\\Delta(r)}{\\mathrm{d}r} = \\frac{3}{r}\\left[\\delta(r)-\\Delta(r)\\right]$ was used. Plugging this back into equation~(\\ref{xi^s}) we obtain\n\\begin{equation}\n1+\\xi^s(\\mathbf{s}) = \\frac{1+\\xi(r)}{1-\\frac{f}{3}\\Delta(r)-f\\mu_r^2\\left[\\delta(r)-\\Delta(r)\\right]}\\;.\n\\label{xi^s_nonlin}\n\\end{equation}\nIn order to evaluate this equation at a given observed separation $\\mathbf{s}$, we make use of equations~(\\ref{s_par(r_par)}) and (\\ref{u(r)}),\n\\begin{equation}\nr_\\parallel = \\frac{s_\\parallel}{1 - \\frac{f}{3}\\Delta(r)}\\;,\n\\label{r_par(s_par)}\n\\end{equation}\nand calculate $r=(r_\\parallel^2+r_\\perp^2)^{1\/2}$ with $r_\\perp=s_\\perp$. However, equation~(\\ref{r_par(s_par)}) already requires knowledge of $r$ in the argument of $\\Delta(r)$, so it can only be evaluated by iteration. We therefore start with using $\\Delta(s)$ as initial step, and iteratively calculate $r_\\parallel$ and $\\Delta(r)$ until convergence is reached. In practice we find $5$ iterations to be fully sufficient for that purpose.\n\nFurthermore, in equation~(\\ref{xi^s_nonlin}) both the void-galaxy cross-correlation function $\\xi(r)$, as well as the void-matter cross-correlation function $\\delta(r)$ are required in real space. The former can be obtained via deprojection of equation~(\\ref{xi_p}),\n\\begin{equation}\n\\xi(r) = -\\frac{1}{\\pi}\\int_r^\\infty\\frac{\\mathrm{d}\\xi^s_p(s_\\perp)}{\\mathrm{d}s_\\perp}\\frac{\\mathrm{d}s_\\perp}{\\sqrt{s_\\perp^2-r^2}}\\;,\n\\label{xi_d}\n\\end{equation}\nmaking use of the inverse Abel transform~\\cite{Pisani2014,Hawken2017}. The latter function $\\delta(r)$, also referred to as the void density profile, is not directly observable. However, it has been investigated in $N$-body simulations and can be inferred via the gravitational lensing effect in imaging surveys~\\cite{Krause2013,Higuchi2013,Melchior2014}. The parametric form suggested in reference~\\cite{Hamaus2014b} (HSW profile) has been shown to accurately describe both simulated~\\cite{Sutter2014a,Hamaus2015,Barreira2015,Falck2018,Pollina2017,Perico2019}, as well as observational data~\\cite{Hamaus2016,SanchezC2017,Pollina2019,Fang2019},\n\\begin{equation}\n\\delta_{\\scriptscriptstyle\\mathrm{HSW}}(r) = \\delta_c\\frac{1-(r\/r_s)^\\alpha}{1+(r\/R)^\\beta}\\;. \\label{HSW}\n\\end{equation}\nHere $R$ is the effective void radius, the scale radius $r_s$ determines where $\\delta_{\\scriptscriptstyle\\mathrm{HSW}}(r_s)=0$, the central underdensity is defined as $\\delta_c\\equiv\\delta_{\\scriptscriptstyle\\mathrm{HSW}}(r=0)$, and the power-law indices $\\alpha$ and $\\beta$ control the inner and outer slopes of the profile. In equation~(\\ref{xi^s_nonlin}) these quantities can then be included as free parameters to be constrained by the observed $\\xi^s(\\mathbf{s})$. This approach has been pursued in the framework of the Gaussian streaming model (GSM)~\\cite{Hamaus2015,Hamaus2016}, which incorporates an additional parameter for the velocity dispersion $\\sigma_v$ of galaxies. In the limit of $\\sigma_v\\rightarrow0$, the GSM recovers the result of equation~(\\ref{xi^s_nonlin}) at linear order in $\\delta$~\\cite{Cai2016}. We note that equation~(\\ref{HSW}) describes the spherically averaged density profile with respect to the void center, but a similar parametrization exists for profiles centered on the void boundary~\\cite{Cautun2016}.\n\nAnother option to constrain the void density profile $\\delta(r)$ is through its relation to $\\xi(r)$, which is equivalent to the void density profile in galaxies. Both simulations~\\cite{Pollina2017,Ronconi2019,Contarini2019} and observational approaches~\\cite{Pollina2019,Fang2019} have established robust evidence for the relationship between $\\delta(r)$ and $\\xi(r)$ to be a linear one, such that\n\\begin{equation}\n\\xi(r) = b\\delta(r)\\;,\n\\label{xi(delta)}\n\\end{equation}\nwith a single proportionality constant $b$. This is similar to the relation between the overdensity of tracers and the underlying matter distribution on large scales, where $|\\delta|\\ll1$~\\cite{Desjacques2018}. That condition is not necessarily satisfied in the interiors and immediate surroundings of voids, and the large-scale linear bias does not coincide with the value of $b$ in general, even for the same tracer population. However, the two bias values approach each other for voids of increasing size, and converge in the limit of large effective void radius $R$~\\cite{Pollina2017,Pollina2019,Contarini2019}. Using equation~(\\ref{xi(delta)}) for $\\delta(r)$, we can simply exchange it with $\\xi(r)$ by making the replacements $f\\rightarrow f\/b$ and $\\Delta(r)\\rightarrow\\overline{\\xi}(r)$, with\n\\begin{equation}\n\\overline{\\xi}(r) = \\frac{3}{r^3}\\int_0^r\\xi(r')r'^2\\mathrm{d}r'\\;.\n\\label{xibar}\n\\end{equation}\nNow equation~(\\ref{xi^s_nonlin}) can be written as\n\\begin{equation}\n1+\\xi^s(\\mathbf{s}) = \\frac{1+\\xi(r)}{1-\\frac{1}{3}\\frac{f}{b}\\overline{\\xi}(r)-\\frac{f}{b}\\mu_r^2\\left[\\xi(r)-\\overline{\\xi}(r)\\right]}\\;.\n\\label{xi^s_nonlin2}\n\\end{equation}\nMoreover, we can expand this to linear order in $\\delta$ (or equivalently, $\\xi$) for consistency with the perturbative level of the mass conservation equation~(\\ref{u(r)})~\\cite{Cai2016,Hamaus2017},\n\\begin{equation}\n\\xi^s(\\mathbf{s}) \\simeq \\xi(r) + \\frac{1}{3}\\frac{f}{b}\\overline{\\xi}(r) + \\frac{f}{b}\\mu_r^2\\left[\\xi(r)-\\overline{\\xi}(r)\\right]\\;.\n\\label{xi^s_lin}\n\\end{equation}\nThe function $\\xi^s(\\mathbf{s})$ can be decomposed into independent multipoles via\n\\begin{equation}\n\\xi^s_\\ell(s) = \\frac{2\\ell+1}{2}\\int\\limits_{-1}^1\\xi^s(s,\\mu_s)\\mathcal{L}_\\ell(\\mu_s)\\mathrm{d}\\mu_s\\;,\n\\label{multipoles}\n\\end{equation}\nwith the Legendre polynomials $\\mathcal{L}_\\ell(\\mu_s)$ of order $\\ell$. For equation~(\\ref{xi^s_lin}) the integral can be performed analytically and the only non-vanishing multipoles at linear order in $\\xi$ and $\\overline{\\xi}$ are the monopole ($\\ell=0$) and quadrupole ($\\ell=2$) with\n\\begin{eqnarray}\n\\xi^s_0(s) &=& \\left(1+\\frac{f\/b}{3}\\right)\\xi(r)\\;,\\label{xi_0} \\\\\n\\xi^s_2(s) &=& \\frac{2f\/b}{3}\\left[\\xi(r)-\\overline{\\xi}(r)\\right]\\;.\\label{xi_2}\n\\end{eqnarray}\nThis can be recast into the following form~\\cite{Cai2016,Hamaus2017},\n\\begin{equation}\n\\xi^s_0(s) - \\overline{\\xi}^s_0(s) = \\xi^s_2(s)\\frac{3+f\/b}{2f\/b}\\;,\n\\label{xi_0_2}\n\\end{equation}\nproviding a direct link between monopole and quadrupole in redshift space without reference to any real-space quantity. However, note that equations~(\\ref{xi_0}),~(\\ref{xi_2}) and (\\ref{xi_0_2}) only hold for the case of $\\varepsilon=1$, and multipoles of higher order can be generated via geometric distortions when assuming a fiducial cosmology that is different from the truth, as discussed in section~\\ref{subsec:geometric}.\n\n\n\\section{Analysis \\label{sec:analysis}}\n\n\\subsection{BOSS galaxies and mocks \\label{subsec:galaxies}}\nWe consider galaxy catalogs from the final data release 12 (DR12) of the SDSS-III~\\cite{Eisenstein2011} Baryon Oscillation Spectroscopic Survey (BOSS)~\\cite{Dawson2013}. In particular, we make use of the combined sample of the individual target selections denoted as LOWZ and CMASS~\\cite{Reid2016}. With a total sky area of about $10\\,000$ square degrees from both the northern and southern Galactic hemispheres the sample contains $1\\,198\\,006$ galaxies in a redshift range of $0.20 N_\\mathrm{s}\\left[\\frac{4\\pi}{3}n(z)\\right]^{-1\/3}\\;,\n\\label{ats}\n\\end{equation}\nwhere $n(z)$ is the number density of tracers at redshift $z$ and $N_\\mathrm{s}$ determines the minimum considered void size in units of the average tracer separation. The smaller $N_\\mathrm{s}$, the larger the contamination by spurious voids that may arise from Poisson fluctuations~\\cite{Neyrinck2008,Cousinou2019}. This cut also preferentially removes voids that may have been misidentified due to RSDs~\\cite{Pisani2015b}. As a default we assume a conservative value of $N_\\mathrm{s}=4$, which yields a minimum effective void radius of $R=34.9h^{-1}{\\rm Mpc}$ in our catalog. We note that this criterion depends on the specific tracer sample considered for void identification. It is known that Poisson point processes exhibit highly non-Gaussian Voronoi cell volume distributions~\\cite{vdWeygaert2009}, which can cause spurious void detections. Reference~\\cite{Cousinou2019} finds a very low contamination fraction of spurious voids in the BOSS DR12 CMASS sample based on a multivariate analysis of void properties in training and test samples. This is attributed to the relatively high clustering bias of CMASS galaxies, which is very similar to the combined BOSS sample used here.\n\nThe top of figure~\\ref{fig:box} presents a three-dimensional view of the selected void centers from the northern (right) and southern (left) Galactic hemispheres in comoving space, with the observer located at the origin. Below it, a narrow slice of about one degree in declination within the northern Galactic hemisphere visualizes the distribution of void centers together with their tracer galaxies. Despite the sparsity of the BOSS DR12 combined sample, intricate features of the cosmic web-like structure become apparent. Note that due to the extended three-dimensional geometry of voids, their centers do not necessarily intersect with the slice, leaving some seemingly empty regions without associated void center. The left panel of figure~\\ref{fig:nz} shows the redshift distribution of galaxies, randoms, and voids from the data. For visibility, we have rescaled the total number of randoms to the number of galaxies (by a factor of $50$). Voids are roughly two orders of magnitude scarcer than galaxies, but their redshift distribution follows a similar trend. This is because higher tracer densities allow the identification of smaller voids, as expected from simulation studies~\\cite{Jennings2013,Sutter2014a,Chan2014,Wojtak2016}. The right panel of figure~\\ref{fig:nz} shows the distribution of effective void radii with Poisson error bars, also known as the void-size function. The latter is a quantity of interest for cosmology on its own~\\cite{Pisani2015a,Ronconi2019,Contarini2019,Verza2019}, but in this paper we do not investigate it for that purpose any further, it is shown here only as supplementary information. We repeat the void finding procedure on each of the PATCHY mocks, allowing us to scale down all statistical uncertainties by roughly a factor of $\\sqrt{N_\\mathrm{m}}$, where $N_\\mathrm{m}$ is the number of mock catalogs considered.\n\\begin{figure}[t]\n\t\\centering\n\t\\resizebox{\\hsize}{!}{\n\t\t\\includegraphics[trim=0 -3 0 0]{fig\/nz.pdf}\n\t\t\\includegraphics{fig\/n_rv.pdf}}\n\t\\caption{LEFT: Number density of galaxies, randoms (scaled to the galaxy density), and \\textsc{vide} voids as a function of redshift in the BOSS DR12 combined sample. RIGHT: Number density of all \\textsc{vide} voids as a function their effective radius (void-size function, only shown for illustration).}\n\t\\label{fig:nz}\n\\end{figure}\n\n\\subsection{Estimators \\label{subsec:estimators}}\nWe need to define an estimator to measure the observed void-galaxy cross-correlation function $\\xi^s(\\mathbf{s})$ in redshift space. In order to take into account the survey geometry, we make use of a random catalog that samples the masked survey volume without intrinsic clustering. We adopt the expression derived in reference~\\cite{Hamaus2017},\n\\begin{equation}\n\\xi^s(\\mathbf{s}) = \\frac{\\langle\\mathbf{X},\\mathbf{x}\\rangle(\\mathbf{s})}{\\langle\\mathbf{X}\\rangle\\langle\\mathbf{x}\\rangle} - \\frac{\\langle\\mathbf{X},\\mathbf{x}_r\\rangle(\\mathbf{s})}{\\langle\\mathbf{X}\\rangle\\langle\\mathbf{x}_r\\rangle}\\;,\n\\label{estimator}\n\\end{equation}\nwith the void center, galaxy, and random positions $\\mathbf{X}$, $\\mathbf{x}$, and $\\mathbf{x}_r$, respectively. Here, the angled brackets with two vectors represent the average pair count between objects at separation $\\mathbf{s}$, whereas the brackets with only one vector indicate the mean number count of an object in the corresponding redshift slice. This estimator is a limiting case of Landy \\& Szalay~\\cite{Landy1993} and has been validated using mock data on the relevant scales for voids in BOSS data~\\cite{Hamaus2017}. We will consider its two-dimensional variant $\\xi^s(s_\\parallel,s_\\perp)$, with explicit dependence on distances along and perpendicular to the line of sight, as well as its multipoles $\\xi^s_\\ell(s)$ with $\\ell=(0,2,4)$, i.e. monopole, quadrupole and hexadecapole. Calculating the multipoles is particularly simple with this estimator, because it allows the direct application of equation~(\\ref{multipoles}) on the pair counts by using Legendre polynomials as weights in the integral without the need to define bins in $\\mu_s$~\\cite{Hamaus2017}. The mean number counts in the denominator of equation~(\\ref{estimator}) can be pulled outside the integral, as they do not depend on $\\mathbf{s}$. This way the estimation of multipoles becomes more accurate, especially at small separations $s$ and when $\\mu_s$ approaches one, where the binning in both quantities inevitably results in a coarse spatial resolution~\\cite{Cai2016}. We have explicitly compared this estimator with other common choices in the literature~\\cite{Vargas2013} and refer the reader to section~\\ref{subsec:systematics} for more details on this.\n\nWatershed voids exhibit an angle-averaged density profile of universal character, irrespective of their absolute size~\\cite{Hamaus2014b,Sutter2014a,Cautun2016}. In order to capture this unique characteristic in the two-point statistics of a void sample with a broad range of effective radii, it is beneficial to express all separations in units of $R$ (see section~\\ref{subsec:comparison}). When this is done for every void individually in the estimation of $\\xi^s$, it is commonly denoted as a void stack. Like the majority of void-related studies in the literature, we adopt this approach in our analysis and refer to $\\xi^s(\\mathbf{s}\/R)$ as the stacked void-galaxy cross-correlation function. For simplicity, we will omit this explicit notation in the following and bear in mind that all separations $\\mathbf{s}$ are expressed in units of~$R$.\n\nThe degree of uncertainty in a measurement of the void-galaxy cross-correlation function can be quantified by its covariance matrix. It is defined as the covariance of $\\xi^s$ at separations $\\mathbf{s}_i$ and $\\mathbf{s}_j$ from $N$ independent observations,\n\\begin{equation}\n\\mathbf{C}_{ij} = \\bigl<\\bigl(\\xi^s(\\mathbf{s}_i)-\\langle\\xi^s(\\mathbf{s}_i)\\rangle\\bigr)\\bigl(\\xi^s(\\mathbf{s}_j)-\\langle\\xi^s(\\mathbf{s}_j)\\rangle\\bigr)\\bigr>\\;,\n\\label{covariance}\n\\end{equation}\nwhere the angled brackets indicate averages over the sample size. Although we can only observe a single universe, we have a large sample of voids at our disposal that enables an estimate of the covariance matrix as well. Note that we are considering mutually exclusive voids, each of which provide an independent patch of large-scale structure. As we are primarily interested in $\\xi^s$ on scales up to the void extent, as opposed to inter-void scales, we can employ a jackknife resampling strategy to estimate $\\mathbf{C}_{ij}$~\\cite{Hamaus2017}. For this we simply remove one void at a time in the estimator of $\\xi^s$ from equation~(\\ref{estimator}), which provides $N_\\mathrm{v}$ jackknife samples in total. These samples can then be used in equation~(\\ref{covariance}) to calculate $\\mathbf{C}_{ij}$, albeit with an additional factor of $(N_\\mathrm{v}-1)$ to account for the statistical weight of the jackknife sample size. We use the square root of the diagonal elements of the covariance matrix to quote error bars on our measurements of $\\xi^s$. The identical procedure can be applied to the multipoles $\\xi^s_\\ell$, except in that case one can use equation~(\\ref{covariance}) to additionally calculate the covariance between multipoles of different order.\n\nThere are several advantages of this jackknife technique over other common methods for covariance estimation, which typically rely on simulated mock catalogs. Most importantly, it is based on the observed data itself and does not involve prior model assumptions about cosmology, structure formation, or galaxy evolution. In addition, a statistically reliable estimation of $\\mathbf{C}_{ij}$ requires large sample sizes, which are expensive in terms of numerical resources when considering realistic mocks from $N$-body simulations. Our void catalog already provides $\\mathcal{O}(10^3)$ spatially independent samples at no additional cost. It has been shown that the jackknife technique provides covariance estimates that are consistent with those obtained from independent mock catalogs in the limit of large jackknife sample sizes~\\cite{Favole2020}.\n\n\n\\subsection{Likelihood \\label{subsec:likelihood}}\nEquipped with the theory from section~\\ref{subsec:correlation} we can now define the likelihood $L(\\hat{\\xi}^s|\\boldsymbol{\\Omega})$ of the measurement given a model, which we approximate to be of Gaussian form,\n\\begin{equation}\n\\ln L(\\hat{\\xi}^s|\\boldsymbol{\\Omega}) = -\\frac{1}{2N_\\mathrm{m}}\\sum\\limits_{i,j}\\Bigl(\\hat{\\xi}^s(\\mathbf{s}_i)-\\xi^s(\\mathbf{s}_i,\\boldsymbol{\\Omega})\\Bigr)\\,\\hat{\\mathbf{C}}_{ij}^{-1}\\Bigl(\\hat{\\xi}^s(\\mathbf{s}_j)-\\xi^s(\\mathbf{s}_j,\\boldsymbol{\\Omega})\\Bigr)\\;.\n\\label{likelihood}\n\\end{equation}\nThe hat symbols indicate a measured quantity to be distinguished from the model, which explicitly depends on the parameters $\\boldsymbol{\\Omega}$. Here we have dropped the normalization term involving the determinant of $\\hat{\\mathbf{C}}_{ij}$, since it only adds a constant. The form of equation~(\\ref{likelihood}) can be applied to either the two-dimensional void-galaxy cross-correlation function $\\xi^s(s_\\parallel,s_\\perp)$, or its multipoles $\\xi^s_\\ell(s)$. We use $\\xi^s(s_\\parallel,s_\\perp)$, which contains the information from the multipoles of all orders. However, we have verified that only including the multipoles of orders $\\ell=(0,2,4)$ yields consistent results. When analyzing mock catalogs we scale their covariance in equation~(\\ref{likelihood}) by the number of mock samples $N_\\mathrm{m}$ used, allowing us to validate the statistical constraining power of the data. When analyzing the data itself, we set $N_\\mathrm{m}=1$. We vary $\\boldsymbol{\\Omega}$ until a global maximum of the likelihood at the best-fit parameter set is found. The quality of the fit can be assessed by evaluation of the reduced chi-square statistic,\n\\begin{equation}\n\\chi^2_\\mathrm{red} = -\\frac{2N_\\mathrm{m}}{N_\\mathrm{dof}}\\ln L(\\hat{\\xi}^s|\\boldsymbol{\\Omega}) \\;,\n\\label{chi2}\n\\end{equation}\nfor $N_\\mathrm{dof}=N_\\mathrm{bin}-N_\\mathrm{par}$ degrees of freedom, where $N_\\mathrm{bin}$ is the number of bins for the data and $N_\\mathrm{par}$ the number of parameters. Moreover, we explore the likelihood surface in the neighborhood of the global maximum using the Monte Carlo Markov Chain (MCMC) sampler \\textsc{emcee}~\\cite{Foreman-Mackey2019}, which enables us to access the posterior probability distribution of the model parameters.\n\n\\subsection{Parameters \\label{subsec:parameters}}\nInstead of using the fundamental cosmological parameters, we express $\\boldsymbol{\\Omega}$ in terms of derived parameters that directly affect the void-galaxy cross-correlation function, namely the linear growth rate to bias ratio $f\/b$, and the AP parameter $\\varepsilon$. To account for potential systematics in the data that can be caused by discreteness noise or selection effects, we further allow for two additional nuisance parameters $\\mathcal{M}$ and $\\mathcal{Q}$. The parameter $\\mathcal{M}$ may adjust for possible inaccuracies arising in the deprojection technique and a contamination of the void sample by Poisson fluctuations, which can attenuate the amplitude of the monopole~\\cite{Cousinou2019}. On the other hand, the parameter $\\mathcal{Q}$ accounts for potential selection effects when voids are identified in anisotropic redshift space~\\cite{Pisani2015b,Nadathur2019b}. A physical origin of this can be violent shell-crossing and virialization events that change the topology of void boundaries~\\cite{Hahn2015}, causing a so-called Finger-of-God (FoG) effect~\\cite{Jackson1972,Hamilton1998,Scoccimarro2004}. It appears around compact overdensities, such as galaxy clusters, generating elongated features along the line of sight that extend over several $h^{-1}{\\rm Mpc}$~\\cite{Peacock2001,Zehavi2011,Pezzotta2017}. A similar effect can be caused by cluster infall regions, leading to closed caustics in redshift space that may be misinterpreted as voids~\\cite{Kaiser1987,Hamilton1998}. Therefore, this can have a non-trivial impact on the identification of voids with diameters of comparable size~\\cite{Stanonik2010,Kreckel2011a,vdWeygaert2011b}, although the tracer sample we use consists of massive luminous red galaxies that typically reside in the centers of clusters and do not sample the entire cluster profile in its outer parts. $\\mathcal{M}$ (monopole-like) is used as a free amplitude of the deprojected correlation function $\\xi(r)$ in real space, and $\\mathcal{Q}$ (quadrupole-like) is a free amplitude for the quadrupole term proportional to $\\mu_r^2$. Hence, equations~(\\ref{xi^s_nonlin2}) and (\\ref{xi^s_lin}) can be extended by the replacements $\\xi(r)\\rightarrow\\mathcal{M}\\xi(r)$ and $\\mu_r^2\\rightarrow\\mathcal{Q}\\mu_r^2$, which results in the following form for the final parametrization of our model at linear perturbation order\n\\begin{equation}\n\\xi^s(\\mathbf{s}) = \\mathcal{M}\\left\\{\\xi(r) + \\frac{1}{3}\\frac{f}{b}\\overline{\\xi}(r) + \\frac{f}{b}\\mathcal{Q}\\mu_r^2\\left[\\xi(r)-\\overline{\\xi}(r)\\right]\\right\\}\\;,\n\\label{xi^s_lin2}\n\\end{equation}\ntogether with the equivalent replacements in equation~(\\ref{r_par(s_par)}) for the mapping from the observed separation $\\mathbf{s}$ to $\\mathbf{r}$: $r_\\parallel = s_\\parallel\/[1-\\frac{1}{3}\\frac{f}{b}\\mathcal{M}\\overline{\\xi}(r)]$. One can think of equation~(\\ref{xi^s_lin2}) as an adaptive template that attempts to extract those anisotropic distortions that match the radial and angular shape as predicted by linear theory. For the nuisance parameters we assign default values of $\\mathcal{M}=\\mathcal{Q}=1$, as they are not known a priori. Note that this extension does not introduce any fundamental parameter degeneracies, due to the different functional forms of $\\xi(r)$ and $\\overline{\\xi}(r)$. For both our model and nuisance parameters we assume uniform prior ranges of $\\left[-10,+10\\right]$, given that each of their expected values is of order unity. We checked that an extension of these boundaries has no impact on our results.\n\n\\subsection{Model validation \\label{subsec:validation}}\nThe theory model derived in section~\\ref{subsec:correlation} requires knowledge of the void density profile $\\delta(r)$ and the void-galaxy cross-correlation function $\\xi(r)$ in real space for equation~(\\ref{xi^s_nonlin}). Its linear version, equation~(\\ref{xi^s_lin}), only requires $\\xi(r)$. $\\delta(r)$ can be accurately modeled with the HSW profile (cf. equation~(\\ref{HSW})), at the price of including additional parameters to be constrained by the data, an approach that has already been successfully applied to BOSS data~\\cite{Hamaus2016}.\n\n\\begin{figure}[t]\n\t\\centering\n\t\\resizebox{\\hsize}{!}{\n\t\t\\includegraphics{fig\/Xip_test.pdf}\n\t\t\\includegraphics{fig\/Xip_mock.pdf}}\n\t\\caption{LEFT: Projection (dashed red) and deprojection (dotted green) of a model void-galaxy cross-correlation function (solid orange) based on the HSW profile from equation~(\\ref{HSW}), using the Abel transform. RIGHT: Projected void-galaxy cross-correlation function (red wedges, dashed line) of voids from $30$ PATCHY mock catalogs in redshift space, and its real-space counterpart after deprojection (green triangles, dotted line). The redshift-space monopole in the mocks (blue dots) follows the same functional form, in agreement with the linear model (blue solid line) from equation~(\\ref{xi_0}).}\n\t\\label{fig:xi_p_mock}\n\\end{figure}\nIn this paper we instead choose a more data-driven approach and use the real-space profile $\\xi(r)$ obtained by deprojecting the measured projected void-galaxy cross-correlation function $\\xi^s_p(s_\\perp)$ using the inverse Abel transform, equation~(\\ref{xi_d}). We first test this procedure based on the model template from equation~(\\ref{HSW}) and use equation~(\\ref{xi(delta)}) to define $\\xi_{\\scriptscriptstyle\\mathrm{HSW}}(r)\\equiv b\\delta_{\\scriptscriptstyle\\mathrm{HSW}}(r)$. The left panel of figure~\\ref{fig:xi_p_mock} shows $\\xi_{\\scriptscriptstyle\\mathrm{HSW}}$ with the parameter values $(r_s\/R,\\delta_c,\\alpha,\\beta)\\simeq(0.82,-0.36,1.6,9.1)$ and $b=2.2$. These values have been chosen to match the mock data reasonably well (see below). We then use the forward Abel transform to obtain the projected correlation function,\n\\begin{equation}\n\\xi^s_p(s_\\perp) = 2\\int_{s_\\perp}^\\infty\\xi(r)\\frac{r\\mathrm{d}r}{\\sqrt{r^2-s_\\perp^2}}\\;.\n\\label{xi_p2}\n\\end{equation}\nFinally, we apply the inverse Abel transform of equation~(\\ref{xi_d}) to infer the original void-galaxy cross-correlation function $\\xi_{\\scriptscriptstyle\\mathrm{HSW}}$. As evident from the perfectly overlapping lines in the plot, this procedure works extremely well with noiseless data. In reality, however, we have to measure correlation functions with an estimator, which is unavoidably associated with a finite covariance. In order to estimate $\\xi^s_p$ from the data, we adopt equation~(\\ref{estimator}) for pairs on the plane of the sky, which can be achieved by exchanging the three-dimensional position $\\mathbf{x}$ of an object by $x_\\perp=(|\\mathbf{x}|^2-x_\\parallel^2)^{1\/2}$ and counting pairs at a given projected separation $s_\\perp$ over the redshift range. We restrict the line-of-sight projection range to $s_\\parallel=3R$ at the near and far sides from the void center, where $\\xi^s$ has well converged to zero. The right panel of figure~\\ref{fig:xi_p_mock} shows the result when stacking $N_\\mathrm{v}\\simeq2\\times10^5$ voids from $N_\\mathrm{m}=30$ PATCHY mock catalogs. In this case the situation is very similar to the test case from the left panel. The deprojection via the inverse Abel transform results in a smooth curve, only close to the void center we observe mild fluctuations away from the expected shape, due to larger statistical uncertainties~\\cite{Pisani2014}. We verified that reprojecting our result using equation~(\\ref{xi_p2}) agrees well with the original $\\xi^s_p$. We also plot the measured monopole $\\xi^s_0$ from the same PATCHY mocks, including its best-fit model from equation~(\\ref{xi^s_lin2}) using the deprojected $\\xi^s_p$. It provides an excellent agreement with the mock data, and confirms the predicted proportionality between $\\xi^s_0(s)$ and $\\xi(r)$ of equation~(\\ref{xi_0}).\n\nHaving validated the deprojection procedure to obtain $\\xi(r)$ from the data, we are now ready to test our model for the void-galaxy cross-correlation function in redshift space.\n\\begin{figure}[h]\n\t\\centering\n\t\\resizebox{0.86\\hsize}{!}{\\includegraphics[trim=-40 0 0 10]{fig\/Xi2d_mock.pdf}}\n\t\\resizebox{0.86\\hsize}{!}{\\includegraphics[trim=0 10 0 10]{fig\/Xi_ell_mock.pdf}}\n\t\\caption{TOP: Estimation of the stacked void-galaxy cross-correlation function $\\xi^s(s_\\parallel\/R,s_\\perp\/R)$ from voids in 30 PATCHY mock catalogs (color scale with black contours) and the best-fit model (white contours) from equation~(\\ref{xi^s_lin2}). BOTTOM: Monopole (blue dots), quadrupole (red triangles) and hexadecapole (green wedges) from the same mock data with corresponding model fits (solid, dashed, dotted lines). The mean redshift and effective radius of the void sample is shown at the top.}\n\t\\label{fig:xi_mock}\n\\end{figure}\n\\begin{figure}[h]\n\t\\centering\n\t\\resizebox{0.7\\hsize}{!}{\\includegraphics{fig\/triangle_mock.pdf}}\n\t\\caption{Posterior probability distribution of the model parameters that enter in equation~(\\ref{xi^s_lin2}), obtained via MCMC from the PATCHY mock data shown in figure~\\ref{fig:xi_mock}. Dark and light shaded areas show $68\\%$ and $95\\%$ confidence regions with a cross marking the best fit, dashed lines indicate fiducial values of the RSD and AP parameters $(f\/b=0.344,\\,\\varepsilon=1)$, and default values for the nuisance parameters $(\\mathcal{M}=\\mathcal{Q}=1)$. The top of each column states the mean and standard deviation of the 1D marginal distributions.}\n\t\\label{fig:triangle_mock}\n\\end{figure}\nFigure~\\ref{fig:xi_mock} presents the corresponding measurement from voids in 30 PATCHY mock catalogs, both its 2D variant with separations along and perpendicular to the line of sight (top panel), as well as the multipoles of order $\\ell=(0,2,4)$ (bottom panel). For the former we use $18$ bins per direction, resulting in $N_\\mathrm{bin}=18^2=324$, whereas for the multipoles we use $25$ radial bins, which yields $N_\\mathrm{bin}=3\\times25=75$ in total. We apply the linear model from equation~(\\ref{xi^s_lin2}) to fit this data (omitting the innermost radial bin) using the $N_\\mathrm{par}=4$ parameters $\\boldsymbol{\\Omega}=(f\/b,\\varepsilon,\\mathcal{M},\\mathcal{Q})$. As apparent from figure~\\ref{fig:xi_mock}, this yields a very good fit to the mock data, with a reduced chi-square value of $\\chi^2_\\mathrm{red}=1.86$. We note that this value corresponds to the statistical power of $30$ mock observations, so it is entirely satisfactory for the purpose of validating our model for a single BOSS catalog. An increase in the number of mock samples marginally affects our summary statistics, which exhibit a negligible amount of statistical noise compared to the real data. The anisotropy of the void-galaxy cross-correlation function is well captured close to the void center, as well as on the void boundaries at $s\\simeq R$. The flattened contours around the void interior are a result of the quadrupole term in equation~(\\ref{xi^s_nonlin}), respectively its linear version~(\\ref{xi^s_lin})~\\cite{Cai2016}. Outside the void boundaries, where the correlation function declines again, this results in elongated contours, as necessary in order to restore spherical symmetry in the limit of large separations. It is worth noting that the coordinate transformation from equation~(\\ref{r_par(s_par)}) causes a line-of-sight elongation from $r_\\parallel$ to $s_\\parallel$ for negative $\\Delta(r)$, acting in the opposite direction. However, this coordinate effect merely accounts for a small correction to the flattening caused by the quadrupole, which we explicitly checked in our analysis. Finally, we emphasize the strong evidence for a vanishing hexadecapole on all scales, in agreement with the theory prediction from section~\\ref{subsec:correlation}.\n\nWe then run a MCMC to sample the posterior distribution of the model parameters. The result is presented in figure~\\ref{fig:triangle_mock} using the \\textsc{getdist} software package~\\cite{Lewis2019}. We recover the fiducial values of the cosmologically relevant parameters $f\/b$ and $\\varepsilon$ to within the $68\\%$ confidence regions, which validates the theory model we use. Moreover, the parameter contours reveal a nearly Gaussian shape of the posterior, only the nuisance parameter $\\mathcal{Q}$ exhibits a slightly non-Gaussian behavior. While $\\mathcal{Q}$ is marginally consistent with unity to within $1\\sigma$, we find clear evidence for the parameter $\\mathcal{M}$ to exceed unity by roughly $14\\%$. This could be attributed to some degree of overdispersion beyond Poisson noise that has been used in the PATCHY algorithm to calibrate the clustering statistics of galaxies in BOSS~\\cite{Kitaura2016a}, resulting in a higher amplitude of random fluctuations inside voids. We also note a strong anti-correlation between $\\mathcal{M}$ and $f\/b$, which can be understood from equation~(\\ref{xi_2}) for the quadrupole, where both parameters enter via multiplication of $\\xi(r)$ and $\\overline{\\xi}(r)$. However, the monopole in equation~(\\ref{xi_0}) breaks their degeneracy.\n\nAs a next step we investigate the dependence on void redshift $Z$. To this end we split our catalog into subsets that contain $50\\%$ of all voids with redshifts below or above their median value. We will refer to these subsets as ``low-z'' and ``high-z'', respectively. Figure~\\ref{fig:xi_mock_Z} presents the corresponding correlation function statistics, revealing characteristic redshift trends. Note that the effective radii $R$ in our catalog are somewhat correlated with $Z$ due to the variation of tracer density $n(z)$ with redshift, as shown in figure~\\ref{fig:nz}. When $n(z)$ decreases, fewer small voids can be identified, which means that at the high-redshift end our voids tend to be larger in size. On average, smaller voids are emptier and exhibit higher compensation walls at their edges~\\cite{Sheth2004,vdWeygaert2011a,Hamaus2014b,vdWeygaert2016}, which in turn induces a higher amplitude of both monopole and quadrupole~\\cite{Hamaus2017}. A similar trend is manifest in the evolution from high to low redshift, which reveals the growth of structure on the void boundaries over time while the void core continuously deepens~\\cite{Sheth2004,vdWeygaert2011a,Hamaus2014b,vdWeygaert2016}. Both effects are supported by the mock data shown in figure~\\ref{fig:xi_mock_Z}.\n\\begin{figure}[t]\n\t\\centering\n\t\\resizebox{\\hsize}{!}{\n\t\t\\includegraphics[trim=0 30 0 5, clip]{fig\/Xi2d_mock_Z1.pdf}\n\t\t\\includegraphics[trim=0 30 0 5, clip]{fig\/Xi_ell_mock_Z1.pdf}}\n\t\\resizebox{\\hsize}{!}{\n\t\t\\includegraphics[trim=0 10 0 5, clip]{fig\/Xi2d_mock_Z2.pdf}\n\t\t\\includegraphics[trim=0 10 0 5, clip]{fig\/Xi_ell_mock_Z2.pdf}}\n\t\\caption{As figure~\\ref{fig:xi_mock} after splitting the PATCHY void sample at its median redshift of $Z=0.51$ into $50\\%$ lowest-redshift (``low-z'', top row) and $50\\%$ highest-redshift voids (``high-z'', bottom row).}\n\t\\label{fig:xi_mock_Z}\n\\end{figure}\n\\begin{figure}[h]\n\t\\centering\n\t\\resizebox{\\hsize}{!}{\n\t\t\\includegraphics{fig\/triangle_mock_Z1.pdf}\n\t\t\\includegraphics{fig\/triangle_mock_Z2.pdf}}\n\t\\caption{As figure~\\ref{fig:triangle_mock} for the ``low-z'' (left) and ``high-z'' (right) PATCHY void sample in figure~\\ref{fig:xi_mock_Z}.}\n\t\\label{fig:triangle_mock_Z}\n\\end{figure}\n\nFinally, we repeat the model fits for the subsets in void redshift and run a full MCMC for each of them. The parameter posteriors are shown in figure~\\ref{fig:triangle_mock_Z}. As for the full void sample from before we retrieve the input cosmology of the PATCHY mocks to within $68\\%$ of the confidence levels for $f\/b$ and $\\varepsilon$. Also the posteriors of the nuisance parameters $\\mathcal{M}$ and $\\mathcal{Q}$ look similar as for the full void sample shown in figure~\\ref{fig:triangle_mock}. However, we notice a very mild increase of $\\mathcal{M}$ and a decrease of $\\mathcal{Q}$ towards higher redshifts. Although the shifts remain well within the $1\\sigma$ confidence intervals for these parameters, they may indicate a slightly lower contamination by Poisson noise, but a slightly stronger anisotropic selection effect for the smaller voids at lower redshift. This would indeed agree with previous simulation results that suggest the impact of RSDs on void identification to be more severe for voids with smaller effective radii~\\cite{Pisani2015b}. Moreover, as large-scale structures develop more non-linear over time, we do expect the FoG effect to have a stronger impact on voids at lower redshift.\n\n\\subsection{Data analysis \\label{subsec:fitting}}\nThe successful model validation from the previous section now enables us to perform model fits on the real BOSS data. To this end we simply repeat the analysis steps that have already been performed on the PATCHY mocks above. We first measure the projected void-galaxy cross-correlation function $\\xi^s_p(s_\\perp)$ and apply the deprojection technique using the inverse Abel transform to obtain $\\xi(r)$. The result is shown in figure~\\ref{fig:xi_p_data}, along with the redshift-space monopole $\\xi^s_0(s)$ and its best-fit model. We observe very similar trends as in the mocks, albeit with larger error bars as expected from the smaller sample size of voids available in the BOSS data.\n\\begin{figure}[t]\n\t\\centering\n\t\\resizebox{0.86\\hsize}{!}{\\includegraphics[trim=0 10 0 10]{fig\/Xip_data.pdf}}\n\t\\caption{Measurement of the projected void-galaxy cross-correlation function (red wedges, dashed line) from the BOSS DR12 combined sample in redshift space, and its real-space counterpart after deprojection (green triangles, dotted line). The measured redshift-space monopole (blue dots) follows the same functional form, in agreement with the linear model (blue solid line) from equation~(\\ref{xi_0}).}\n\t\\label{fig:xi_p_data}\n\\end{figure}\n\\begin{figure}[h]\n\t\\centering\n\t\\resizebox{0.86\\hsize}{!}{\\includegraphics[trim=-40 0 0 10]{fig\/Xi2d_data.pdf}}\n\t\\resizebox{0.86\\hsize}{!}{\\includegraphics[trim=0 10 0 10]{fig\/Xi_ell_data.pdf}}\n\t\\caption{TOP: Measurement of the stacked void-galaxy cross-correlation function $\\xi^s(s_\\parallel\/R,s_\\perp\/R)$ from voids in the BOSS DR12 combined sample (color scale with black contours) and the best-fit model (white contours) from equation~(\\ref{xi^s_lin2}). BOTTOM: Monopole (blue dots), quadrupole (red triangles) and hexadecapole (green wedges) from the same data with corresponding model fits (solid, dashed, dotted lines). The mean redshift and effective radius of the void sample is shown at the top.}\n\t\\label{fig:xi_data}\n\\end{figure}\nFigure~\\ref{fig:xi_data} presents the two-point statistics for the void-galaxy cross-correlation function and its multipoles. Apart from the larger impact of statistical noise due to the substantially smaller sample size (by a factor of $N_\\mathrm{m}=30$), the results are in excellent agreement with the mock data. Both amplitude and shape of $\\xi^s(s_\\parallel,s_\\perp)$, as well as $\\xi^s_\\ell(s)$ are very consistent in comparison with figure~\\ref{fig:xi_mock}. A mild but noticeable difference can be seen very close to the void center, which appears more flattened in the data. One can also perceive stronger fluctuations of the quadrupole and hexadecapole in this regime, but those are simply due to the sparser statistics of galaxies near the void center and thus fully consistent with the error bars. This fact is further supported by the accurate model fit to the data, resulting in a reduced chi-square value of $\\chi^2_\\mathrm{red}=1.12$.\n\\begin{figure}[t]\n\t\\centering\n\t\\resizebox{0.7\\hsize}{!}{\n\t\t\\includegraphics{fig\/triangle_data.pdf}}\n\t\\caption{Posterior probability distribution of the model parameters that enter in equation~(\\ref{xi^s_lin2}), obtained via MCMC from the BOSS DR12 data shown in figure~\\ref{fig:xi_data}. Dark and light shaded areas show $68\\%$ and $95\\%$ confidence regions with a cross marking the best fit, dashed lines indicate fiducial values of the RSD and AP parameters $(f\/b=0.409,\\,\\varepsilon=1)$, and default values for the nuisance parameters $(\\mathcal{M}=\\mathcal{Q}=1)$. The top of each column states the mean and standard deviation of the 1D marginal distributions.}\n\t\\label{fig:triangle_data}\n\\end{figure}\n\nThe full posterior parameter distribution obtained from the BOSS data is shown in figure~\\ref{fig:triangle_data}, which qualitatively resembles the mock results from figure~\\ref{fig:triangle_mock}. However, a few important differences are apparent. Firstly, the value of $f\/b$ from the data is significantly higher than in the mocks, which is partly driven by the lower value of $b=1.85$ in the BOSS data, compared to $b=2.20$ of the mocks. Further, the nuisance parameters $\\mathcal{M}$ and $\\mathcal{Q}$ both take on lower values in the data than in the mocks. In particular, $\\mathcal{M}$ is consistent with unity to within $68\\%$ confidence, which could indicate that voids in the BOSS data are less affected by discreteness noise than what was expected from the PATCHY mocks. At the same time, $\\mathcal{Q}$ is consistent with unity only at the $95\\%$ confidence level from below, suggesting an attenuation of the quadrupole amplitude when compared to the mocks. This could be caused by systematics in the BOSS data that have not been taken into account at the same level of complexity in the mocks. One such example is the foreground contamination by stars~\\cite{Reid2016}.\n\n\\begin{figure}[t]\n\t\\centering\n\t\\resizebox{\\hsize}{!}{\n\t\t\\includegraphics[trim=0 30 0 5, clip]{fig\/Xi2d_data_Z1.pdf}\n\t\t\\includegraphics[trim=0 30 0 5, clip]{fig\/Xi_ell_data_Z1.pdf}}\n\t\\resizebox{\\hsize}{!}{\n\t\t\\includegraphics[trim=0 10 0 5, clip]{fig\/Xi2d_data_Z2.pdf}\n\t\t\\includegraphics[trim=0 10 0 5, clip]{fig\/Xi_ell_data_Z2.pdf}}\n\t\\caption{As figure~\\ref{fig:xi_data} after splitting the BOSS void sample at its median redshift of $Z=0.52$ into $50\\%$ lowest-redshift (``low-z'', top row) and $50\\%$ highest-redshift voids (``high-z'', bottom row).}\n\t\\label{fig:xi_data_Z}\n\\end{figure}\n\\begin{figure}[h]\n\t\\centering\n\t\\resizebox{\\hsize}{!}{\n\t\t\\includegraphics{fig\/triangle_data_Z1.pdf}\n\t\t\\includegraphics{fig\/triangle_data_Z2.pdf}}\n\t\\caption{As figure~\\ref{fig:triangle_data} for the ``low-z'' (left) and ``high-z'' (right) BOSS void sample in figure~\\ref{fig:xi_data_Z}.}\n\t\\label{fig:triangle_data_Z}\n\\end{figure}\n\nAs done for the PATCHY mocks, we further investigate the redshift evolution of our constraints with the BOSS data. To this end we split our catalog into two equally sized low- and high-redshift bins again (``low-z'' and ``high-z'' sample), the resulting clustering statistics are shown in figure~\\ref{fig:xi_data_Z}. We observe the same trends as before, namely a deepening of void interiors and an increase of the quadrupole towards lower redshift. Given the lower statistical power of these bins the data evidently look more noisy, but the linear model still provides a good fit overall. Note that we neglect any uncertainties in our theory model, which relies on a measurement of the projected correlation function $\\xi^s_p(s_\\perp)$. Thus, especially for noisy data, this may result in an underestimation of the full covariance and hence a higher reduced chi-square. Nevertheless, our $\\chi^2_\\mathrm{red}$ values are still reasonably close to unity. Figure~\\ref{fig:triangle_data_Z} presents the parameter posteriors of the model fit. Evidently, even these subsets of voids can still provide interesting constraints with a good accuracy. We find our best-fit values for $f\/b$ and $\\varepsilon$ to be in agreement with the fiducial Planck cosmology to within $68\\%$ of the confidence levels. Moreover, we notice that the low amplitude for the nuisance parameter $\\mathcal{Q}$ is driven by the high-redshift bin, otherwise the best-fit values for both $\\mathcal{M}$ and $\\mathcal{Q}$ are consistent with unity to within the $68\\%$ contours.\n\nSo far our analysis has exclusively been based on the observed data without using any prior information. The model ingredients $\\xi(r)$, $\\mathcal{M}$, and $\\mathcal{Q}$ have been derived from this data self-consistently. One may argue, however, that these quantities are already available from the survey mocks to a much higher accuracy (see section~\\ref{subsec:validation}). Hence, making use of the mocks to calibrate those model ingredients allows us to evade marginalization over nuisance parameters and to use the statistical power of the data solely to constrain cosmology. We implement this calibration approach by simply using the $30$ PATCHY mocks to estimate $\\xi(r)$ via equation~(\\ref{xi_d}) and fixing the nuisance parameters $\\mathcal{M}$ and $\\mathcal{Q}$ to their best-fit values from the corresponding void sample in the mock analysis. This leaves us with only two remaining free parameters $f\/b$ and $\\varepsilon$, for which we repeat the MCMC runs.\n\nThe results are presented in figure~\\ref{fig:triangle_data_cal}, showing their posterior distribution for each of our void samples. Evidently, the mock-calibrated analysis (calib.) significantly improves upon the constraints obtained without calibration (free). While the accuracy on the AP parameter $\\varepsilon$ exhibits mild improvements of about $10\\%$ to $30\\%$, the error on $f\/b$ shrinks by roughly a factor of~$4$. This is mainly due to the considerable anti-correlation between $f\/b$ and $\\mathcal{M}$ apparent in figures~\\ref{fig:triangle_data} and~\\ref{fig:triangle_data_Z}, which is removed when $\\mathcal{M}$ is fixed to a fiducial value. We note, however, that the best-fit values for $\\mathcal{M}$ in our uncalibrated analysis differ significantly between the observed data and the mocks. In particular, we found the values of $\\mathcal{M}$ in the mocks to be higher than in the data by about $10\\%$. This may be partly due to the higher bias parameter and the level of overdispersion in the PATCHY mocks as compared to the data, but more fundamentally the mismatch reveals that not all aspects of the data are understood precisely enough to be fully represented by the mocks.\n\\begin{figure}[b]\n\t\\centering\n\t\\resizebox{\\hsize}{!}{\n\t\t\\includegraphics{fig\/triangle_data_cal_Z1.pdf}\n\t\t\\includegraphics{fig\/triangle_data_cal_Z2.pdf}\n\t\t\\includegraphics{fig\/triangle_data_cal.pdf}}\n\t\\caption{As figures~\\ref{fig:triangle_data} and~\\ref{fig:triangle_data_Z}, but focused on the RSD and AP parameters $f\/b$ and $\\varepsilon$. The red contours show constraints when the theory model is calibrated with the PATCHY mocks to determine $\\xi(r)$ and the values of the nuisance parameters $\\mathcal{M}$ and $\\mathcal{Q}$. The blue contours show the original constraints when $\\xi(r)$, $\\mathcal{M}$ and $\\mathcal{Q}$ are left free to be jointly estimated from the BOSS data. The top of each column states the mean and standard deviation of the calibrated constraints, the corresponding void sample is indicated above the figure legend of each panel.}\n\t\\label{fig:triangle_data_cal}\n\\end{figure}\n\nTherefore, we caution the use of mocks for model calibration, as such an approach is prone to cause biased constraints on cosmology. This is evident from the significant shifts of the posteriors in figure~\\ref{fig:triangle_data_cal} after performing the calibration. Another consequence is the underestimation of parameter uncertainties, which is caused by mistaking prior information from the mocks as the truth. The mocks merely represent many realizations of a single cosmological model with one fiducial parameter set and one fixed prescription of how dark matter halos are populated by galaxies (halo occupation distribution). A realistic model must therefore either take into account the dependence on these ingredients including their uncertainty, or constrain them from the data directly. Our approach follows the philosophy to exclusively rely on the observed data to obtain most robust constraints.\n\n\n\\section{Discussion\\label{sec:discussion}}\n\n\\subsection{Parameter constraints \\label{subsec:constraints}}\n\\begin{table}[b]\n\t\\centering\n\t\\caption{Constraints on RSD and AP parameters (mean values with $1\\sigma$ errors) from \\textsc{vide} voids in the final BOSS data (top rows). The middle rows show corresponding constraints after model-calibration on the PATCHY mocks, and the bottom rows provide Planck 2018~\\cite{Planck2018} results as reference values, assuming a flat $\\Lambda$CDM model. All constraints on $\\Omega_\\mathrm{m}$ in the last column assume flat $\\Lambda$CDM as well.}\\vspace{10pt}\n\t\\label{tab:constraints}\n\t\\centerline{\n\t\t\\begin{tabular}{lccccc}\n\t\t\t\\toprule\n\t\t\tSample ($\\bar{Z}$) & $f\/b$ & $f\\sigma_8$ & $\\varepsilon$ & $D_\\mathrm{A} H\/c$ & $\\Omega_\\mathrm{m}$\\\\\n\t\t\t\\midrule\n\t\t\tlow-z ($0.43$) & $0.493\\pm0.105$ & $0.590\\pm0.125$ & $0.9996\\pm0.0081$ & $0.485\\pm0.004$ & $0.306\\pm0.027$\\\\[3pt]\n\t\t\thigh-z ($0.58$) & $0.538\\pm0.146$ & $0.594\\pm0.162$ & $1.0100\\pm0.0111$ & $0.702\\pm0.008$ & $0.334\\pm0.030$\\\\[3pt]\n\t\t\tall ($0.51$) & $0.540\\pm0.091$ & $0.621\\pm0.104$ & $1.0017\\pm0.0068$ & $0.588\\pm0.004$ & $0.312\\pm0.020$\\\\[3pt]\n\t\t\t\\midrule\n\t\t\tlow-z calib. & $0.390\\pm0.025$ & $0.554\\pm0.036$ & $1.0134\\pm0.0075$ & $0.492\\pm0.004$ & $0.353\\pm0.026$\\\\[3pt]\n\t\t\thigh-z calib. & $0.288\\pm0.033$ & $0.379\\pm0.043$ & $0.9953\\pm0.0084$ & $0.691\\pm0.006$ & $0.295\\pm0.022$\\\\[3pt]\n\t\t\tall calib. & $0.347\\pm0.023$ & $0.474\\pm0.031$ & $1.0011\\pm0.0060$ & $0.588\\pm0.003$ & $0.310\\pm0.017$\\\\[3pt]\n\t\t\t\\midrule\n\t\t\tlow-z ref. & $0.398\\pm0.003$ & $0.476\\pm0.006$ & $1.0025\\pm0.0022$ & $0.487\\pm0.001$ & $0.315\\pm0.007$\\\\[3pt]\n\t\t\thigh-z ref. & $0.425\\pm0.003$ & $0.470\\pm0.005$ & $1.0031\\pm0.0028$ & $0.697\\pm0.002$ & $0.315\\pm0.007$\\\\[3pt]\n\t\t\tall ref. & $0.412\\pm0.003$ & $0.474\\pm0.006$ & $1.0028\\pm0.0025$ & $0.589\\pm0.001$ & $0.315\\pm0.007$\\\\[3pt]\n\t\t\t\\bottomrule\n\t\\end{tabular}}\n\\end{table}\n\nThe final parameter constraints from our \\textsc{vide} void samples found in the BOSS DR12 data are summarized in table~\\ref{tab:constraints}. We distinguish between uncalibrated and mock-calibrated (calib.) samples, both for the subsets at low and high redshift (low-z, high-z), as well as the full redshift range (all). The table presents measured quantities of mean void redshift $\\bar{Z}$, RSD parameter $f\/b$ and AP parameter $\\varepsilon$. Furthermore, it provides derived constraints on $f\\sigma_8$, $D_\\mathrm{A} H$ and $\\Omega_\\mathrm{m}$. For $f\\sigma_8$ we multiply our constraint on $f\/b$ by $b\\sigma_8$, with $b=1.85$ and $\\sigma_8=0.8111$ from Planck 2018~\\cite{Planck2018}. For the calibrated case we use $b=2.20$ from the mocks.\n\nIn principle the parameter combination $f\\sigma_8$ could be constrained from voids directly, but only if the theory model can explicitly account for the dependence of the void-galaxy cross-correlation function $\\xi(r)$ on $\\sigma_8$. Sometimes the assumption $\\xi(r)\\propto\\sigma_8$ is used without further justification, but evidently this must fail in the non-linear regime~\\cite{Juszkiewicz2010} where $\\xi(r)$ approaches values close to $-1$, due to the restriction $\\xi(r)>-1$. The same argument applies to the density profiles of dark matter halos or galaxy clusters, which do not simply scale linearly with $\\sigma_8$~\\cite{Brown2020}. Moreover, while the value of $\\sigma_8$ controls the amplitude of matter fluctuations and thus the formation of halos, its effect on voids identified in the distribution of galaxies that populate those halos is far from trivial. Another approach is to directly estimate $b\\sigma_8$ via an integral over the projected galaxy auto-correlation function~\\cite{Hawken2017}. However, the result contains non-linear contributions from small scales that must be accounted for, which again involves assumptions about a particular cosmological model. The covariance between measurements of $f\/b$ and $b\\sigma_8$ remains inaccessible to that approach as well.\n\n\\begin{figure}[t]\n\t\\centering\n\t\\resizebox{0.51\\hsize}{!}{\n\t\t\\includegraphics[trim=10 10 10 10]{fig\/triangle_data_fs8_Om.pdf}}\n\t\\caption{Calibration-independent constraints on the cosmological parameters $f\\sigma_8$ and $\\Omega_\\mathrm{m}$ from our full \\textsc{VIDE} void sample in the final BOSS data. They are converted from the RSD and AP parameter posteriors shown in figure~\\ref{fig:triangle_data}, as described in section~\\ref{subsec:constraints}. A white cross indicates our best fit and dashed lines show mean parameter values from the Planck 2018 baseline results as a reference~\\cite{Planck2018}.}\n\t\\label{fig:triangle_data_fs8_Om}\n\\end{figure}\n\nFinally, measurements involving the parameter $\\sigma_8$ commonly ignore its implicit dependence on the Hubble parameter $h$ via the choice of $8h^{-1}{\\rm Mpc}$ as reference scale, and therefore underestimate the uncertainty. Reference~\\cite{Sanchez2020} argued to instead use $\\sigma_{12}$ with $12$Mpc, which yields about the same value as $\\sigma_8$ for a Planck-constrained value of $h$. For these reasons we decided to follow the simpler procedure described above to derive constraints on $f\\sigma_8$, allowing us to compare existing results across the literature. The constraint on $D_\\mathrm{A} H$ can be obtained via equation~(\\ref{epsilon}) by multiplying $\\varepsilon$ and its error with $D_\\mathrm{A}'H'$ from our fiducial flat $\\Lambda$CDM cosmology from section~\\ref{subsec:galaxies}. In this case the only free cosmological parameter in the product $D_\\mathrm{A} H$ is $\\Omega_\\mathrm{m}$, so we can numerically invert this function to obtain the full posterior on $\\Omega_\\mathrm{m}$. Its mean and standard deviation are shown in the last column of table~\\ref{tab:constraints}. Finally, we present our main result for the converted parameter constraints on $f\\sigma_8$ and $\\Omega_\\mathrm{m}$ in figure~\\ref{fig:triangle_data_fs8_Om}. It originates from the calibration-independent analysis of our full void sample in the final BOSS data at mean redshift $\\bar{Z}=0.51$.\n\n\n\\subsection{Systematics tests\\label{subsec:systematics}}\n\n\\subsubsection{Fiducial cosmology}\nIn order to affirm the robustness of our results, we have performed a number of systematics tests on our analysis pipeline. One potential systematic can be a residual dependence on the fiducial cosmology we assumed in section~\\ref{subsec:galaxies} when converting angular sky coordinates and redshifts into comoving space via equation~(\\ref{x_comoving}). This conversion preserves the topology of large-scale structure, but in the presence of statistical noise due to sparse sampling of tracers it can have an impact on void identification~\\cite{Mao2017}. We investigate how a change of the fiducial cosmology affects our final constraints on cosmological parameters by shifting the fiducial value for $\\Omega_\\mathrm{m}'=0.307$ to $0.247$. This shift amounts to three times the standard deviation we obtain from the posterior on $\\Omega_\\mathrm{m}=0.312\\pm0.020$ in the uncalibrated analysis of all voids (see table~\\ref{tab:constraints}). We then repeat our entire analysis including the void-finding procedure and sample the posterior on $f\/b$ and $\\Omega_\\mathrm{m}$ assuming the new value for $\\Omega_\\mathrm{m}'$. The result is presented in the left panel of figure~\\ref{fig:triangle_sys}, showing a very mild impact of the fiducial cosmology on the posterior parameter distribution. The resulting shifts of the posterior mean values are well within the $68\\%$ credible regions of both cases and their relative accuracies practically remain unchanged, suggesting the impact of our fiducial cosmology to contribute a marginal systematic effect to our final constraints.\n\\begin{figure}[b]\n\t\\centering\n\t\\resizebox{\\hsize}{!}{\n\t\t\\includegraphics{fig\/triangle_data_Om.pdf}\n\t\t\\includegraphics{fig\/triangle_mock_bias.pdf}}\n\t\\caption{LEFT: Impact of the fiducial parameter $\\Omega_\\mathrm{m}'$ on the final posterior distribution for $f\/b$ and $\\Omega_\\mathrm{m}$ from all voids in the BOSS DR12 data. The top of each column states the mean and standard deviation obtained for the assumed value of $\\Omega_\\mathrm{m}'=0.247$ and dashed lines indicate fiducial values of the default cosmology with $\\Omega_\\mathrm{m}'=0.307$. RIGHT: Impact of the bias of the galaxy sample used in the cross-correlation with all voids from the PATCHY mocks on the derived posterior for $f\\sigma_8$ and $\\varepsilon$. The top of each column states the mean and standard deviation obtained for the new value $b=1.93$.}\n\t\\label{fig:triangle_sys}\n\\end{figure}\n\n\\subsubsection{Galaxy bias}\nThe bias of the galaxy sample we use to estimate the cross-correlation with voids can contribute another systematic effect on our final parameters. This especially so for the derived combination $f\\sigma_8$, which we obtain via multiplying $f\/b$ by the average bias $b$ of the galaxy sample, and the Planck-constrained $\\sigma_8$ value (see section~\\ref{subsec:constraints}). The BOSS data does not readily allow us to define sub-samples of galaxies with known bias values that differ from the sample average. However, the PATCHY mocks provide a bias parameter for every object in the catalog, so we can investigate its influence on our analysis pipeline. As a simple test, we selected $50\\%$ of all PATCHY galaxies with a bias value below the median, which amounts to an average of $b=1.93$. Because the galaxy bias follows its own redshift evolution, we had to re-sample the random catalog in order for it to follow the same density-redshift trend as the selected galaxy sample. We then cross-correlate it with our original PATCHY void sample used in section~\\ref{subsec:validation} and compare its posterior on $f\\sigma_8$ and $\\varepsilon$ to the original one from figure~\\ref{fig:triangle_mock} in the right panel of figure~\\ref{fig:triangle_sys}. The two constraints are very consistent with each other, suggesting that the final result on $f\\sigma_8$ does not depend on the bias of the galaxy sample used for the cross-correlation.\n\n\\subsubsection{Estimator}\nThe main advantage of our clustering estimator from equation~(\\ref{estimator}) is its simplicity, allowing a fast and precise evaluation of the void-galaxy cross-correlation function and its multipoles without angular binning. In order to assess its accuracy, we have compared it with the more common Landy-Szalay estimator~\\cite{Landy1993}\n\\begin{equation}\n\\xi^s(\\mathbf{s}) = \\left(\\frac{\\langle\\mathbf{X},\\mathbf{x}\\rangle(\\mathbf{s})}{\\langle\\mathbf{X}\\rangle\\langle\\mathbf{x}\\rangle} -\\frac{\\langle\\mathbf{X},\\mathbf{x}_r\\rangle(\\mathbf{s})}{\\langle\\mathbf{X}\\rangle\\langle\\mathbf{x}_r\\rangle} -\\frac{\\langle\\mathbf{X}_r,\\mathbf{x}\\rangle(\\mathbf{s})}{\\langle\\mathbf{X}_r\\rangle\\langle\\mathbf{x}\\rangle} +\\frac{\\langle\\mathbf{X}_r,\\mathbf{x}_r\\rangle(\\mathbf{s})}{\\langle\\mathbf{X}_r\\rangle\\langle\\mathbf{x}_r\\rangle}\\right)\\left\/\n\\left(\\frac{\\langle\\mathbf{X}_r,\\mathbf{x}_r\\rangle(\\mathbf{s})}{\\langle\\mathbf{X}_r\\rangle\\langle\\mathbf{x}_r\\rangle}\\right.\\right)\\;,\n\\label{LS_estimator}\n\\end{equation}\nwhich additionally involves the random void-center positions $\\mathbf{X}_r$. From the PATCHY mocks we generate a sample of such void randoms by assigning the same angular and redshift distribution of its voids to a randomly generated set of points with $50$ times the number of objects (in analogy to the galaxy randoms, see section~\\ref{subsec:galaxies}). We also assign an effective radius to each random void, with the same distribution as the one obtained in the mocks. This guarantees a consistent stacking procedure, as described in section~\\ref{subsec:estimators}. We find that the additional terms $\\langle\\mathbf{X}_r,\\mathbf{x}\\rangle(\\mathbf{s})\/\\langle\\mathbf{X}_r\\rangle\\langle\\mathbf{x}\\rangle$ and $\\langle\\mathbf{X}_r,\\mathbf{x}_r\\rangle(\\mathbf{s})\/\\langle\\mathbf{X}_r\\rangle\\langle\\mathbf{x}_r\\rangle$ in the stacked void-galaxy correlation estimator from equation~(\\ref{LS_estimator}) are independent of the direction and magnitude of~$\\mathbf{s}$, in agreement with the findings of reference~\\cite{Hamaus2017}. However, we notice the amplitude of both terms to exceed unity by roughly $20\\%$, while their ratio remains very close to one with deviations in the order of $10^{-3}$. This results in a different overall normalization between equations~(\\ref{estimator}) and~(\\ref{LS_estimator}), making their amplitudes differ by a constant factor of about $1.2$. When we rescale one of the void-galaxy correlation functions by this number, we find the results from these two estimators to be virtually indistinguishable. Because we use the same estimator for $\\xi^s(s_\\parallel,s_\\perp)$ and the projected correlation function $\\xi^s_p(s_\\perp)$, which is used to infer the real-space $\\xi(r)$ via equation~(\\ref{xi_d}), any such normalization constant gets absorbed on both sides of equation~(\\ref{xi^s_lin}) and therefore has no effect on our model parameters.\n\n\n\\subsubsection{Covariance matrix}\n\\begin{figure}[t]\n\t\\centering\n\t\\resizebox{\\hsize}{!}{\n\t\t\\includegraphics[trim=5 10 5 5, clip]{fig\/cov_data.pdf}\n\t\t\\includegraphics[trim=5 10 5 5, clip]{fig\/cov_mock.pdf}}\n\t\\caption{Covariance matrix (normalized by its diagonal components) of the stacked void-galaxy cross-correlation function $\\xi^s(s_\\parallel,s_\\perp)$ from the BOSS DR12 data (left) and the PATCHY mocks (right).}\n\t\\label{fig:covariance}\n\\end{figure}\nAs a last consistency test we investigate the impact of the covariance matrix on our results. The left panel of figure~\\ref{fig:covariance} shows the covariance matrix estimated using the jackknife technique on the BOSS data as described in section~\\ref{subsec:estimators}, normalized by its diagonal components (i.e., the correlation matrix $\\mathbf{C}_{ij}\/\\sqrt{\\mathbf{C}_{ii}\\mathbf{C}_{jj}}\\,$). Note that this matrix contains $N_\\mathrm{bin}^2=(18^2)^2$ elements for the covariance of the two-dimensional correlation function $\\xi^s(s_\\parallel,s_\\perp)$. In order to overcome the statistical noise in the data covariance, we can measure the same quantity for all voids in our $N_\\mathrm{m}=30$ independent mock catalogs. The result is shown in the right panel figure~\\ref{fig:covariance}, featuring a very similar structure as for the real data. In our main analysis we have used the data covariance for the sake of maintaining an entirely calibration-independent approach. However, when exchanging it by the mock covariance in our likelihood from equation~(\\ref{likelihood}), we obtain posteriors that are consistent with our previous results, which is why we do not show them again. This suggests that the estimation of the covariance matrix from the data itself provides a sufficiently precise method allowing us to obtain fully calibration-independent constraints on cosmology from the observed sample of voids.\n\n\\subsection{Comparison to previous work \\label{subsec:comparison}}\n\\begin{figure}[t]\n\t\\centering\n\t\\resizebox{\\hsize}{!}{\n\t\t\\includegraphics[trim=30 50 60 80, clip]{fig\/fs8_DAH.pdf}}\n\t\\caption{Comparison of constraints on $f\\sigma_8$ and $D_\\mathrm{A} H$ (mean values with $68\\%$ confidence regions) obtained from cosmic voids in the literature, references are ordered chronologically in the figure legend. To improve readability, $D_\\mathrm{A} H$ is normalized by its reference value $D_\\mathrm{A}'H'$ in the Planck 2018 flat $\\Lambda$CDM cosmology~\\cite{Planck2018} (gray line with shaded error band). Filled markers indicate growth rate measurements without consideration of the AP effect, while open markers include the AP test. The line style of error bars indicates various degrees of model assumptions made: model-independent (solid), calibrated on simulations (dashed), calibrated on mocks (dotted), calibrated on simulations and mocks (dash-dotted). All the employed simulations and mocks assume a flat $\\Lambda$CDM cosmology. Data points at similar redshifts have been slightly shifted horizontally to avoid overlap.}\n\t\\label{fig:comparison}\n\\end{figure}\nThe observational AP test with cosmic voids has already experienced some history since it was first proposed by Lavaux and Wandelt in 2009~\\cite{Lavaux2010} and its first measurement by Sutter et al. in 2012~\\cite{Sutter2012b}. The early measurements of the AP effect did not yet account for RSD distortions by a physically motivated model, but they calibrated its impact using simulations~\\cite{Sutter2014b,Mao2017}. The first joint RSD and AP analysis from observed cosmic voids has been published in 2016 based on BOSS DR11 data~\\cite{Hamaus2016}. It demonstrated for the first time that a percent-level accuracy on $\\varepsilon$ can be achieved with voids, making them the prime candidate for observational AP tests. Since then, a number of papers appeared that either focused on the RSD analysis of voids exclusively in order to constrain the growth rate~\\cite{Achitouv2017a,Hawken2017,Hamaus2017,Achitouv2019,Hawken2020}, or performed further joint analyses including the AP effect~\\cite{Nadathur2019b}.\n\nWe summarize the constraints on $f\\sigma_8$ and $D_\\mathrm{A} H$ that have been obtained from voids throughout the literature in figure~\\ref{fig:comparison}, including the results from this paper. Evidently, the different analysis techniques have progressed over time and achieved significant improvements of accuracy. Moreover, spectroscopic data from a number of surveys covering different redshift ranges has been exploited to this end, including 6dFGS~\\cite{6dFGS}, BOSS~\\cite{BOSS}, eBOSS~\\cite{EBOSS}, SDSS~\\cite{SDSS}, and VIPERS~\\cite{VIPERS}. All of the published results are consistent with a flat $\\Lambda$CDM cosmology, in agreement with the measurements by Planck~\\cite{Planck2018}. However, some of the analyses have been calibrated using simulations and \/ or mocks to determine unknown model ingredients. If such external information has been used and has not been marginalized over, we indicate the calibrated results in figure~\\ref{fig:comparison} by different line styles of error bars, as described in the caption. A comparison based on equal terms can only be made by taking this essential fact into account. In addition to this, there are a number of analysis choices that differ among the published results. In the following we provide a list of aspects that we have investigated in more detail and encountered to be relevant for our results.\n\n\\subsubsection{Void finding in real vs. redshift space}\nLike most papers on the topic of void RSD in the literature, we define voids in observable redshift space. The recent analysis of reference~\\cite{Nadathur2019b} advocates the use of reconstruction techniques to identify voids in real space instead. Their centers in real space are then correlated with the original galaxy positions in redshift space to estimate a hybrid real\/redshift space void-galaxy cross-correlation function. We have investigated this approach using halo catalogs from $N$-body simulations and calculated the resulting two-point statistics. We confirm that this results in a more elongated shape of $\\xi^s(s_\\parallel,s_\\perp)$ along the line of sight and a change of sign in its quadrupole at small void-centric separations. This can be readily understood from the illustration in figure~\\ref{fig:voidstretch}: the separation vector between a void center in real space and one of the void's galaxies in redshift space is now given by the gray dashed line, which is more elongated along the line of sight because it contains a contribution from the velocity of the void center, $\\tilde{\\mathbf{s}}=\\mathbf{r}+\\mathbf{u}_\\parallel+\\mathbf{V}_\\parallel$ (with velocities in units of $(1+z_h)\/H$). Another consequence of this approach is that for void velocities with $|\\mathbf{V}_\\parallel|\\gtrsim R$, a significant number of galaxies from neighboring voids in redshift space will be closer to the void center in real space than the void's own member galaxies. As a result, the void-galaxy cross-correlation function contains different contributions from galaxies of the same and neighboring voids, depending on the magnitude of $\\mathbf{V}_\\parallel$. As this effect is difficult to model from first principles, reference~\\cite{Nadathur2019b} resorts to the use of mock catalogs to calibrate the form of this hybrid correlation function, and restricts its analysis to the largest $50\\%$ of all voids.\n\nThe motivation for velocity field reconstruction was grounded on the claim that the velocities $\\mathbf{V}$ of void centers cannot be accounted for in all existing models for the void-galaxy cross-correlation function in redshift space~\\cite{Nadathur2019b}. This presumption is unfounded, as these models have actually been derived assuming local mass conservation relative to the motion of the void center~\\cite{Paz2013,Hamaus2015,Cai2016}. While it is true that absolute void velocities $\\mathbf{V}$ are difficult to predict, the same holds for the absolute galaxy velocities $\\mathbf{v}$ in the vicinity of void centers. Both $\\mathbf{V}$ and $\\mathbf{v}$ contain bulk-flow contributions sourced by density fluctuations on scales beyond the void's extent. However, local mass conservation provides a very good prediction for their difference $\\mathbf{u}$, as discussed in section~\\ref{subsec:dynamic}. A consequence of this is a vanishing hexadecapole $\\xi^s_4(s)$, as explained in reference~\\cite{Cai2016} and confirmed by our analysis. We further note that the galaxy velocity field $\\mathbf{v}$ is anisotropic around void centers in redshift space, as expected from figure~\\ref{fig:voidstretch}. The model derived in section~\\ref{subsec:correlation} merely assumes statistical isotropy of the field $\\mathbf{u}$ in real space, which follows from the cosmological principle. Reference~\\cite{Nadathur2019a} speculated about a potential selection effect in favor of voids with higher outflow velocities and hence lower observed central densities in redshift space. As explained in section~\\ref{subsec:voids}, our void finder operates on local minima and their surrounding watershed basins, irrespective of any absolute density threshold. Such a selection effect therefore cannot affect voids identified with~\\textsc{vide} or~\\textsc{zobov}.\n\nAnother argument for the use of reconstruction was motivated by the impact of redshift-space distortions on the void-size function~\\cite{Nadathur2019a}. We note that the effective radii for voids of any size are expected to change between real and redshift space due to dynamic distortions, as evident from figure~\\ref{fig:voidstretch}. However, we only use the observed effective void radii as units to express all separations in either space, which leaves the mapping between $\\mathbf{r}$ and $\\mathbf{s}$ unchanged. A problematic impact of this mapping can be the destruction of voids from catastrophic redshift-space distortions, such as the FoG effect, or from shot noise due to the sparsity of tracers that may change the topology of watershed basins. Because the FoG effect is limited to scales of a few $h^{-1}{\\rm Mpc}$ and smaller voids are defined by fewer tracers, this problem becomes more relevant for voids of relatively small size. We account for this potential systematic via marginalization over the nuisance parameters introduced in section~\\ref{subsec:parameters}.\n\nIn conclusion, velocity field reconstruction is not required to account for the dynamic distortions of voids, as evident from this paper and numerous earlier works~\\cite{Paz2013,Hamaus2016,Cai2016,Achitouv2017a,Hawken2017,Hamaus2017,Correa2019,Achitouv2019,Hawken2020}. The velocity field reconstruction technique merely offers an alternative approach to model RSDs around voids, in addition to the existing models. If reconstruction is used in conjunction with another RSD model, dynamic distortions are unnecessarily taken into account twice. The disadvantages of reconstruction include its dependence on a smoothing scale, assumptions on tracer bias and growth rate relations, as well as its sensitivity to survey edges and shot noise~(e.g., \\cite{Sherwin2019,Philcox2020}). Last but not least, reconstruction makes the data a function of the theory model. Vice-versa, calibration of the theory model on survey mocks that are informed by the data generates an inverse dependence. If information from the mocks is used in the model, theory and data are intertwined to a degree that makes a rigorous likelihood analysis much more involved. Moreover, this practice forfeits the criteria necessary for an independent model validation. The authors of reference~\\cite{Nadathur2019b} claim their analysis to be ``free of systematic errors'', but neglect its systematic dependence on the assumed mock cosmology.\n\n\\subsubsection{Void center definition}\nBecause voids are aspherical by nature, the definition of their centers is not unique. In observations, which typically provide the 3D locations (but not the 3D velocities) of tracers that outline each void, there are in practice two options: the point of minimum tracer density inside the void, or the geometric center defined by the void boundary. Minimum-density centers can be defined as maximally extended empty spheres in a tracer distribution~\\cite{Zhao2016}, without requiring the sophistication of a watershed algorithm. The optimal choice of center definition depends on the specific type of application, so it is not possible to make general statements about this. However, for the sake of measuring geometric distortions via the AP effect it is desirable to enhance the amplitude of tracer fluctuations around their background density to increase the signal-to-noise ratio of anisotropic clustering measurements. As described in section~\\ref{subsec:voids}, the geometric center (barycenter) retains information about the void boundary and thereby generates a pronounced compensation wall in its cross-correlation with galaxies at a separation of one effective void radius $R$. On the other hand, the minimum-density center produces a stronger negative amplitude of the void-galaxy cross-correlation function at small separations. The number of tracers in a shell of width $\\mathrm{d}s$ grows as $s^2$ for a constant tracer density, and even faster for increasing density with $s$, as is the case inside voids. Therefore the coherent compensation walls around void barycenters serve as a lever arm to provide significantly higher signal-to-noise ratios for measurements of anisotropic clustering and hence the AP effect. We have checked this explicitly by repeating our analysis using minimum-density centers, which results in less pronounced compensation walls in $\\xi^s(s_\\parallel,s_\\perp)$, a lower amplitude of its quadrupole, and an uncertainty on the AP parameter $\\varepsilon$ of roughly double the size.\n\n\\subsubsection{Void stacking}\nThe method of void stacking is related to the previous aspect, as it affects the void-galaxy cross-correlation function in a similar way. Because voids are objects of finite extent, the correlation of their centers with tracers inside or outside their boundaries is qualitatively different~\\cite{Hamaus2014a,Chan2014,Cai2016,Voivodic2020}. This is analogous to the halo model, which ascribes two different contributions to the clustering properties of matter particles, those inside the same halo, and those among different halos~\\cite{Seljak2000,Peacock2000}. Therefore, in order to capture the characteristic clustering properties of tracers inside a sample of differently sized voids, one typically rescales the tracer coordinates by the effective radius of their respective host void, a method referred to as void stacking. This guarantees that the void boundaries coherently overlap at a separation of $s=R$, and thus creates a strong compensation wall feature in the stacked void-galaxy cross-correlation function. Without the rescaling procedure, compensation walls of different-size voids do not aggregate, which results in a smeared out correlation function with almost no feature remaining at $s=R$. This smearing in turn is disadvantageous for measurements of AP distortions, following the arguments discussed above. A similar effect can be caused by stacking voids of different evolutionary stages from a wide range of redshifts. For example, the properties of void galaxies are expected to be redshift-dependent~\\cite{Kreckel2011a,Kreckel2011b}. This can be accounted for by splitting the void sample into redshift bins. However, in the BOSS DR12 data and the PATCHY mocks we find a very mild evolution with redshift, allowing us to average over the full void sample.\n\n\\subsubsection{Correlation function estimation}\nIt is common practice to estimate correlation functions via counts in shells, i.e. by counting the number of tracers and randoms inside a spherical shell of width $\\mathrm{d}s$ at separation $s$. In the interiors of voids, however, the density of tracers is low by construction, which can result in shells with insufficiently low tracer counts to reliably estimate correlation functions that are intended to infer properties of the density field (such as its growth rate $f$). The previously discussed method on void stacking helps in this respect, as one may collect the tracers that fall into a given shell from all rescaled voids of the entire sample. The convergence of the estimator can then be assessed by increasing the void sample size, as we have done using mocks in section~\\ref{subsec:validation}. Within our approach we find no dependence of the estimators on sample size, in support of the conclusion that our correlation function statistics have converged. However, if shells with very few or no tracers are encountered in every single void, the counts-in-shell estimator yields biased results, even in the limit of infinite sample size~\\cite{Nadathur2015}.\n\nThis is particularly relevant for shells in the vicinity of the minimum-density center, which exhibits no nearby tracers by construction. As a consequence, the counts-in-shell estimator yields a value of $\\xi=-1$ for all empty shells, regardless of the nature of the tracer distribution. In fact, this is even the case for empty shells in a random distribution of tracers, an example that reveals the limitations of this estimator most clearly. As its name suggests, the counts-in-shell estimator is only defined for non-zero object counts, but returns meaningless results otherwise. Towards larger scales, as soon as the first tracers are encountered in a shell at separation $s_\\mathrm{d}$, the correlation estimate abruptly jumps to a value significantly higher than $\\xi=-1$, and finally converges to a smooth curve at separations with sufficient tracer counts~\\cite{Nadathur2019b}. If voids are not rescaled by their effective radius, this results in a kink in the average correlation function at $s=s_\\mathrm{d}$ even for arbitrarily large sample sizes. A similar behavior can be observed when estimating the radial velocity profile of the tracers~\\cite{Nadathur2019a}. The resulting bias in the counts-in-shell estimator on small scales breaks the validity of equations~(\\ref{u(r)}) and~(\\ref{xi(delta)}), which only apply in the continuous limit of high tracer counts, and can be misinterpreted as evidence for an intrinsic non-linearity or stochasticity of the tracer density field.\n\nThe shell at separation $s_\\mathrm{d}$ indicates the discreteness limit of the tracer distribution. It is determined by the average density of tracers and therefore unrelated to cosmologically induced clustering statistics that can be measured on larger scales. Moreover, discreteness artifacts are notoriously difficult to model from first principles due to their unphysical nature, which leaves no option other than to calibrate them via mock catalogs. In reference~\\cite{Nadathur2019a} it is argued that the void-galaxy cross-correlation function exhibits a ``feature'' both in its monopole and quadrupole at separation $s_\\mathrm{d}$, which is calibrated on mocks to be used for AP distortion measurements in a later publication~\\cite{Nadathur2019b}. It yet remains to be demonstrated whether such features at the discreteness limit of an estimator are of any use for the AP test. They similarly arise in scale-free Poisson distributions, which are insensitive to geometric distortions for the lack of spatial correlations and therefore satisfy the condition $\\varepsilon=1$ in any coordinate system. Consequently, in such a scenario the AP test necessarily returns the fiducial cosmology and therefore becomes dominated by confirmation bias. Changing the fiducial cosmology provides a useful sanity check to exclude the presence of confirmation bias, as we have shown in section~\\ref{subsec:systematics}.\n\n\\subsubsection{RSD model}\nWe have performed extensive tests to compare the existing RSD models for voids that are available in the literature. This essentially concerns the GSM for voids as proposed by Paz et al.~\\cite{Paz2013}, the linear model of Cai et al.~\\cite{Cai2016} used in this paper, and variants thereof. We find consistent results with the GSM, albeit with slightly weaker constraints on $f\/b$ and $\\varepsilon$ due to marginalization over the additional velocity dispersion parameter~$\\sigma_v$. Moreover, as the GSM requires an integration over the pairwise velocity probability distribution function in every bin of $\\xi^s(s_\\parallel,s_\\perp)$, it significantly slows down the model evaluation. We find the impact of velocity dispersion to marginally affect our fits to the data, so we settled on the simpler linear model from equation~(\\ref{xi^s_lin2}).\n\nFurthermore, we explored extensions of the linear model, such as the full non-linear expression~(\\ref{xi^s_nonlin2}). We also tested the model extension proposed in equation (14) of reference~\\cite{Nadathur2019a}, which contains terms of linear and second order in $\\delta$. Note that in this model, every occurrence of the parameter $f$ is multiplied by a factor of $1+\\xi(r)$, unfolding an additional degeneracy between the growth rate and the amplitude of $\\xi(r)$, which depends on $b$ and $\\sigma_8$. Moreover, that model requires the void mass density profile $\\delta(r)$ from simulations in addition to $\\xi(r)$ from the mocks, making it even more dependent on the calibration input. However, none of these extensions improves our fits to either data or mocks. We suspect that a rigorous model at the non-linear level must additionally involve an extension of the linear mass conservation relation that leads to equation~(\\ref{u(r)}), as suggested by reference~\\cite{Achitouv2017b}. Nevertheless, our analysis of the final BOSS data does not indicate any limitations of the simplest linear model from equation~(\\ref{xi^s_lin}), in agreement with previous analyses~\\cite{Hamaus2017,Achitouv2019}.\n\n\n\\section{Conclusion\\label{sec:conclusion}}\nWe have presented a comprehensive cosmological analysis of the geometric and dynamic distortions of cosmic voids in the final BOSS dataset. The extracted information is condensed into constraints on two key quantities, the RSD parameter $f\/b$, and the AP parameter $\\varepsilon$. When calibrated on survey mocks, our analysis provides a relative accuracy of $6.6\\%$ on $f\/b$ and $0.60\\%$ on $\\varepsilon$ (at $68\\%$ confidence level) from the full void sample at a mean redshift of $0.51$. This represents the tightest growth rate constraint obtained from voids in the literature. The AP result even represents the tightest constraint of its kind. However, as these results are calibrated by mock catalogs from a fixed fiducial cosmology, they need to be taken with a grain of salt. Without calibration we are still able to self-consistently model the data, obtaining a relative accuracy of $16.9\\%$ on $f\/b$ and $0.68\\%$ on $\\varepsilon$. While the weaker AP constraint still remains unrivaled, the degradation in the uncertainty on $f\/b$ is mainly due to its strong anti-correlation with the amplitude of the real-space void-galaxy cross-correlation function $\\xi(r)$, which we jointly infer from the data. We emphasize that these uncalibrated constraints are entirely independent from any prior model assumptions on cosmology, or structure formation involving baryonic components, and do not rely on mocks or simulations in any way. They exclusively emerge from the observed data and a linear-theory model that is derived from first principles. With the additional validation of this model on external survey mocks with much higher statistical power, these constraints are robust. The quality of the BOSS data even allows us to analyze sub-samples of voids in two redshift bins. This decreases the mean accuracy per bin by roughly a factor of $\\sqrt{2}$, as expected for statistically independent samples.\n\nWe account for potential systematics in our analysis via a marginalization strategy. To this end we include two nuisance parameters in the model: $\\mathcal{M}$ for modulating the amplitude of the deprojected real-space void-galaxy cross-correlation function $\\xi(r)$, and $\\mathcal{Q}$ for adjusting the quadrupole amplitude. The first parameter accounts for inaccuracies in the deprojection technique and a possible contamination of our void sample by random Poisson fluctuations that can be mistaken as voids with a shallow density profile. The second parameter accounts for anisotropic selection effects due to catastrophic RSDs (such as the FoG effect) or shot noise, that can affect the identification of voids. In the PATCHY mocks we find significant evidence for $\\mathcal{M}>1$, and very marginal evidence for $\\mathcal{Q}>1$, while in the BOSS data both parameters are consistent with unity (to within $68\\%$ confidence) in our low-redshift bin. At higher redshift we observe a mild indication of $\\mathcal{M}>1$ and $\\mathcal{Q}<1$, but only at the significance level of $2\\sigma$ at best. This observation leads us to draw the following conclusion: survey mocks do not necessarily account for all aspects of, and systematics in, the data. They typically represent different realizations drawn from one and the same set of cosmological and astrophysical parameters. Therefore, using the mocks for model calibration may lead to biased constraints on cosmology. This is particularly relevant in situations where model extensions from the fiducial mock cosmology are explored, such as curvature, the properties of dark energy, or massive neutrinos. In addition, this practice underestimates parameter uncertainties, by up to a factor of $4$ for constraints on growth from RSDs. \n\nAs a final remark, we emphasize the important role that cosmic voids will play in the cosmological analysis of future datasets with a much larger scope. Both observatories from the ground~\\cite{DESI,LSST} and from space~\\cite{EUCLID,SPHEREX,WFIRST} will soon provide an unprecedented coverage of large-scale structure in the local Universe, increasing the available sample sizes of voids to at least an order of $10^5$ per survey. This implies that currently achievable error bars on RSD and AP parameters will be further reduced by a factor of a few, potentially allowing us to perform precision cosmology at the level of sub-percent accuracy that could bring about deep ramifications concerning the standard model of cosmology. In this work we have merely investigated flat $\\Lambda$CDM, which exhibits no signs of inconsistency with the final BOSS data.\n\n\n\\begin{acknowledgments}\nWe thank David Spergel for reading over our manuscript and providing pertinent feedback and suggestions. NH would like to thank Kerstin Paech for exchange on coding best practices, Giorgia Pollina for useful suggestions to improve the quality of figures, and Enzo Branchini, Chia-Hsun Chuang, Carlos Correa, Luigi Guzzo, Martha Lippich, Nelson Padilla, Ariel S\\'anchez, Ravi Sheth, Sergey Sibiryakov, Rien van de Weygaert, and Simon White for inspiring discussions about voids at the MIAPP 2019 workshop on ``Dynamics of Large Scale Structure Formation'' in Garching. NH and JW are supported by the Excellence Cluster ORIGINS, which is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy -- EXC-2094 -- 390783311. AP is supported by NASA grant 15-WFIRST15-0008 to the Nancy Grace Roman Space Telescope Science Investigation Team ``Cosmology with the High Latitude Survey''. GL and BDW acknowledge financial support from the ANR BIG4 project, under reference ANR-16-CE23-0002. The Center for Computational Astrophysics is supported by the Simons Foundation.\n\nFunding for SDSS-III~\\cite{SDSS} has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the U.S. Department of Energy Office of Science. SDSS-III is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS-III Collaboration including the University of Arizona, the Brazilian Participation Group, Brookhaven National Laboratory, Carnegie Mellon University, University of Florida, the French Participation Group, the German Participation Group, Harvard University, the Instituto de Astrofisica de Canarias, the Michigan State\/Notre Dame\/JINA Participation Group, Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington, and Yale University.\n\\end{acknowledgments}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\n\n\nThe nature of dark matter and dark energy remains one of the greatest\nmysteries of modern cosmology. Dark matter is responsible for the flat rotation\ncurves of the galaxies and dark energy is responsible for the accelerated\nexpansion of the Universe. It is found that dark energy represents about $70\\%$\nof the energy content of the present Universe while the proportions of dark\nmatter and baryonic matter are $25\\%$ and $5\\%$ respectively.\n\n\n\nIn a previous paper \\cite{epjp} (see also \\cite{lettre,jcap}) we have\nintroduced a new cosmological\nmodel that we called\nthe logotropic model. In this model, there is no dark matter and no dark energy.\nThere is just a single dark fluid. What we call ``dark matter'' actually\ncorresponds to its rest-mass energy and what we call ``dark energy'' corresponds\nto its internal energy.\\footnote{Many models try to\nunify dark matter and dark energy. They are called unified dark energy and dark\nmatter (UDE\/M) models. However, the interpretation of dark matter\nand dark energy that we give in Refs. \\cite{epjp,lettre} is new and\noriginal.}\n\nOur model does not contain any arbitrary parameter so that it is totally\nconstrained. It involves a fundamental constant $\\Lambda$ which is the\ncounterpart of Einstein's cosmological constant \\cite{einsteincosmo} in the\n$\\Lambda$CDM (cold dark\nmatter) model and which turns out to have the same value. Still the\nlogotropic model\nis\nfundamentally different from the $\\Lambda$CDM model. \n\n\nOn the large (cosmological) scales, the logotropic model is indistinguishable\nfrom the $\\Lambda$CDM model up to the present epoch \\cite{epjp,lettre,jcap}. The\ntwo\nmodels\nwill differ in the far future, in about $25\\, {\\rm Gyrs}$ years, after which the\n logotropic model will become phantom (the\nenergy density will increase as\nthe Universe expands)\nand present a Little Rip (the\nenergy density and the scale factor will become infinite in\ninfinite time) contrary to the $\\Lambda$CDM\nmodel in which the energy density tends towards a constant \n(de Sitter era).\n\nOn the small (galactic) scales, the logotropic model is able to solve\nsome of the problems encountered by the $\\Lambda$CDM model \\cite{epjp,lettre}.\nIn\nparticular, it is able to account, without free parameter, for the\nconstant surface density of the dark matter halos, for their mass-radius\nrelation, and for the Tully-Fisher relation. \n\nIn this paper, we explore other consequences of this model. By advocating a form\nof ``strong cosmic coincidence'', stating that the present value of\nthe dark energy density $\\rho_{\\rm de, 0}$ is equal to the fundamental constant\n$\\rho_\\Lambda$ appearing in the logotropic model, we predict that\nthe present proportion of dark energy in the Universe is $\\Omega_{\\rm\nde,0}=e\/(1+e)=0.731$ which is close to the observed value\n$0.691$ \\cite{planck2016}. The\nconsequences of this result, which implies that our epoch is very special in the\nhistory of the Universe, are intriguing and related to a form of\n anthropic cosmological\nprinciple \\cite{barrow}.\n\nWe also remark that the universal surface density of dark matter halos (found\nfrom the observations \\cite{donato} and predicted by our model\n\\cite{epjp,lettre}) and the surface density of\nthe Universe are of the same order of magnitude as the surface density of the\nelectron.\nThis makes a curious connection between cosmological and atomic scales.\nExploiting this coincidence, we can relate the Hubble constant, the\nelectron mass and the electron charge to the\ncosmological constant $\\Lambda$. We also argue that the famous\nnumbers $137$ (fine-structure constant) and $123$ (logotropic\nconstant) may actually represents\nthe same thing. This may be a hint for a theory of\nunification of microphysics and cosmophysics. Speculations are made in the\nAppendices to try to relate these interconnections to a form of holographic\nprinciple \\cite{bousso} stating that the entropy of the electron, the\nentropy of dark matter\nhalos, and the entropy of the Universe scales like their area as in the case of\nthe entropy of black\nholes \\cite{bekenstein,hawking}.\n\n\n\n\n\\section{The logotropic model}\n\\label{sec_lm}\n\n\\subsection{Unification of dark matter and dark energy}\n\nThe Friedmann equations for a flat universe without cosmological constant\nare \\cite{weinbergbook}:\n\\begin{equation}\n\\frac{d\\epsilon}{dt}+3\\frac{\\dot a}{a}(\\epsilon+P)=0,\\quad H^2=\\left\n(\\frac{\\dot a}{a}\\right )^2=\\frac{8\\pi\nG}{3c^2}\\epsilon,\n\\label{lm1}\n\\end{equation}\nwhere $\\epsilon(t)$ is the energy density of the Universe, $P(t)$ is the\npressure, $a(t)$ is the\nscale factor, and $H=\\dot a\/a$ is the Hubble parameter.\n\n\n\nFor a relativistic fluid experiencing an\nadiabatic evolution such that $Td(s\/\\rho)=0$, the first law of\nthermodynamics\nreduces to \\cite{weinbergbook}:\n\\begin{equation}\nd\\epsilon=\\frac{P+\\epsilon}{\\rho}d\\rho,\n\\label{lm2}\n\\end{equation}\nwhere $\\rho$ is the rest-mass density of the Universe. Combined\nwith the equation of continuity\n(\\ref{lm1}), we get\n\\begin{equation}\n\\frac{d\\rho}{dt}+3\\frac{\\dot a}{a}\\rho=0 \\Rightarrow \\rho=\\frac{\\rho_0}{a^3},\n\\label{lm3}\n\\end{equation}\nwhere $\\rho_0$ is the present value of the rest-mass density (the\npresent\nvalue of the scale factor is taken to be $a=1$). This equation, which\nexpresses\nthe conservation of the rest-mass, is valid\nfor an arbitrary\nequation\nof state. \n\n\nFor an equation of state specified under the form $P=P(\\rho)$, Eq.\n(\\ref{lm2}) can be integrated to obtain the relation between the energy density\n$\\epsilon$ and the rest-mass density. We obtain \\cite{epjp}:\n\\begin{equation}\n\\epsilon=\\rho c^2+\\rho\\int^{\\rho}\\frac{P(\\rho')}{{\\rho'}^2}\\, d\\rho'=\\rho\nc^2+u(\\rho).\n\\label{lm4}\n\\end{equation}\nWe note that $u(\\rho)$\ncan be interpreted as an internal energy density \\cite{epjp}. Therefore, the\nenergy density $\\epsilon$ is the sum of the rest-mass energy $\\rho c^2$ and the\ninternal energy $u(\\rho)$.\n\n\n\n\\subsection{The logotropic dark fluid}\n\\label{sec_ldf}\n\nWe assume that the Universe is filled with a single dark fluid described by\nthe\nlogotropic equation of state \\cite{epjp}:\n\\begin{equation}\nP=A\\ln\\left (\\frac{\\rho}{\\rho_P}\\right ),\n\\label{lm5}\n\\end{equation}\nwhere $\\rho_P=c^5\/\\hbar G^2=5.16\\times 10^{99}\\, {\\rm g\\, m^{-3}}$ is the Planck\ndensity and $A$ is a new fundamental constant\nof physics, with the dimension of an energy density, which is the\ncounterpart of the cosmological constant $\\Lambda$ in the $\\Lambda$CDM model\n(see below).\nUsing Eqs. (\\ref{lm4}) and (\\ref{lm5}), the relation\nbetween the energy density and the rest-mass density is\n\\begin{equation}\n\\epsilon=\\rho c^2-A\\ln \\left (\\frac{\\rho}{\\rho_P}\\right )-A=\\rho c^2+u(\\rho).\n\\label{lm6}\n\\end{equation}\nThe energy density is the\nsum of two\nterms: a rest-mass energy term $\\rho c^2=\\rho_0c^2\/ a^{3}$ that mimics the\nenergy density $\\epsilon_{\\rm m}$ of dark matter and\nan internal energy term $u(\\rho)=-A\\ln \\left ({\\rho}\/{\\rho_P}\\right )-A\n=-P(\\rho)-A=3A\\ln a-A\\ln(\\rho_0\/\\rho_P)-A$\nthat mimics the\nenergy density $\\epsilon_{\\rm de}$ of dark energy. This\ndecomposition\nleads to a natural, and physical, unification of dark matter and dark energy and\nelucidates their\nmysterious nature. \n\nSince, in our model, the rest-mass energy of the dark fluid mimics dark matter,\nwe\nidentify $\\rho_0c^2$ with the present energy density of dark matter. We thus\nset $\\rho_0c^2=\\Omega_{\\rm m,0}\\epsilon_0$,\nwhere $\\epsilon_0\/c^2={3H_0^2}\/{8\\pi G}$ is the\npresent energy density of the Universe and \n$\\Omega_{\\rm m,0}$ is the present fraction of dark matter (we also include\nbaryonic\nmatter). As a result, the present internal energy of the dark\nfluid, $u_0=\\epsilon_0-\\rho_0c^2$, is identified with the present\ndark energy density $\\epsilon_{\\rm de,0}=\\Omega_{\\rm de,0}\\epsilon_0$\nwhere\n$\\Omega_{\\rm de,0}=1-\\Omega_{\\rm m,0}$ is the present\nfraction of dark energy. Applying Eq. (\\ref{lm6}) at the present epoch ($a=1$),\nwe\nobtain the identity\n\\begin{equation}\nA=\\frac{\\epsilon_{\\rm de,0}}{\\ln\\left(\\frac{\\rho_Pc^2}{\\epsilon_{\\rm\nde,0}}\\right\n)+\\ln\\left (\\frac{\\Omega_{\\rm de,0}}{1-\\Omega_{\\rm de,0}}\\right )-1}.\n\\label{lm7}\n\\end{equation}\nAt that stage, we can have two points of view. We can consider that this\nequation determines the constant $A$ as a function of\n$\\epsilon_{0}$ and $\\Omega_{\\rm de,0}$ that are both obtained from the\nobservations \\cite{planck2016}. This allows us to determine the value of $A$.\nThis is the\npoint of view that we have adopted in our previous papers \\cite{epjp,lettre} and\nthat we adopt in Sec. \\ref{sec_B} below. However, in the following section,\nwe present another point of view leading to an intriguing result.\n\n\n\n\\subsection{Strong cosmic coincidence and\nprediction of $\\Omega_{\\rm de,0}$}\n\nLet us recall that, in our model, $A$ is considered as a fundamental constant\nwhose value is fixed by Nature. As a result, Eq. (\\ref{lm7}) relates\n$\\Omega_{\\rm de,0}$ to $\\epsilon_{0}$ for a given value of $A$. A priori, we\nhave two unknowns for just one equation. However, we can\nobtain the value of $\\Omega_{\\rm de,0}$ by the following argument. \n\n\nWe can always write the constant $A$ under the form\n\\begin{equation}\nA=\\frac{\\rho_{\\Lambda}c^2}{\\ln\\left(\\frac{\\rho_P}{\\rho_{\\Lambda}}\\right\n)}.\n\\label{lm8}\n\\end{equation}\nThis is just a change of notation. Eq. (\\ref{lm8}) defines a new\nconstant, the cosmological\ndensity $\\rho_{\\Lambda}$, in place of $A$. From the cosmological density\n$\\rho_{\\Lambda}$,\nwe can define an effective cosmological constant $\\Lambda$ by\\footnote{We stress\nthat our\nmodel is different from the $\\Lambda$CDM model so that $\\Lambda$ is\nfundamentally different from Einstein's cosmological constant\n\\cite{einsteincosmo}. However, it is always possible to introduce from the\nconstant $A$ an effective\ncosmological density $\\rho_{\\Lambda}$ and an effective cosmological constant\n$\\Lambda$ by Eqs. (\\ref{lm8}) and (\\ref{lm9}).}\n\\begin{equation}\n\\rho_{\\Lambda}=\\frac{\\Lambda}{8\\pi G}.\n\\label{lm9}\n\\end{equation}\nAgain this is just a change of notation. Therefore, the fundamental constant\nof our model is either $A$, $\\rho_{\\Lambda}$ or $\\Lambda$ (equivalently). We now\nadvocate a form of ``strong cosmic coincidence''. We assume that the present\nvalue of the dark energy density is equal to $\\rho_{\\Lambda}c^2$, i.e.,\n\\begin{equation}\n\\epsilon_{\\rm de,0}=\\rho_{\\Lambda}c^2.\n\\label{lm10}\n\\end{equation}\nSince, in the $\\Lambda$CDM model, $\\epsilon_{\\rm de}$ is a constant usually\nmeasured at the present epoch our\npostulate implies that $\\rho_{\\Lambda}c^2$ coincides with the\ncosmological density in the $\\Lambda$CDM model and that $\\Lambda$, as defined\nby Eq. (\\ref{lm9}), coincides with\nthe ordinary cosmological constant. This is why we have used the same\nnotations. Now, comparing Eqs. (\\ref{lm7}), (\\ref{lm8}) and (\\ref{lm10}) we\nobtain $\\ln\\left\n\\lbrack \\Omega_{\\rm de,0}\/(1-\\Omega_{\\rm de,0})\\right \\rbrack-1=0$\nwhich determines $\\Omega_{\\rm de,0}$. We find that\n\\begin{equation}\n\\Omega_{\\rm de,0}^{\\rm th}=\\frac{e}{1+e}\\simeq 0.731\n\\label{lm11}\n\\end{equation}\nwhich is close to the observed value $\\Omega_{\\rm de,0}^{\\rm\nobs}=0.691$ \\cite{planck2016}. This agreement is puzzling. It\nrelies on the ``strong cosmic\ncoincidence'' of Eq. (\\ref{lm10}) implying that our epoch is very special. This\nis a form of anthropic cosmological principle \\cite{barrow}. This may also\ncorrespond to a fixed point of our model. In order to avoid\nphilosophical issues, in the following, we adopt the more conventional\npoint of view discussed at the end of Sec. \\ref{sec_ldf}. \n\n\n\n\\subsection{The logotropic constant $B$}\n\\label{sec_B}\n\n\n\nWe can rewrite Eq. (\\ref{lm8}) as\n\\begin{equation}\nA=B\\rho_{\\Lambda}c^2\\qquad {\\rm\nwith}\\qquad B=\\frac{1}{\\ln\\left({\\rho_P}\/{\\rho_{\\Lambda}}\n\\right\n)}.\n\\label{lm12}\n\\end{equation}\nAgain, this is just a change of notation defining the dimensionless number\n$B$. We shall call it the logotropic constant since it is equal\nto the inverse of the logarithm of the cosmological density normalized by the\nPlanck density (see Appendix \\ref{sec_const}).\nWe note that $A$ can be expressed in terms of $B$ (see below) so that the\nfundamental constant of our model is either $A$, $\\rho_{\\Lambda}$, $\\Lambda$,\nor $B$. In the following, we shall express all the results in terms of $B$.\nFor example, the relation (\\ref{lm6}) between the energy density and the scale\nfactor can be rewritten as \n\\begin{equation}\n\\frac{\\epsilon}{\\epsilon_0}=\\frac{\\Omega_{\\rm\nm,0}}{a^3}+(1-\\Omega_{\\rm m,0})(1+3B\\ln\na).\n\\label{lm13}\n\\end{equation}\nCombined with the Friedmann equation (\\ref{lm1}) this equation determines the\nevolution of the scale factor $a(t)$ of the Universe in the logotropic model.\nThis evolution has been studied in detail\nin \\cite{epjp,lettre,jcap}. \n\n\n\n{\\it Remark:} Considering Eq. (\\ref{lm13}), we see that the\n$\\Lambda$CDM model is recovered for $B=0$.\nAccording to Eq. (\\ref{lm12}) this implies that\n$\\rho_P\\rightarrow +\\infty$, i.e., $\\hbar\\rightarrow 0$. Therefore, the \n$\\Lambda$CDM model corresponds to the semiclassical limit of the logotropic\nmodel. The fact that $B$ is intrinsically nonzero implies that\nquantum mechanics ($\\hbar\\neq 0$) plays some role in our model in addition to\ngeneral relativity. This may\nsuggest a link with a theory of quantum gravity. \n\n\n\n\\subsection{The value of $B$ from the\nobservations}\n\\label{sec_Bobs}\n\n\n\nThe fundamental constant ($A$, $\\rho_{\\Lambda}$, $\\Lambda$,\nor $B$) appearing in our model can be determined from the\nobservations by using Eq. (\\ref{lm7}). We take $\\Omega_{\\rm de,0}=0.6911$ and\n $H_0=2.195\\times 10^{-18}\\, {\\rm\ns^{-1}}$ \\cite{planck2016}. This implies $\\epsilon_0\/c^2=3H_0^2\/8\\pi\nG=8.62\\times 10^{-24}\\, {\\rm g\\, m^{-3}}$ and $\\epsilon_{\\rm\nde,0}\/c^2=\\Omega_{\\rm\nde,0}\\epsilon_0\/c^2=5.96\\times 10^{-24}\\, {\\rm g\\, m^{-3}}$. Since $\\ln\\left\n\\lbrack \\Omega_{\\rm de,0}\/(1-\\Omega_{\\rm de,0})\\right \\rbrack-1=-0.195$\nis small as\ncompared to $\\ln(\\rho_Pc^2\/\\epsilon_{\\rm de,0})=283$, we can write in very\ngood approximation $A$ as in Eq. (\\ref{lm8}) with $\\rho_{\\Lambda}\\simeq\n\\epsilon_{\\rm de,0}\/c^2$ as in Eq. (\\ref{lm10}). Therefore, \n\\begin{equation}\n\\rho_{\\Lambda}=\\frac{3\\Omega_{\\rm\nde,0}H_0^2}{8\\pi G}=5.96\\times 10^{-24}\\, {\\rm g\\, m^{-3}}\n\\label{lm14a}\n\\end{equation}\nand\n\\begin{equation}\n\\Lambda= 3\\Omega_{\\rm\nde,0}H_0^2=1.00\\times 10^{-35}\\, {\\rm s^{-2}}\n\\label{lm14b}\n\\end{equation}\nare approximately equal to\nthe cosmological density and to the cosmological constant in the $\\Lambda$CDM\nmodel. From Eq. (\\ref{lm12}) we get\n\\begin{equation}\nB=\\frac{1}{\\ln(\\rho_P\/\\rho_{\\Lambda})}\\simeq\n\\frac{1}{123\\ln(10)}\\simeq 3.53\\times 10^{-3}.\n\\label{lm15}\n\\end{equation}\nAs discussed in our previous papers \\cite{epjp,lettre,jcap}, $B$ is\nessentially the inverse of\nthe\nfamous number $123$ (see Appendix \\ref{sec_const}). Finally,\n\\begin{equation}\nA=B\\,\\rho_{\\Lambda}c^2=1.89\\times 10^{-9}\n\\, {\\rm g}\\, {\\rm m}^{-1}\\, {\\rm s}^{-2}. \n\\label{lm16}\n\\end{equation}\n\n\n\nFrom now on, we shall view $B$ given by Eq. (\\ref{lm15}) as the fundamental\nconstant of the theory. Therefore, everything should be expressed in terms of\n$B$ and the other fundamental constants of physics defining the Planck scales.\nFirst, we have\n\\begin{equation}\n\\frac{\\rho_{\\Lambda}}{\\rho_{P}}=\\frac{G\\hbar\\Lambda}{8\\pi\nc^5}=e^{-1\/B}=1.16\\times 10^{-123}.\n\\label{lm18}\n\\end{equation}\nThen,\n\\begin{equation}\n\\frac{A}{\\rho_{P}c^2}=Be^{-1\/B}=4.08\\times 10^{-126}.\n\\label{lm17}\n\\end{equation}\nThe logotropic equation of state (\\ref{lm5}) can be written as\n$P\/\\rho_Pc^2=Be^{-1\/B}\\ln(\\rho\/\\rho_P)$. Using Eq. (\\ref{lm10}) and\n$\\epsilon_{\\rm\nde,0}=\\Omega_{\\rm de,0}\\epsilon_0$, we get\n\\begin{equation}\n\\frac{\\epsilon_0}{\\rho_{P}c^2}=\\frac{1}{\\Omega_{\\rm de,0}}e^{-1\/B}=1.67\\times\n10^{-123}.\n\\label{lm19}\n\\end{equation}\nFinally, using Eq. (\\ref{lm1}),\n\\begin{equation}\nt_P H_0=\\left (\\frac{8\\pi}{3\\Omega_{\\rm de,0}}\\right\n)^{1\/2}e^{-1\/2B}=1.18\\times 10^{-61},\n\\label{lm20}\n\\end{equation}\nwhere $t_P=(\\hbar G\/c^5)^{1\/2}=5.391\\times 10^{-44}\\, {\\rm s}$ is the Planck\ntime. In the last two expressions,\nwe can either consider that $\\Omega_{\\rm de,0}$ is ``predicted'' by\nEq. (\\ref{lm11}) or take its measured value. To the order of\naccuracy that we\nconsider, this does not change the numerical values.\n\n\n\n\n\\section{Previous predictions of the logotropic model}\n\nThe interest of the logotropic model becomes apparent when it is\napplied to dark matter halos \\cite{epjp,lettre}. We assume that dark matter\nhalos are\ndescribed by the logotropic equation of state of Eq. (\\ref{lm5}) with\n$A=1.89\\times 10^{-9} \\, {\\rm g}\\, {\\rm m}^{-1}\\, {\\rm\ns}^{-2}$ (or $B=3.53\\times 10^{-3}$). At the\ngalactic scale, we can use Newtonian gravity. \n\n\n\\subsection{Surface density of dark matter halos}\n\n\n\nIt is an empirical evidence that the surface density of galaxies has\nthe same value \n\\begin{equation}\n\\Sigma_0^{\\rm obs}\\equiv \\rho_0 r_h\\simeq 295\\, {\\rm g\\, m^{-2}}\\simeq 141\\,\nM_{\\odot}\/{\\rm pc^2}\n\\label{lm21}\n\\end{equation}\neven\nif their sizes and masses vary by several orders of magnitude (up to $14$\norders of magnitude in luminosity) \\cite{donato}. Here $\\rho_0$ is the central\ndensity and $r_h$ is the halo radius at which the density has decreased by a\nfactor of $4$. The logotropic model predicts that the surface density of the\ndark matter halos is the\nsame for all the halos (because $A$ is a universal constant) and that it is\ngiven by \\cite{epjp,lettre}:\n\\begin{equation}\n\\Sigma_0^{\\rm th}=\\left (\\frac{A}{4\\pi G}\\right )^{1\/2}\\xi_h= \\left\n(\\frac{B}{32}\\right\n)^{1\/2}\\frac{\\xi_h}{\\pi}\\frac{c\\sqrt{\\Lambda}}{G},\n\\label{lm22}\n\\end{equation}\nwhere $\\xi_h=5.8458...$ is a pure number arising from the Lane-Emden equation\nof index $n=-1$ expressing the condition of hydrostatic equilibrium of\nlogotropic\nspheres.\\footnote{The logotropic spheres \\cite{epjp,lettre}, like the isothermal\nspheres \\cite{chandra}, have an infinite mass. This implies that the logotropic\nequation of state cannot\ndescribe dark matter halos at infinitely large distances. Nevertheless, it may\ndescribe the inner region of dark matter halos and this is sufficient to\ndetermine their surface density. The stability of bounded logotropic spheres has\nbeen studied in \\cite{logo} by analogy with the stability of bounded isothermal\nspheres and similar results have been obtained. In particular, bounded\nlogotropic\nspheres are stable\nprovided that the density contrast is not too large.} Numerically,\n\\begin{equation}\n\\Sigma_0^{\\rm th}= 278\\, {\\rm g\\,\nm^{-2}}\\simeq 133\\,\nM_{\\odot}\/{\\rm pc^2},\n\\label{lm23}\n\\end{equation}\nwhich is very close to the observational value\n(\\ref{lm21}). The fact that the surface density of dark matter halos is\ndetermined by the effective cosmological constant $\\Lambda$ (usually related to\nthe dark energy) tends to confirm that dark matter and dark energy are just two\nmanifestations of the {\\it same} dark fluid, as we have\nassumed in our model.\n\n{\\it Remark:} The dimensional term $c\\sqrt{\\Lambda}\/G$ in Eq. (\\ref{lm22}) can\nbe interpreted as representing the surface density of the Universe (see Appendix\n\\ref{sec_w}). We note that this term alone, $c\\sqrt{\\Lambda}\/G=14200\\, {\\rm g\\,\nm^{-2}}=6800 M_{\\odot}\/{\\rm pc}^2$, is too large to account precisely for the\nsurface density of dark matter halos so that the prefactor\n$(B\/32)^{1\/2}(\\xi_h\/\\pi)=0.01955$ is\nnecessary to reduce this number. It is interesting to remark\nthat the term $c\\sqrt{\\Lambda}\/G$ arises from classical general relativity while\nthe prefactor $\\propto B^{1\/2}$ has a quantum origin as discussed at the end of\nSec. \\ref{sec_B}. Actually, we will see that it is related to the fine-structure\nconstant $\\alpha$ [see Eq. (\\ref{lm30}) below].\n\n\n\\subsection{Mass-radius relation}\n\n \n\nThere are interesting consequences of the preceding result. For logotropic\nhalos, the mass of the halos calculated at the halo\nradius $r_h$ is given by \\cite{epjp,lettre}:\n\\begin{equation}\nM_h=1.49\\Sigma_0 r_h^2.\n\\label{lm24a}\n\\end{equation}\nThis\ndetermines the mass-radius relation of dark matter-halos. On the other hand, the\ncircular\nvelocity at the halo radius is $v_h^2=GM_h\/r_h=1.49\\Sigma_0\nG r_h$. Since the surface density of\nthe dark matter halos is constant, we obtain\n\\begin{equation}\n\\frac{M_h}{M_{\\odot}}=198 \\left (\\frac{r_h}{{\\rm pc}}\\right )^2,\\qquad\n\\left (\\frac{v_h}{{\\rm\nkm}\\, {\\rm s}^{-1}}\\right )^2=0.852\\, \\frac{r_h}{\\rm pc}.\n\\label{lm24}\n\\end{equation}\nThe scalings $M_h\\propto r_h^2$ and $v_h^2\\propto\nr_h$ (and also the prefactors) are consistent with the observations. \n\n\n\n\\subsection{The Tully-Fisher relation}\n\n\nCombining the previous equations, the logotropic model leads to the Tully-Fisher\n\\cite{tf} relation $v_h^4\\propto M_h$ or, more precisely, \n\\begin{equation}\n\\left (\\frac{M_b}{v_h^4}\\right )^{\\rm th}=\\frac{f_b}{1.49\\Sigma_0^{\\rm th}\nG^2}=46.4 M_{\\odot}{\\rm km}^{-4}{\\rm s}^4,\n\\label{lm25}\n\\end{equation}\nwhere\n$f_b=M_b\/M_h\\sim 0.17$ is the cosmic baryon fraction \\cite{mcgaugh}. The\npredicted value from Eq. (\\ref{lm25}) is\nclose to the observed one $\\left ({M_b}\/{v_h^4}\\right )^{\\rm obs}=47\\pm 6\nM_{\\odot}{\\rm\nkm}^{-4}{\\rm\ns}^4$ \\cite{mcgaugh}.\n\n\n\n{\\it Remark:} The Tully-Fisher relation is sometimes justified by\nthe MOND (Modification of Newtonian dynamics) theory \\cite{mond} which predicts\na relation of the form $v_h^4=Ga_0\nM_b$ between the asymptotic circular velocity and the baryon mass, where $a_0$\nis a critical acceleration. Our results imply\n$a_0^{\\rm th}=1.62\\times\n10^{-10}\\, {\\rm m}\\, {\\rm s}^{-2}$ which is\nclose to the value $a_0^{\\rm obs}=(1.3\\pm 0.3)\\times\n10^{-10}\\, {\\rm m}\\, {\\rm s}^{-2}$ obtained from the observations\n\\cite{mcgaugh}.\nCombining Eqs. (\\ref{lm24a}) and (\\ref{lm25}), we first get $a_0^{\\rm\nth}=(1.49\/f_b)\\Sigma_0^{\\rm th}G=GM_h\/(f_br_h^2)$ which shows that $a_0$ can be\ninterpreted as the surface gravity of the galaxies $G\\Sigma_0$ (which\ncorresponds to Newton's acceleration $GM_h\/r_h^2$) or as the surface density of\nthe Universe (see Appendix \\ref{sec_abh}). Then, using Eqs.\n(\\ref{lm14b}) and (\\ref{lm22}), we obtain $a_0^{\\rm\nth}=({1.49}\/{f_b})({B}\/{32})^{1\/2}({\\xi_h}\/{\\pi})c\\sqrt{\\Lambda}\\simeq H_0\nc\/4$ which\nexplains why $a_0$ is of the order of $H_0 c$. We emphasize,\nhowever, that we do\nnot use the MOND theory in our approach and that the logotropic model assumes\nthe existence of a dark fluid.\n\n\n\n\\subsection{The mass $M_{300}$}\n\n\n\n\n\nThe logotropic equation of state also explains the observation of Strigari {\\it\net al.} \\cite{strigari} that all the dwarf spheroidals (dSphs) of the Milky\nWay have the same total dark matter mass $M_{300}$ contained within a radius\n$r_u=300\\, {\\rm pc}$, namely $M_{300}^{\\rm obs}\\simeq 10^7\\, M_{\\odot}$\nThe logotropic model predicts the\nvalue \\cite{epjp,lettre}:\n\\begin{equation}\nM_{300}^{\\rm th}=\\frac{4\\pi \\Sigma_0^{\\rm th} r_u^2}{\\xi_h\\sqrt{2}}=1.82\\times\n10^{7}\\, M_{\\odot},\n\\label{lm26}\n\\end{equation}\nwhich is in very good agreement with the\nobservational value. \n\n\n\\section{A curious connection between atomic and\ncosmological scales}\n\\label{sec_curious}\n\n\n\n\\subsection{The surface density of the electron}\n\\label{sec_sde}\n\n\n\nThe classical radius of the\nelectron $r_e$ can be obtained qualitatively by writing that the electrostatic\nenergy of the electron, $e^2\/r_e$, is equal to its rest-mass energy $m_e c^2$.\nRecalling the value of the charge of the electron $e=4.80\\times 10^{-13}\\, {\\rm\ng^{1\/2}\\, m^{3\/2}\\, s^{-1}}$ and its mass $m_e=9.11\\times 10^{-28}\\, {\\rm g}$,\nwe obtain $r_e=e^2\/m_ec^2=2.82\\times 10^{-15}\\, {\\rm m}$. As a result, the\nsurface density of the electron is\\footnote{We note that the Thomson\ncross-section $\\sigma=(8\\pi\/3)(e^2\/m_e\nc^2)^2$ can be written as $\\sigma=(8\\pi\/3)r_e^2$ giving a physical meaning to\nthe classical electron radius $r_e$. We also note that $r_e$ can be written as \n$r_e=\\alpha\\hbar\/m_ec$ where $\\lambda_C=\\hbar\/m_e c$ is the Compton\nwavelength of the electron and $\\alpha$ is the fine-structure constant $\\alpha$\n[see Eq. (\\ref{const1})]. Similarly, \n$\\Sigma_e=(1\/\\alpha^2)m_e^3c^2\/\\hbar^2$.}\n\\begin{equation}\n\\Sigma_e=\\frac{m_e}{r_e^2}=\\frac{m_e^3c^4}{e^4}=115\\, {\\rm g\/m^2}= 54.9\\,\nM_{\\odot}\/{\\rm pc^2},\n\\label{lm27}\n\\end{equation}\nwhich is of the same order of magnitude as the surface density of dark matter\nhalos from Eq. (\\ref{lm21}). This\ncoincidence is amazing in view of the different scales (atomic\nversus cosmological) involved. More precisely, we find\n$\\Sigma_e=\\sigma\\Sigma_0^{\\rm th}$ with $\\sigma\\simeq 0.413$. Of\ncourse, the value of $\\sigma$ depends on the\nprecise manner used to define the surface density of the electron, or its\nradius, but the\nimportant point is that this number is of order unity. \n\n\n\n\\subsection{Relation between $\\alpha$ and $B$}\n\\label{sec_Ba}\n\n\n\nBy matching the two formulae (\\ref{lm22}) and (\\ref{lm27}),\nwriting $\\Sigma_e=\\sigma\\Sigma_0^{\\rm th}$, we get\n\\begin{equation}\n\\Lambda=\\frac{32\\pi^2}{B\\xi_h^2\\sigma^2}\n\\frac{m_e^6c^6G^2}{e^8}=\\frac{32\\pi^2}{B\\xi_h^2\\sigma^2\\alpha^4}\n\\frac{m_e^6c^2G^2}{\\hbar^4},\n\\label{lm28}\n\\end{equation}\nwhere we have introduced the fine-structure constant $\\alpha$ in the\nsecond equality (see Appendix \\ref{sec_const}).\nThis expression provides a curious relation between the cosmological constant,\nthe mass of the electron and its charge. This relation is similar to\nWeinberg's empirical relation (see Appendix\n\\ref{sec_w}) which can be written as [combining Eqs. (\\ref{lm14b}) and\n(\\ref{w4})]\n\\begin{equation}\n\\Lambda=192\\pi^2\\mu^2\\Omega_{\\rm de,0}\\frac{m_e^6c^6G^2}{e^8},\n\\label{w4b}\n\\end{equation}\nwhere $\\mu\\simeq 3.42$. Note that in our formula (\\ref{lm28}),\n$\\Lambda$ appears two times: on the left hand side and in $B$ (which depends\nlogarithmically on $\\Lambda$). This will have important consequences in the\nfollowing.\n\n\nB\\\"ohmer and Harko \\cite{bhLambda}, by a completely different approach, found a\nsimilar relation\\footnote{A closely related formula, involving the Hubble\nconstant instead of the cosmological constant, was first found by Stewart\n\\cite{stewart} in 1931 by trial and error.}\n\\begin{equation}\n\\Lambda=\\nu \\frac{\\hbar^2 G^2 m_e^6 c^8}{e^{12}}=\\frac{\\nu}{\\alpha^6} \\frac{G^2\nm_e^6 c^2}{\\hbar^{4}},\n\\label{lm29}\n\\end{equation}\nwhere $\\nu\\simeq 0.816$ is of order unity. Their result can be\nobtained as follows. They first introduce a minimum\nmass $m_{\\Lambda}\\sim\\hbar\\sqrt{\\Lambda}\/c^2$ interpreted as being the mass of\nthe elementary particle of dark energy, called the cosmon. Then, they define a\nradius $R$ by the relation $m_{\\Lambda}\\sim \\rho_{\\Lambda} R^3$ where\n$\\rho_{\\Lambda}= \\Lambda\/8\\pi G$ is the cosmological density considered as being\nthe lowest density in the Universe. Finally, they remark that $R$ has\ntypically the same value as the classical radius of the electron \n$r_e=e^2\/m_ec^2$. Matching $R$ and $r_e$ leads to the scaling of Eq.\n(\\ref{lm29}). We have then added a prefactor $\\nu$ and adjusted its\nvalue\nin order to exactly obtain the measured value of the cosmological constant\n\\cite{planck2016}.\nSince the approach of B\\\"ohmer and Harko \\cite{bhLambda} is essentially\nqualitative, and depends on the precise manner used to define the radius of\nthe\nelectron, their result can be at best valid up to a constant of order unity.\n\nWe would like now to compare the estimates from Eqs. (\\ref{lm28}) and\n(\\ref{lm29}). At that\nstage, we can have two points of view. If we consider that comparing the\nprefactors is meaningless because our approach can only provide ``rough'' orders\nof\nmagnitude, we conclude that Eqs. (\\ref{lm28}) and\n(\\ref{lm29}) are\nequivalent, and that they are also equivalent to Weinberg's empirical\nrelation (\\ref{w4}). Alternatively, if we take the prefactors seriously into\naccount (in particular the presence of $B$ which depends on $\\Lambda$) and match\nthe formulae (\\ref{lm28}) and\n(\\ref{lm29}), we find an interesting relation between the\nfine-structure constant\n$\\alpha$ and the logotropic constant $B$:\n\\begin{equation}\n\\alpha=\\left (\\frac{\\nu}{32}\\right\n)^{1\/2}\\frac{\\xi_h\\sigma}{\\pi}\\sqrt{B}\\simeq 0.123 \\sqrt{B}.\n\\label{lm30}\n\\end{equation}\nTherefore, the fine-structure constant (electron charge normalized by the\nPlanck charge) is determined by the logotropic constant $B$\n(cosmological density normalized by the Planck density) by a relation of the\nform $\\alpha\\propto B^{1\/2}$. This makes a connection between atomic scales and\ncosmological scales. This also suggests that the famous numbers\n$137$ and $123$\n(see Appendix \\ref{sec_const}) are related to each other, or may even represent\nthe same thing. From Eq. (\\ref{lm30}), we have\\footnote{We note that the\nprefactors in\nEqs. (\\ref{lm30}) and (\\ref{lm31}) appear to be close to $123\/1000$ and\n$123\/10$, where the number $123$ appears again (!). We do not know whether this\nis\nfortuitous or if this bears a deeper significance than is apparent at first\nsight.} \n\\begin{equation}\n137\\simeq 12.3 \\sqrt{123}.\n\\label{lm31}\n\\end{equation}\n\n\n{\\it Remark:} the logotropic constant $B$ is related to the effective\ncosmological constant $\\Lambda$ by [see Eq.\n(\\ref{lm18})]\n\\begin{equation}\nB=\\frac{1}{\\ln\\left (\\frac{8\\pi c^5}{G\\hbar\\Lambda}\\right )}.\n\\label{lm31b}\n\\end{equation}\nUsing Eqs. (\\ref{lm30}) and (\\ref{lm31b}), we can express the fine-structure\nconstant $\\alpha$ as a function of the effective cosmological\nconstant $\\Lambda$ or, using Eq. (\\ref{lm20}), as a function of the age of the\nUniverse $t_{\\Lambda}=1\/H_0$ as\n\\begin{equation}\n\\alpha=\\frac{0.123}{\\ln\\left (\\frac{8\\pi c^5}{G\\hbar\\Lambda}\\right\n)^{1\/2}}=\\frac{0.123}{\\sqrt{2}\\ln\\left \\lbrack \\left (\\frac{8\\pi}{3\\Omega_{\\rm\nde,0}}\\right )^{1\/2}\\frac{t_{\\Lambda}}{t_P}\\right\\rbrack^{1\/2}}.\n\\label{lm31c}\n\\end{equation}\nWe emphasize the scaling $1\/\\alpha\\propto (\\ln t_{\\Lambda})^{1\/2}$. It is\ninteresting to note that similar relations have been introduced in the past\nfrom pure numerology (see \\cite{kragh}, P. 428). These relations suggest\nthat the fundamental constants may change with time as argued by Dirac\n\\cite{dirac1,dirac2}. \n\n\n\\subsection{The mass and the charge of the electron\nin terms of $B$}\n\nUsing Eqs. (\\ref{lm9}), (\\ref{lm18}), (\\ref{lm28}) and (\\ref{lm30}), we find\nthat the mass and the charge of the electron\nare determined by the logotropic\nconstant $B$ according to\n\\begin{eqnarray}\n\\frac{m_e}{M_P}=\\left (\\frac{8\\pi}{\\nu}\\right )^{1\/6}\\left\n(\\frac{\\nu}{32}\\right\n)^{1\/2}\\frac{\\xi_h\\sigma}{\\pi}\\sqrt{B}e^{-1\/(6B)}\\nonumber\\\\\n=0.217\\sqrt{B}e^{\n-1\/(6B) } =4.18\\times 10^{-23},\n\\label{lm32}\n\\end{eqnarray}\n\\begin{equation}\n\\frac{e^2}{q_P^2}=\\left (\\frac{\\nu}{32}\\right\n)^{1\/2}\\frac{\\xi_h\\sigma}{\\pi}\\sqrt{B}=0.123\\sqrt{B}=7.29\\times 10^{-3},\n\\label{lm33}\n\\end{equation}\nwhere $M_P=(\\hbar c\/G)^{1\/2}=2.18\\times 10^{-5}\\, {\\rm g}$ is the\nPlanck mass and $q_P=(\\hbar c)^{1\/2}=5.62\\times 10^{-12}\\, {\\rm\ng^{1\/2}\\, m^{3\/2}\\, s^{-1}}$ is the Planck charge.\nThese relations suggest that the mass and the charge of the electron (atomic\nscales) are determined by the effective cosmological constant\n$\\Lambda$ or $B$ (cosmological scales). We emphasize the presence of\nthe exponential factor $e^{-1\/(6B)}$ in Eq. (\\ref{lm32}) explaining why the\nelectron mass is much smaller than the Planck mass while the electron charge is\ncomparable to the Planck charge. \n\n\\subsection{A prediction of $B$}\n\n\nIf we match Eqs. \n(\\ref{lm22}) and (\\ref{w3}), or equivalently Eqs. \n(\\ref{lm28}) and (\\ref{w4b}), we obtain\n\\begin{equation}\nB^{\\rm app}=\\frac{1}{6\\lambda^2\\xi_h^2\\Omega_{\\rm\nde,0}}.\n\\label{w5}\n\\end{equation}\nTaking $\\lambda^{\\rm app}=1$ (since we cannot predict its value) and\n$\\Omega_{\\rm de,0}^{\\rm th}=e\/(1+e)$ [see Eq. (\\ref{lm11})], we get $B^{\\rm\napp}=6.67\\times 10^{-3}$ instead of $B=3.53\\times\n10^{-3}$. We recall\nthat the value of $B$ was obtained in Sec. \\ref{sec_Bobs} from the\nobservations.\nOn the other hand, Eq. (\\ref{w5}) gives the correct order of magnitude of $B$\nwithout any reference to observations, up to a dimensionless constant\n$\\lambda\\simeq 1.41$ of order unity.\nConsidering that $B$ is predicted by Eq.\n(\\ref{w5}) implies that we can predict the values of $\\Lambda$, $H_0$, $\\alpha$,\n$m_e$ and $e$ without reference to observations, up to dimensionless constants\n$\\lambda\\simeq 1.41$, $\\nu\\simeq 0.816$ and $\\sigma\\simeq 0.413$\nof order unity. We note, however, that even if these dimensionless\nconstants ($\\lambda$, $\\nu$, $\\sigma$) are of order unity, their precise values\nare of importance since $B$ usually\nappears in exponentials like in Eqs. (\\ref{lm18}), (\\ref{lm20}) and\n(\\ref{lm32}). \n\n\n\n\\section{Conclusion}\n\nIn this paper, we have developed the logotropic model introduced in\n\\cite{epjp,lettre}. In this model, dark matter corresponds to the rest mass\nenergy of a dark fluid and dark energy corresponds to its internal energy. The\n$\\Lambda$CDM model may be interpreted as the semiclassical limit\n$\\hbar\\rightarrow 0$ of the logotropic model. We have first recalled that the \nlogotropic model is able to predict (without free parameter) the universal value\nof the surface density of dark matter halos $\\Sigma_0$, their mass-radius\nrelation\n$M_h-r_h$, the Tully-Fisher relation $M_b\\sim v_h^4$ and the value of the mass\n$M_{300}$ of dSphs. Then, we have argued that it also predicts the value of the\npresent fraction of dark energy $\\Omega_{\\rm de,0}$. This arises from a\nsort of ``strong cosmic coincidence'' but this could also correspond to a fixed\npoint of the model. Finally, we have observed that the surface density of the\ndark matter halos $\\Sigma_0$ is of the same order as the surface density of the\nUniverse\n $\\Sigma_\\Lambda$ and of the same order as the surface density of the electron \n$\\Sigma_e$. This makes an\nempirical connection between atomic physics and cosmology. From this connection,\nwe have obtained a relation between the fine-structure constant $\\alpha\\sim\n1\/137$ and the logotropic constant $B\\sim 1\/123$. We have also expressed the\nmass $m_e$ and the charge $-e$ of the electron as a function of $B$\n(or as a function of the effective\ncosmological constant $\\Lambda$). Finally, we have obtained a prediction of the\norder of magnitude of $B$ independent from the observations. In a sense, our\napproach which expresses the mass and the charge of the electron in terms of\nthe cosmological constant is a continuation of the program initiated by\nEddington \\cite{eddington} in his quest for a `{\\it Fundamental Theory}' of\nthe physical world in which the basic interaction strengths and elementary\nparticle masses would be prediced entirely combinatorically by simple counting\nprocesses \\cite{barrow}. In the Appendices, we try to\nrelate these interconnections to a form of holographic\nprinciple \\cite{bousso} (of course not known at the time of Eddington) stating\nthat the entropy of the electron, of dark matter\nhalos, and of the Universe scales like their area as in the case of black\nholes \\cite{bekenstein,hawking}.\n\n\nThis paper has demonstrated that physics is full of ``magic'' and mysterious\nrelations that are still not fully understood (one of them being the empirical\nWeinberg relation). Hopefully, a contribution of this\npaper is to reveal these ``mysteries'' and propose some tracks so as to induce\nfurther research towards\ntheir elucidation.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\subsection*{Introduction}%\n\\section{Introduction}\nThe isolation and control of the number of carriers in single and\nfew layer graphene flakes\\cite{Netal04t,Netal05t} has lead to a\nlarge research activity exploring all aspects of these\nmaterials\\cite{NGPNG09}. Among others, the application of graphene\nto spintronic\ndevices\\cite{HGNSB06t,CCF07,NG07,HJPJW07t,TTVJJvW08t,WPLCWSK08t,Petal09}\nand to spin qubits\\cite{TBLB07,FTL09,WYMK09} is being intensively\nstudied. The understanding of these devices requires a knowledge of\nthe electronic spin-orbit interaction. In principle, this\ninteraction turns single layer graphene into a topological\ninsulator\\cite{KM05}, which shows a bulk gap and edge states at all\nboundaries. The magnitude of the spin-orbit coupling in single layer\ngraphene has been studied\\cite{HGB06,HMING06t,YYQZF07,GKEAF09}. The\ncalculated couplings are small, typically below 0.1K. The observed\nspin relaxation\\cite{TTVJJvW08t,Jetal09} suggests the existence of\nstronger mechanisms which lead to the precession of the electron\nspins, like impurities or lattice\ndeformations\\cite{NG09,HGB09,EKGF09}.\n\nBilayer graphene is interesting because, among other properties, a\ngap can be induced by electrostatic means, leading to new ways for\nthe confinement of electrons\\cite{MF06}. The spin-orbit interactions\nwhich exist in single layer graphene modulate the gap of a graphene\nbilayer\\cite{GM09}. The unit cell of bilayer graphene contains four\ncarbon atoms, and there are more possible spin-orbit couplings than\nin single layer graphene.\n\nWe analyze in the following the intrinsic and extrinsic spin-orbit\ncouplings in bilayer graphene, using a tight binding model, and\ndescribing the relativistic effects responsible for the spin-orbit\ninteraction by a $\\vec{{\\bf L}}\\vec{{ \\bf S}}$ intraatomic coupling.\nWe use the similarities between the electronic bands of a graphene\nbilayer and the bands of three dimensional graphite with Bernal\nstacking to generalize the results to the latter.\n\n\\section{The model}.\nWe describe the electronic bands of a graphene bilayer using a tight\nbinding model, with four orbitals, the $2s$ and the three $2p$\norbitals, per carbon atom. We consider hoppings between nearest\nneighbors in the same plane, and nearest neighbors and next nearest\nneighbors between adjacent layers, see\\cite{CLM09}. The couplings\nbetween each pair of atoms is parametrized by four hoppings, $V_{ss}\n, V_{sp} , V_{pp \\pi}$ and $V_{pp \\sigma}$. The model includes also\ntwo intraatomic levels, $\\epsilon_s$ and $\\epsilon_p$, and the\nintraatomic spin-orbit coupling\n\\begin{align}\n{\\cal H}_{so} &\\equiv \\Delta_{so} \\sum_i \\vec{\\bf L}_i \\vec{\\bf S}_i\n\\end{align}\n The parameters used\nto describe the $\\pi$ bands of graphite\\cite{M57,SW58}, $\\gamma_0 ,\n\\gamma_1 , \\gamma_2 , \\gamma_3 , \\gamma_4 , \\gamma_5$ and $\\Delta$,\ncan be derived from this set of parameters. We neglect the\ndifference between different hoppings between atoms which are next\nnearest neighbors in adjacent layers, which are responsible for the\ndifference between the parameters $\\gamma_3$ and $\\gamma_4$. We also\nset the difference in onsite energies between the two inequivalent\natoms, $\\Delta$ to zero. The parameters $\\gamma_2$ and $\\gamma_5$\nare related to hoppings between next nearest neighbor layers, and\nthey do not play a role in the description of the bilayer. The total\nnumber of parameters is 15, although, without loss of generality, we\nset $\\epsilon_p = 0$. We do not consider hoppings and spin orbit\ninteractions which include $d$ levels, although they can contribute\nto the total magnitude of the spin-orbit\ncouplings\\cite{MY62,GKEAF09}. The effects mediated by $d$ orbitals\ndo not change the order of magnitude of the couplings in single\nlayer graphene, and their contribution to interlayer effects should\nbe small.\n\nThe main contribution to the effective spin-orbit at the Fermi level\ndue to the interlayer coupling is due to the hoppings between $p$\norbitals in next nearest neighbor atoms in different layers. This\ninteraction gives rise to the parameters $\\gamma_3$ and $\\gamma_4$\nin the parametrization of the bands in graphite. For simplicity, we\nwill neglect couplings between $s$ and $p$ orbitals in neighboring\nlayers. The non zero hoppings used in this work are listed in\nTable~\\ref{hoppings}.\n\n\\begin{table}\n\\begin{tabular}{||c|c||} \\hline \\hline\n$\\epsilon_s$ &-7.3 \\\\ \\hline $t^0_{ss}$ & 2.66 \\\\\n\\hline $t^0_{sp}$ & 4.98 \\\\ \\hline $t^0_{pp \\sigma}$ &2.66\n\\\\ \\hline $t^0_{pp \\pi}$ &-6.38 \\\\ \\hline $t^1_{pp \\pi}$ &0.4 \\\\ \\hline\n$t^2_{pp \\sigma}$ &0.4 \\\\ \\hline $t^2_{pp \\pi}$ &-0.4 \\\\ \\hline\n $\\Delta_{so}$ &0.02 \\\\ \\hline \\hline\n\\end{tabular}\n\\caption{Non zero tight binding parameters, in eV, used in the\nmodel. The hoppings are taken from\\cite{TL87,TS91}, and the\nspin-orbit coupling from\\cite{SCR00}. Superindices 0,1, and 2\ncorrespond to atoms in the same layer, nearest neighbors in\ndifferent layers, and next nearest neighbors in different layers.}\n\\label{hoppings}\n\\end{table}\n\nThe hamiltonian can be written as a $32 \\times 32$ matrix for each\nlattice wavevector. We define an effective hamiltonian acting on the\n$\\pi$, or $p_z$, orbitals, by projecting out the rest of the\norbitals:\n\\begin{align}\n{\\cal H}_{\\pi}^{eff} &\\equiv {\\cal H}_{\\pi} + {\\cal H}_{\\pi \\sigma}\n\\left( \\omega - {\\cal H}_{\\sigma \\sigma} \\right)^{-1} {\\cal\nH}_{\\sigma \\pi} \\label{heff}\n\\end{align}\nWe isolate the effect of the spin-orbit coupling by defining:\n\\begin{align}\n{\\cal H}_{\\pi}^{so} \\left( \\vec{\\bf k} \\right) &\\equiv {\\cal\nH}_{\\pi}^{eff} ( \\Delta_{so} ) - {\\cal H}_{\\pi}^{eff} ( \\Delta_{so}=\n0 )\n\\end{align}\nNote that ${\\cal H}_{\\pi}^{so}$ depends on the energy, $\\omega$.\n\nWe analyze ${\\cal H}_{\\pi}^{so}$ at the $K$ and $K'$ points. The two\nmatrices have a total of 16 entries, which can be labeled by\nspecifying the sublattice, layer, spin, and valley. We define\noperators which modify each of these degrees of freedom using the\nPauli matrices $\\hat{\\sigma} , \\hat{\\mu} , \\hat{s}$, and\n$\\hat{\\tau}$. The unit cell is described in\nFig.~\\ref{bilayer_lattice_so}.\n\\begin{figure}\n\\includegraphics[width=5cm]{lattice_bilayer_so.pdf}\n\\caption[fig]{(Color online). Unit cell of a graphene bilayer.\nLabels A and B define the two sublattices in each layer, while\nsubscripts 1 and 2 define the layers.} \\label{bilayer_lattice_so}\n\\end{figure}\n\nThe hamiltonian has inversion and time reversal symmetry, and it is\nalso invariant under rotations by $120^\\circ$. These symmetries are\ndefined by the operators:\n\\begin{align}\n{\\cal I} &\\equiv \\sigma_x \\mu_x \\tau_x \\nonumber \\\\\n{\\cal T} &\\equiv i s_y \\tau_x {\\cal K} \\nonumber \\\\\n{\\cal C}_{120^\\circ} &\\equiv \\left( - \\frac{1}{2} + i\n\\frac{\\sqrt{3}}{2} s_z \\right) \\times \\left( - \\frac{1}{2} - i\n\\frac{\\sqrt{3}}{2} \\tau_z \\mu_z \\right) \\times \\nonumber \\\\ &\\times\n\\left( - \\frac{1}{2} + i \\frac{\\sqrt{3}}{2} \\tau_z \\sigma_z \\right)\n\\end{align}\nwhere ${\\cal K}$ is complex conjugation.\n\nThe possible spin dependent terms which respect these symmetries\nwere listed in\\cite{DD65}, in connection with the equivalent problem\nof three dimensional Bernal graphite (see below). In the notation\ndescribed above, they can be written as\n\\begin{align}\n{\\cal H}_{\\pi}^{so} &= \\lambda_1 \\sigma_z \\tau_z s_z + \\lambda_2\n\\mu_z \\tau_z s_z + \\lambda_3 \\mu_z \\left( \\sigma_y s_x - \\tau_z\n\\sigma_x s_y \\right) + \\nonumber \\\\ &+ \\lambda_4 \\sigma_z \\left(\n\\mu_y s_x + \\tau_z \\mu_x s_y \\right) \\label{hamilso}\n\\end{align}\nThe first term describes the intrinsic spin-orbit coupling in single\nlayer graphene. The other three, which involve the matrices $\\mu_i$,\nare specific to bilayer graphene. The term proportional to\n$\\lambda_3$ can be viewed as a Rashba coupling with opposite signs\nin the two layers.\n\n\\section{Results}.\n\\subsection{Bilayer graphene}.\n\\begin{figure}\n\\includegraphics[width=8cm]{couplings_bilayer_so_E.pdf}\n\\caption[fig]{(Color online). Dependence on energy of the spin-orbit\ncouplings, as defined in eq.~\\ref{hamilso}.}\n\\label{couplings_bilayer_so_E}\n\\end{figure}\n\nThe energy dependence of the four couplings in eq.~\\ref{hamilso} is\nshown in Fig.~\\ref{couplings_bilayer_so_E}. The values of the\ncouplings scale linearly with $\\Delta_{so}$. This dependence can be\nunderstood by treating the next nearest neighbor interlayer coupling\nand the intratomic spin-orbit coupling as a perturbation. The\nspin-orbit coupling splits the spin up and spin down states of the\n$\\sigma$ bands in the two layers. The interlayer couplings couple\nthe $\\pi$ band in one layer to the $\\sigma$ band in the other layer.\nTheir value is of order $\\gamma_3$. The $\\pi$ states are shifted\nby:\n\\begin{align}\n\\delta \\epsilon_{\\pi \\pm} &\\sim - \\frac{\\gamma_3^2}{\\left|\n\\epsilon_{\\sigma \\pm}\\right|} \\propto \\mp \\Delta_{so} \\left(\n\\frac{\\gamma_3}{ \\epsilon_{\\sigma}^0 } \\right)^2\n\\label{spin_hopping}\n\\end{align}\nwhere $\\epsilon_\\sigma^0$ is an average value of a level in the\n$\\sigma$ band.\n\n The model gives for the only\nintrinsic spin-orbit coupling in single layer graphene the value\n\\begin{align}\n\\left| \\lambda_1^{SLG} \\right| &= 0.0065 {\\rm meV} \\label{lambdaSLG}\n\\end{align}\nThis coupling depends quadratically on $\\Delta_{so}$, $\\delta\n\\epsilon_{\\pi \\pm} \\sim \\pm \\Delta_{so}^2 \/\n\\epsilon_\\sigma^0$\\cite{HGB06}.\n\nThe band dispersion of bilayer graphene at low energies, in the\nabsence of spin-orbit couplings is given by four Dirac cones,\nbecause of trigonal warping effects associated with\n$\\gamma_3$\\cite{MF06}. Hence, we must to consider the couplings for\nwavevectors $\\vec{\\bf k}$ slightly away from the $K$ and $K'$\npoints. We have checked that the dependence of the couplings\n$\\lambda_i$ on momentum, in the range where trigonal warping is\nrelevant, is comparable to the changes with energy shown in\nFig.~\\ref{couplings_bilayer_so_E}.\n\n\\begin{figure}\n\\includegraphics[width=8cm]{couplings_bilayer_so_Egap.pdf}\n\\caption[fig]{(Color online). Dependence on interlayer gap, $E_g$,\nof the spin-orbit couplings, as defined in eq.~\\ref{hamilso}.}\n\\label{couplings_bilayer_so_Egap}\n\\end{figure}\n\nA gap, $E_g$, between the two layers breaks inversion symmetry, and\ncan lead to new couplings. The calculations show no new coupling\ngreater than $10^{-6}$meV for gaps in the range $-0.1 {\\rm eV} \\le\nE_g \\le 0.1 {\\rm eV}$. The dependence of the couplings on the value\nof the gap is shown in Fig.~\\ref{couplings_bilayer_so_Egap}. This\ncalculation considers only the effect in the shift of the\nelectrostatic potential between the two layers. The existence also\nof an electric field will mix the $p_z$ and $s$ orbitals within each\natom, leading to a Rashba term similar to the one induced in single\nlayer graphene\\cite{HGB06,HMING06t}.\n\n\n\nThe effect of $\\lambda_1$ is to open a gap of opposite sign in the\ntwo valleys, for each value of $s_z$. The system will become a\ntopological insulator\\cite{H88,KM05}. The number of edge states is\ntwo, that is, even. The spin Hall conductivity is equal to two\nquantum units of conductance. A perturbation which preserves time\nreversal invariance can hybridize the edge modes and open a gap.\nSuch perturbation should be of the form $\\tau_x s_y$.\n\nThe terms with $\\lambda_3$ and $\\lambda_4$ describe spin flip\nhoppings which involve a site coupled to the other layer by the\nparameter $\\gamma_1$. The amplitude of the wavefunctions at these\nsites is suppressed at low energies\\cite{MF06}. The shifts induced\nby $\\lambda_3$ and $\\lambda_4$ in the low energy electronic levels\nwill be of order $\\lambda_3^2 \/ \\gamma_1 , \\lambda_4^2 \/ \\gamma_1$.\n\n\\begin{figure}\n\\includegraphics[width=8cm]{couplings_bilayer_so_kz.pdf}\n\\caption[fig]{(Color online). Dependence on momentum perpendicular\nto the layers in Bernal graphite of the spin-orbit couplings, as\ndefined in eq.~\\ref{hamilso}.} \\label{couplings_bilayer_so_kz}\n\\end{figure}\n\\subsection{Bulk graphite}.\nThe hamiltonian of bulk graphite with Bernal stacking can be reduced\nto a set of bilayer hamiltonians with interlayer hoppings which\ndepend on the momentum along the direction perpendicular to the\nlayers, $k_z$. We neglect in the following the (small) hoppings\nwhich describe hoppings between next nearest neighbor layers,\n$\\gamma_2$ and $\\gamma_5$, and the energy shift $\\Delta$ between\natoms in different sublattices. At the $K$ and $K'$ points of the\nthree dimensional Brillouin Zone ($2 k_z c = 0$, where $c$ is the\ninterlayer distance) the hamiltonian is that of a single bilayer\nwhere the value of all interlayer hoppings is doubled. At the $H$\nand $H'$ points, where $2 k_z c = \\pi$, the hamiltonian reduces to\ntwo decoupled layers, and in the intermediate cases the interlayer\ncouplings are multiplied by $| 2 \\cos (k_z c) |$. Carrying out the\ncalculations described in the previous section, $k_z$ dependent\neffective couplings, $\\lambda_i ( k_z )$, can be defined. These\ncouplings are shown in Fig.~\\ref{couplings_bilayer_so_kz}. The\nresults for bilayer graphene correspond to $k_z c = 2 \\pi \/ 3 , 4\n\\pi \/ 3$. The layers are decoupled for $k_z c = \\pi$. In this case,\nthe only coupling is $\\lambda_1$, which gives the coupling for a\nsingle layer, given in eq.~\\ref{lambdaSLG}.\n\nThe significant dispersion as function of momentum parallel to the\nlayers shown in Fig.~\\ref{couplings_bilayer_so_kz} implies the\nexistence of spin dependent hoppings between layers in different\nunit cells. This is consistent with the analysis which showed that\nthe spin-orbit coupling in a bilayer has a contribution from\ninterlayer hopping, see eq.~\\ref{spin_hopping}.\n\nThe spin-orbit couplings can be larger in bulk graphite than in a\ngraphene bilayer. The bands in Bernal graphite do not have\nelectron-hole symmetry. The shift in the Fermi energy with respect\nto the Dirac energy is about $E_F \\approx 20 {\\rm meV} \\gg \\lambda_1\n, \\lambda_3$\\cite{DM64}. Hence, the spin-orbit coupling is not\nstrong enough to open a gap throughout the entire Fermi surface, and\ngraphite will not become an insulator.\n\n\n\\begin{figure}\n\\includegraphics[width=8cm]{couplings_bilayer_so_ky_ortho.pdf}\n\\caption[fig]{(Color online). Dependence on wavevector, $2 k_y $, of\nthe spin-orbit couplings for orthorhombic graphite, as defined in\neq.~\\ref{couplings_ortho}. The point $k_x = 0 , k_y a \\sqrt{3} = 4\n\\pi \/3$ corresponds to the $K$ point ($a$ is the distance between\ncarbon atoms in the plane).} \\label{couplings_bilayer_so_ky_ortho}\n\\end{figure}\n\nA similar analysis applies to orthorhombic graphite, which is\ncharacterized by the stacking sequence $ABCABC \\cdots$\\cite{M69}.\nThe electronic structure of this allotrope at low energies differs\nmarkedly from Bernal graphite\\cite{GNP06,AG08}, and it can be a\nmodel for stacking defects\\cite{BCP88,GNP06,AG08}. If hoppings\nbeyond nearest neighbor layers are neglected, the hamiltonian can be\nreduced to an effective one layer hamiltonian where all sites are\nequivalent. The effective hamiltonian which describes the $K$ and\n$K'$ valleys contains eight entries, which can be described using\nthe matrices $\\sigma_i , s_i$, and $\\tau_i$. Orthorhombic graphene\nis not invariant under inversion, and a Rashba like spin-orbit\ncoupling is allowed. The spin-orbit coupling takes the form:\n\\begin{align}\n{\\cal H}_{ortho}^{so} &\\equiv \\lambda_1^{ortho} \\sigma_z s_z \\tau_z\n+ \\lambda_2^{ortho} \\left( \\sigma_y s_x - \\tau_z \\sigma_x s_y\n\\right) \\label{ortho}\n\\end{align}\nAs in the case of Bernal stacking, the couplings have a significant\ndependence on the momentum perpendicular to the layers, $k_z$, and\ninterlayer hopping terms are induced. For $\\omega = 0, \\vec{\\bf k} =\n0$ and $k_z = 0$, we find:\n\\begin{align}\n\\lambda_1^{ortho} &= 0.134 {\\rm meV} \\nonumber \\\\\n\\lambda_2^{ortho} &= 0.275 {\\rm meV} \\label{couplings_ortho}\n\\end{align}\nIn orthorhombic graphite the Fermi level is away from the $K$ and\n$K'$ points, in the vicinity of a circle defined by $| \\vec{\\bf k} |\n= \\gamma_1 \/ v_F$\\cite{GNP06,AG08}. The variation of the couplings\nas function of wavevector is shown in\nFig.~\\ref{couplings_bilayer_so_ky_ortho}.\n\n\n\n\\section{Conclusions}\nWe have studied the intrinsic spin-orbit interactions in a graphene\nbilayer and in graphite. We assume that the origin of the couplings\nis the intraatomic $\\vec{\\bf L} \\vec{\\bf S}$ interaction, and we use\na tight binding model which includes the $2s$ and $2p$ atomic\norbitals.\n\nThe intrinsic spin-orbit couplings in a graphene bilayer and in\ngraphite are about one order of magnitude larger than in single\nlayer graphene, due to mixing between the $\\pi$ and $\\sigma$ bands\nby interlayer hoppings. Still, these couplings are typically of\norder $0.01 - 0.1$meV, that is, $0.1 - 1$K.\n\nBilayer graphene becomes an insulator with an even number of edge\nstates. These states can be mixed by perturbations which do not\nbreak time reversal symmetry. These perturbations can only arise\nfrom local impurities with strong spin-orbit coupling, as a spin\nflip process and intervalley scattering are required.\n\nThe interplay of spin-orbit coupling and interlayer hopping leads to\nspin dependent hopping terms. The spin-orbit interactions are\nlargest in orthorhombic graphite, which does not have inversion\nsymmetry.\n\n\\section{Acknowledgements}\nFunding from MICINN (Spain), through grants FIS2008-00124 and\nCONSOLIDER CSD2007-00010 is gratefully acknowledged.\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\chapter{INTRODUCTION}\n\nAutonomous vehicles are a rapidly expanding market and research field. In recent years, the automotive industry has increased its projects and importance in this area in order to reduce the number of deaths caused by crashes \\cite{1_importance}. Furthermore, according to \\cite{1_economic}.the autonomous vehicle market is expected to grow 36.9\\% between 2017 and 2027 and reach \\$126.8 billion dollar market value. And the fact that it is such a big economy enables it to renew itself as something. In this direction, studies are carried out for smartening and autonomy in the design of automobiles. According to a World Health Organization research, road traffic collisions are the seventh leading cause of mortality across all age categories, accounting for about 1.35 million deaths in 2016, with cyclists, motorcyclists, and pedestrians contributing for more than half of the deaths \\cite{1_who}. Considering the complex traffic situations and the increase in traffic accidents due to carelessness, misbehavior, fatigue or distraction, the improvements seen in different levels of autonomous vehicles are motivated to reduce injuries and deaths caused by complex traffic scenarios and errors. Injuries and deaths caused by traffic accidents can be reduced with more accurate Advanced Driver Assistance System(ADAS) applications. \n\n\n\nIn light of all of this knowledge, in order to be able to prevent crashes or pass through an obstacle, path planning algorithms are being developed for decades. Path planning algorithms are basically used to enable autonomous vehicles to move in environments with obstacles. Also, as mobile robots have recently started to work in dynamic environments where people are also present, path planning algorithms have gained more importance in order for autonomous vehicles to work safely. To achieve autonomous driving skills, different path planning algorithms use different approaches. While algorithms like RRT(Rapidly-Exploring Random Tree), RRT*(Rapidly-exploring random tree (star)) and PRM(Probabilistic Roadmap) use probabilistic methods for path planning, algorithms like APF use geometric approaches.\n\n\n\n\\clearpage\n\n\nWhen it comes to real-world applications, such as autonomous vehicle deployment, testing the boundaries of safety and performance on real cars is expensive, inaccessible, and dangerous. Commercially available mobile systems such as Jackal UGV(Unmanned Ground Vehicle) \\cite{1_jackal} and TurtleBot2 \\cite{1_turtle} have been developed to resolve these concerns. Several small-scale robotic platforms have been built in recent years to further research, particularly in the area of autonomous driving. Many of these platforms are built on a small-scale racecar with a mechanical framework to support the electronic components, generally 1\/10 size of an actual vehicle. In 2014, Mike Boulet, Owen Guldner, Michael Lin, and Sertac Karaman developed RACECAR (Rapid Autonomous Complex-Environment Competing Ackermann steering Robot) that was the first mobile robot with a strong graphics processing unit(GPU) \\cite{1_sertac}. By offering realistic car dynamics with Ackermann steering and drive trains, the RACECAR platform provides as a robust robotic platform for research and education. It is built on completely open-source and standardized systems that use ROS and its related libraries.\n\n\n\\section{Purpose of The Thesis}\n\nThis project mainly focuses on implementing and testing different path planning algorithms on MIT RACECAR platform. These algorithms are expected to avoid obstacles while following the road on a high curvature road. It is expected to determine the path planning algorithm that implements this scenario most successfully and to create a starting point for the solution of complex problems such as overtaking and escape maneuver using this algorithm in the period after the thesis. A lane finding algorithm based on image processing had to be developed at the same time in order to implement the scenario. Within this project, necessary tools will be developed and, route planning algorithms will be evaluated by creating a scenario environment. \n\n\n\\clearpage\n\n\n\\section{Literature Review}\n\n\nSince path planning is a subject that has been studied for a long time, many algorithms related to path planning have been developed. DWA is one of the most popular algorithms. Fox proposed DWA algorithm in 1997 but, it is still frequently used. Also, algorithms that relies on geometric relations like APF are very successful with a low computational cost \\cite{1_apf}. Also, algorithms based on optimization of dynamic parameters like TEB are proposed by R\u00f6smann and it is shown that it can navigate complex environments \\cite{3_teb_2}. Several vision-based lane detection algorithms have been presented in the literature to reliably detect lanes. These approaches may be divided into two types: feature-based and model-based. Detecting the evident properties of lanes that distinguishes them from the rest of the objects on the lane image is the basis of the feature-based technique. The task of feature detection would be split into two parts: edge detection and line detection. To identify lane edges and model lane borders, edge-based techniques have been developed. The accuracy and computing needs of more sophisticated implementations of these models rise, while the resilience to noise decreases. For feature extraction, the Canny edge detection method is presented, which offers an exact match to lane lines and is adaptable to a complex road environment \\cite{1_canny}. Improvements to Canny edge detection can successfully deal with numerous noises in the road environment, according to \\cite{1_canny2}. Furthermore, defining the targeted region of the picture where lane borders are located, called to as region of interest (ROI), enhances performance efficiency \\cite{1_roi}. To ease the identification procedure, the region of interest would be split into left and right sub-regions based on the width of the lane.\n\n\n\\chapter{Platform Overview}\n\n\\begin{figure}[h!] \n \\centerline{\\includegraphics[width=.6\\linewidth]{2.racecar_platform\/figures\/racecar.png}}\n \\vspace{5mm}\n \\caption{MIT Racecar Platform}\n \\label{fig:racecar}\n\\end{figure}\n\nRACECAR is a full stack robotics system that provides high computing power with reliable mechanical structure. RACECAR platform serves as a strong robotic platform for research and education by providing realistic vehicle dynamics with Ackermann steering, and drive trains. It is designed based on fully open-source and standardized systems that take advantage of ROS and its associated libraries \\cite{2_racecar}. MIT RACECAR has sensors that can be listed as:\n\n\\begin{itemize}\n \\item StereoLabs ZED Stereo Camera\n \\item RPLidar A2 Lidar\n \\item Sparkfun 9DoF Razor IMU\n\\end{itemize}\n\n\n\nBecause of its numerous sensors and well documented software, MIT RACECAR was chosen to be used in this project as the robot that trajectory generation algorithms will be tested on. \n\n\n\\clearpage\n\n\n\\section{Hardware}\n\n\\subsection{Chasis}\n\nChassis of MIT Racecar platform is Slash 4\u00d74 Platinum Truck model from Traxxas. These chassis is giving providing enough performance parameters and has enough space for installation of other equipments. The chassis is able to drive 40 mph with its default Velineon 3500 Brushless Motor, but VESC software the maximum speed is limited by 5 mph for safety concerns. Power required by BLDC is provided by a 2S 4200 mah LiPo battery \\cite{2_traxxas}. \n\n\n\n\\subsection{NVIDIA Jetson TX2}\nNVIDIA Jetson TX2 is high power graphical processing unit which is widely used in robotics applications. It has enough processing power for trajectory planning and other tasks. Jetson TX2 has an ARMCortexA57 CPU(Central Process Unit) and 256 CUDA enabled GPU with an 8 GB RAM(Random Access Memory) \\cite{2_jetson}.\n\n\n\\begin{figure}[h] \n \\centerline{\\includegraphics[width=.5\\linewidth]{2.racecar_platform\/figures\/jetson.png}}\n \\caption{NVIDIA Jetson TX2 \\cite{2_jetson}}\n \\label{fig:racecar}\n\\end{figure}\n\n\n\\subsection{StereoLabs ZED Stereo Camera}\nStereoLabs ZED Stereo Camera is one of the enviromental awareness sensors MIT Racecar. ZED camera comes with dual 4 MP Camera which that provides 110\u00b0 FOV(Field of View). ZED camera provides real-time pointcloud data in addition to getting 1080p HD video at 30 FPS or WVGA at 100 FPS(Frame Per Second) thanks to its SDK(Software Development Kit) \\cite{2_zed}.\n\n\\clearpage\n\n\\begin{figure}[h!] \n \\centerline{\\includegraphics[height=3cm]{2.racecar_platform\/figures\/zed.png}}\n \\caption{StereoLabs ZED Stereo Camera \\cite{2_zed}}\n \\label{fig:racecar}\n\\end{figure}\n\n\n\n\\subsection{RPLidar A2 Lidar}\n\n\nRPLidar A2 Lidar is a low-cost 2d lidar that developed by SLAMTEC will give us the obstacle distance information which is necessary for trajecrory generation. It can measure distances with a 360 degree field of view with maximum 16 meters range \\cite{2_lidar}.\n\n\n\\begin{figure}[h] \n \\centerline{\\includegraphics[width=.7\\linewidth]{2.racecar_platform\/figures\/rplidar.png}}\n \\vspace{5mm}\n \\caption{RPLidar A2 Lidar \\cite{2_lidar}}\n \\label{fig:racecar}\n\\end{figure}\n\n\n\\subsection{Sparkfun 9DoF Razor IMU}\n\nSparkfun 9DoF Razor IMU is reprogrammable IMU(Inertial Measurement Unit) with an MPU-9250 9DoF (9 Degrees of Freedom) sensor. Sparkfun IMU is a easy-to-use IMU with its various connection types like UART, I2C and USB. In MIT RACECAR platform it is fully entegrated by a USB connection but in this project it is not actively used \\cite{2_imu}.\n\n\n\\subsection{VESC Electronic Speed Controller}\n\nBLDC(Brushless DC Motor) and steering servo of the MIT Racecar is controlled by Vedder Electronic Speed Controller (VESC). Unlike conventional ESCs, VESC also provides useful data over USB connection like current and voltage data for each motor phase and wheel odometer data. VESC is capable of controlling speed and steering angle of MIT Racecar succeffully. These low level controllers of VESC allow researchers to be more focused on developing solutions to higher level problems.\n\n\n\n\\section{Software}\n\nThe MIT RACECAR software relies on JetPack and ROS as two main components. JetPack is a special software for NVIDIA Jetson computers that specializes Ubuntu for them. Along with specialized OS for Jetson it also provides libraries, APIs, samples and developer tools for AI applications and GPU computing \\cite{2_jetpack}. \n\n\nAs the other main component of The MIT RACECAR software, RACECAR ROS package provides robotic capabilities on ROS as subpackages and it consists of sub-packages like; \\textit{\\textbf{ackermann\\_cmd\\_mux, joy\\_node rplidar\\_ros, sparkfun-9dof-razor-imu, zed\\_ros\\_wrapper.}} These packages are required for collecting sensor data from vehicle, applying commanded control signals, making necessary transformations between different frames. As an addition to these packages, for data collection and logging \\textit{\\textbf{rosbag}} package is used. The rosbag package allows us to save the sensor data as time-series and then play it back in real time. \\textit{\\textbf{Rosbag}} package also allows us to measure performance of developed algorithm. Also, for visualization purposes, another ROS package \\textit{\\textbf{rqt}} is used. \n\n\n\\subsection{Robot Operating System (ROS)}\n\nROS is an open-source framework that mainly handles communication between nodes and creates a baseline for researchers. Since it also has useful packages like transformation calculations etc., it is very helpful for beginners in robotics programming and accelerate prototyping processes for researchers.\n\n\n\\begin{figure}[h!] \n \\centerline{\\includegraphics[width=.7\\linewidth]{2.racecar_platform\/figures\/ros-master-node.jpg}}\n \\vspace{5mm}\n \\caption{ROS Topics Communication System}\n \\label{fig:ros_communication}\n\\end{figure}\n\n\\clearpage\n\nCommunication architecture of ROS is communication between nodes with a publish-subscribe messaging model. This communication is only possible with a \\textbf{\\textit{master}} which serves as an XML-RPC based server. \\textbf{\\textit{Master}} makes the communication between nodes with an address which is named \\textbf{\\textit{topic}}. \\textbf{\\textit{Topics}} are special messaging addresses with a specific data type and name. \n\nThe ROS nodes of MIT RACECAR can be seen in Table \\ref{ros_node_t}.\n\n\n\\begin{table}[]\n\\centering\n\\begin{tabular}{@{}\n>{\\columncolor[HTML]{EFEFEF}}c \n>{\\columncolor[HTML]{EFEFEF}}c \n>{\\columncolor[HTML]{EFEFEF}}c @{}}\n\\toprule\n\\textbf{NODE NAME} &\n \\textbf{PACKAGE} &\n \\textbf{DESCRIPTION} \\\\ \\midrule\n\\begin{tabular}[c]{@{}c@{}}ackermann\\_cmd\\\\ \\_mux\\end{tabular} &\n \\begin{tabular}[c]{@{}c@{}}ackermann\\_cmd\\\\ \\_mux\\end{tabular} &\n \\begin{tabular}[c]{@{}c@{}}Ackermann velocity command inputs are \\\\ multiplexed in this node. It controls \\\\ incoming ackermann command topics but,\\\\ based on priority, allows \\\\ one topic command at a time.\\end{tabular} \\\\ \\midrule\njoy\\_node &\n joy &\n \\begin{tabular}[c]{@{}c@{}}This node is used for generic joystick to ROS. \\\\ It publishes joy message that \\\\ includes joystick all possible states \\\\ of buttons and axes.\\end{tabular} \\\\ \\midrule\nrplidarNode &\n rplidar\\_ros &\n \\begin{tabular}[c]{@{}c@{}}Driver for RPLIDAR sensor that converts messages \\\\ that comes from lidar sensor to laser scan messages \\\\ and publishes to a topic.\\end{tabular} \\\\ \\midrule\n\\begin{tabular}[c]{@{}c@{}}zed\\_camera\\\\ \\_node\\end{tabular} &\n zed-ros-wrapper &\n \\begin{tabular}[c]{@{}c@{}}Driver for ZED camera that outputs pointcloud, \\\\ camera left and right images and pose \\\\ information of robot.\\end{tabular} \\\\ \\midrule\nrosbag\\_node &\n rosbag &\n \\begin{tabular}[c]{@{}c@{}}It is a tool for recording the messages published on \\\\ topics to bag files which are specially formatted files \\\\ that stores timestamped ROS messages.\\\\ It also used for replaying the recorded topics.\\end{tabular} \\\\ \\midrule\nrqt\\_node &\n rqt &\n \\begin{tabular}[c]{@{}c@{}}This node is used for visualizatioon purpose. \\\\ It provides tools to easily plot data in real-time,\\\\ displays rostopic messages etc.\\end{tabular} \\\\ \\bottomrule\n\\end{tabular}\n\\caption{The ROS nodes of MIT RACECAR software package}\n\\label{ros_node_t}\n\\end{table}\n\n\\clearpage\n\n\n\\section{Vehicle Kinematic Model}\n\nAckermann steered vehicles like MIT RACECAR are commonly expressed as bicycle kinematic model. Bicycle kinematic model for Ackermann steered vehicles assumes that front wheels and rear wheels are combined amongst themselves and end up with a two-wheel bicycle \\cite{2_bicycle}. In addition to this, bicycle kinematic model also assumes that the vehicle only moves on XY plane and thanks to these assumptions, bicycle kinematic model is a simple geometric relationship between the steering angle and the curvature that the rear axle of the vehicle will follow.\n\n\n\\begin{figure}[h] \n \\centerline{\\includegraphics[height=.6\\linewidth]{2.racecar_platform\/figures\/bicycle.png}}\n \\caption{Simple Bicycle Model \\cite{2_bicycle}}\n \\label{fig:bicycle}\n\\end{figure}\n\n\\begin{equation}\n tan(\\gamma) = \\frac{L}{R}\n\\label{eq_bicycle}\n\\end{equation}\n\nThe geometric relationship can be written as equation \\ref{eq_bicycle} where $\\gamma$ is steering angle, $L$ is the wheelbase and $R$ is the turning radius of the vehicle. It should be noted that this geometric relationship gives acceptable results for only low speed and moderate steering angles.\n\n\\clearpage\n\n\n\n\\chapter{PATH PLANNING}\n\nPath planning algorithms are used to enable mobile robots to perform their most basic tasks, which are their ability to move. These algorithms mainly consists of two parts which is path planning and trajectory planning. While path planning is responsible for the higher level motion of the robot, trajectory planning algorithms calculates the required actuator behavior for following via-points generated by path planning \\cite{3_global_local}. This project focuses on trajectory planning algorithms. In ROS methodology, path planning algorithms corresponds to global planner and trajectory planner corresponds to local planner. \n\n\n\\begin{figure}[h!] \n \\centering\n \\centerline{\\includegraphics[width=\\linewidth, height=14cm]{3.trajectory_planning\/figures\/planners.png}}\n \\caption{An example view of local and global planners \\cite{3_global_local_fig}.}\n \\label{fig:costmap}\n\\end{figure}\n\n\\clearpage\n\n\nIn this project, the performance of different trajectory planning algorithms like DWA, TEB and APF, will be compared by their performance. In the realization phase of the project, the algorithms will be tested on the Racecar Platform in a curvy road. The algorithms are implemented in the ROS environment. Thanks to the useful features of ROS, implementation and testing of the algorithms could be done easily. \n\n\n\\section{Main Elements of Path Planning}\n\nPath planning processes may become complex processes. In order to make path planning more tidy and less complex, some auxiliary ROS packages will be used. Before diving into path planning algorithms, these auxiliary packages will be examined. \n\n\n\\subsection{Global Planner}\n\nGlobal planners are responsible for creating a collision free path for the robot. Global planners generate this path around the obstacles from a geometric approach, and they don't take into account vehicles dynamics. Unlike local planners, generally they plan for a larger map. A*, Dijkstra's Algorithm, RRT, $RRT^*$, and PRM can be counted as examples of global planning algorithms. The output of Global planners is a roadmap or waypoints to the target point. These roadmap provides local planner the points to follow. \n\n\nIn this project, \\textbf{\\textit{base\\_global\\_planner}} package from ROS navigation stack will be used as global planner. This package provides a good baseline that can be configured to use different algorithms for global planning. In this project, since global planning is not one of the main tasks of this project, the package is configured for the Dijkstra's algorithm for calculating the shortest path that has the lowest cost without including robot kinematic parameters. Dijkstra's algorithm is mainly chosen for its simplicity and being computationally cheap. Global planners are basic algorithms for choosing the path that has minimum costs. Though, their performance heavily depends on configuration of global costmap. Since there is no mapping in this project, we are using our global planner with respect to the base\\_link frame. \n\n\n\\clearpage\n\n\\subsection{Local Planner}\n\nLocal planner is the planner that calculates the short term plan and executes the plan. Unlike global planner, local planner includes robot kinematic parameters and calculates the plan according to these parameters. This extra calculation comes with a computational cost. In order to decrease the computational cost of local planners, boundaries of the local planner generally being selected small and local planner aims the furthest point that is on the global plan that is inside of local planner boundaries, not the real target point. \n\nGlobal planners produce waypoints or roadmaps as output and local planners get the output of global planners as inputs and calculates the required control signal to follow these waypoints. This control signal is generally a ROS message type named \\textbf{\\textit{\"\/geometry\\_msgs\/Twist\"}}. The ROS message contains the commanded linear and angular velocity data, and another node takes this message and controls the vehicle with respect to these commanded velocities.\n\n\n\\begin{figure}[h!] \n \\centerline{\\includegraphics[width=.8\\linewidth]{3.trajectory_planning\/figures\/local_plan.png}}\n \\vspace{5mm}\n \\caption{Local Planner trajectory generation \\cite{3_local_planner}.}\n \\vspace{5mm}\n \\label{fig:planner}\n\\end{figure}\n\n\n\n\n\n\n \n \n \n \n\n\n\n\n\\subsection{Global and Local Costmaps}\n\nCosmaps are basically maps that stores the costs of every point on the map that is calculated based on sensor measurements or provided by a known map. 2D and 3D costmaps can be constructed, but for ground vehicles like the MIT RACECAR 2D costmaps are more suitable since the movement in z-axis can be neglected. \n\n\\clearpage\n\nMainly, the costs of cells in costmaps are calculated based on whether there is an obstacle on that cell. In addition to this, another source of cost is the distance of the cell to an obstacle. This \"distance\" is named \\textbf{\\textit{inflation}} in ROS Navigation stack. The inflation is calculated by parameters called \\textbf{\\textit{inflation\\_radius}} and \\textbf{\\textit{cost\\_scaling\\_factor}}. These parameters are required for deciding whether the cell will be determined as near an obstacle or not, and decay rate of inflation cost. The effect of inflation parameters on the cost of a cell can be seen in Figure \\ref{fig:inflation}.\n \n\n\\begin{figure}[h!] \n \\centering\n \\includegraphics[width=\\linewidth]{3.trajectory_planning\/figures\/inflation.png}\n \\vspace{5mm}\n \\caption{The effect of inflation parameters on cell cost \\cite{3_ros_costmap}}\n \\vspace{5mm}\n \\label{fig:inflation}\n\\end{figure}\n\n\nAlso, another important parameters of costmaps are \\textbf{\\textit{cost\\_factor}} and \\textbf{\\textit{neutral\\_cost}}. These parameters determine the smoothness and the curvature of the calculated path. The effects of these parameters can be seen in Figure \\ref{fig:cf_effect} and Figure \\ref{fig:nc_effect}.\n\n\n\\clearpage\n \n\n\\begin{figure}[h!] \n \\centerline{\\includegraphics[width=\\linewidth]{3.trajectory_planning\/figures\/cf_0.01_0.55_3.55.png}}\n \\vspace{5mm}\n \\caption{The effect of cost factor on generated path (cost\\_factor=0.01, cost\\_factor=0.55, cost\\_factor=3.55) \\cite{3_tuning_guide}.}\n \\vspace{5mm}\n \\label{fig:cf_effect}\n\\end{figure}\n\n\\begin{figure}[h!] \n \\centerline{\\includegraphics[width=\\linewidth]{3.trajectory_planning\/figures\/nc_1_66_233.png}}\n \\vspace{5mm}\n \\caption{The effect of neutral factor on generated path (neutral\\_factor=1, neutral\\_factor=66, neutral\\_factor=233) \\cite{3_tuning_guide}.}\n \\vspace{5mm}\n \\label{fig:nc_effect}\n\\end{figure}\n\n\nAnd lastly, it should be noted that, as the names suggests, global costmaps are for global planners and local costmaps are for local planners. Although, there is no difference between global costmaps and local costmaps in fact, their parameter configurations are differs. The map sizes and sensors are main elements that can be difference between them.\n\n\n\n\\clearpage\n\n\n\n\n\\section{Trajectory Planning Methods}\n\nIn this project, 3 different trajectory planning algorithms is considered and applied on MIT RACECAR platform. These algorithms are; Dynamic Window Approach(DWA), Time Elastic Band(TEB) and Artificial Potential Field(APF).\n\n\\subsection{Dynamic Window Approach}\n\nDWA is a proven concept that is used for a long time in robotics. The method was first proposed in 1999 \\cite{3_dwa}. And it is still a popular method for mobile robots. DWA provides a controller between global path and the robot. It calculates the cost function for different control inputs and searches for the maximum scored trajectory to follow. Thanks to its simplicity and easy implementation, DWA is a good choice as the first algorithm for trajectory planning. Basic working principle of DWA can be expressed as below.\n\n\\begin{enumerate}\n \\item Take dx, dy and dtheta samples from control space \n \n \\item Predict next states from current states based on sampled dx, dy and dtheta.\n \n \\item Score each trajectory from predictions with distance to obstacles, distance to the goal, distance to the global path, and speed and remove the trajectories that collide with obstacles.\n \n \\item Choose the trajectory with the highest score and send the trajectory to the robot.\n \n \\item Repeat until goal is reached.\n\\end{enumerate}\n\n\nIn order to be able to use DWA effectively, DWA needs to be configured properly. DWA has parameters that will configure robot configuration, goal tolerance, forward simulation, trajectory scoring and global plan. One of the important parameters of the DWA is sim time parameter. Sim time is essentially the time length that will DWA plan for, and this parameter heavily affects computation time of the trajectory. In tests, it is seen that when sim time is set to low values like 2.0 seconds or less, the performance of DWA is not sufficient for passing complex pass ways because it can not see what will happen after the sim time. This result in a suboptimal trajectory. Also, it should be noted that, since all trajectories generated by DWA is simple arcs, setting the sim time to high value like 5 seconds will result in long curves that are not very flexible. Thus, in order to achieve good performance with DWA, setting sim time to an optimum value is a must.\n\n\n\\begin{figure}[h!] \n \\centerline{\\includegraphics[width=\\linewidth]{3.trajectory_planning\/figures\/sim_time.png}}\n \\caption{The Effect of Simulation Time on Generated Trajectory (sim\\_factor=1.5, sim\\_time=4.0 \\cite{3_tuning_guide}}\n \\label{fig:planner}\n\\end{figure}\n \n \nAside from sim time, there are a few other factors to consider. Samples of velocity vx sample and vy sample are two parameters that determine how many translational velocity samples should be taken in the x and y directions, respectively. The number of rotational velocities samples is controlled by vth sample. The number of samples you want to take is determined by your computer's processing power. Because turning is generally more complicated than moving straight ahead, it is preferred to set vth samples to be higher than translational velocity samples in most cases. Since, our vehicle is non-holonomic there is no need for velocity samples in y direction. Simulation granularity is the step size between points on a trajectory that is referred to as sim granularity. It essentially means how often the points on this trajectory should be examined. A lower value indicates a higher frequency, which necessitates more processing power. \n\nLastly, as an addition to all of these parameters, DWA can also plan both for holonomic and non-holonomic robots, but it does not support Ackermann drive robots as MIT RACECAR. Still, DWA was implemented on MIT RACECAR and got acceptable results. \n\n\n\n\\clearpage\n\n\n\n\n\n\n\n\n\\subsection{Time Elastic Band}\n\nTime Elastic Band(TEB) planner was proposed by R\u00f6smann as an improved version of elastic band algorithm \\cite{3_teb}. The classic elastic band algorithm is based on optimizing a global path for the shortest path length. However, TEB optimizes the path by the time-optimal objective function. Also, while elastic band does take into account of dynamic constraints, TEB considers kino-dynamic constraints in the trajectory planning. Additionally, TEB planner also supports non-holonomic car-like robots.\n\nTEB planner is essentially a solution to a sparse scalarized multi-objective optimization problem. The multi-objective problem includes constraints like maximum velocity, maximum acceleration, minimum turning radius etc. Since this optimization problem does not always have only one solution, TEB can get stuck in locally optimum points. Thus, sometimes the robot can not pass an obstacle even if there is a possible trajectory which the robot can follow. In order to solve this local minimum problem, an improved version of TEB was proposed \\cite{3_teb_2}. With this improved version, TEB optimizes a globally optimal trajectory parallel to the multi-objective optimization problem that it already solves. The algorithm switches to this new globally optimum solution when necessary, and this way the local minimum problem of TEB is solved.\n\nDue to being numerous kino-dynamic constraints for a car-like robot, TEB has numerous weights for each constraint and effects of weights on the generated trajectory depends on other weights. For this reason, optimizing TEB planner parameters requires good understanding of the concept, attention and controlled experiments. In this project, in order to simplify this process, initial values of the TEB planner was optimized in simulation environment and final configuration fine-tuned in real time tests. The results showed that while MIT RACECAR can avoid obstacles smoothly with TEB planner, the planner still requires more tuning for getting better results while driving narrow areas. \n\n\n\n\n\\begin{figure}[h!] \n \\centerline{\\includegraphics[width=\\linewidth]{3.trajectory_planning\/figures\/teb_chart.png}}\n \\vspace{5mm}\n \\caption{Flowchart of TEB Algorithm \\cite{3_teb}}\n \\vspace{5mm}\n \\label{fig:planner}\n\\end{figure}\n\n\n\\clearpage\n\n\n\n\n\\section{Artificial Potential Field Method}\n\nArtificial Potential Field(APF) Method is a basic approach for trajectory planning that is widely used by both industrial and academical applications. APF basically creates artificial attractive vectors that diverts vehicle to the goal position and repulsive vectors from obstacles that diverts vehicle from obstacles. Basically, addition of these attractive and repulsive vectors results in a target direction and speed for the vehicle. \n\n\n\n\\begin{equation}\n U(q) = U_{attractive}(q) + U_{repulsive}(q)\n\\end{equation}\n\n\n\nAnd additionally, magnitudes of these vectors are defined by user. By tuning these magnitudes, the user can tune how close the robot will navigate through obstacles. With the help of this simple calculation, APF calculates how much it should divert from current heading.\n\nAs an addition to determining target orientation of the vehicle, speed of the vehicle can be calculated with the help of APF method by the density of obstacles. Simply, the density of obstacles determines the targeted velocity for the vehicle. \n\n\n\n\\begin{equation}\n V = V_{max} - K_{gain} \\cdot n_{obstacles}\n\\end{equation}\n\n\n\n\\begin{figure}[h!] \n \\centerline{\\includegraphics[width=.4\\linewidth]{3.trajectory_planning\/figures\/apf.jpg}}\n \\vspace{5mm}\n \\caption{Basic trajectory generation of APF Method}\n \\label{fig:planner}\n\\end{figure}\n\n\nAPF method has a very basic approach for trajectory planning. And this approach provides a non-complex solution to the problem with less computational costs. The main reason for choosing this method is this direct approach, but it has some problems that we will discuss in the next chapter. \n\n\n\\subsection{Problems of APF Method}\n\n\nThe main problem with the APF method is the local minimum problem. Similar to the previous version of TEB algorithm, APF can get stuck in local minimum points. As it can be seen in figure below, when repulsive forces from obstacles are equal in both directions, it calculates the resultant vector as the vehicle moves towards to the obstacle. This can result in a collision for the vehicle. Another problem of the APF method is Goal Non-reachable with Obstacles Nearby(GNRON) problem. In such cases that the goal point is near an obstacle, the repulsive force from the obstacle can prevent the robot from reaching the goal point by resulting in a local minimum that is close to the goal point.\n\nThese two problems are primary problems of APF method and various solutions are proposed like Evolutionary APF \\cite{3_apf_evo}, Fuzzy APF \\cite{3_apf_fuzzy} as modified APF algorithms. \n\n\n\\begin{figure}[h!] \n \\centerline{\\includegraphics[width=.7\\linewidth]{3.trajectory_planning\/figures\/gnron.png}}\n \\caption{Goal points with GNRON and local minimum problem \\cite{3_apf_fig}}\n \\label{fig:planner}\n\\end{figure}\n\n\nWhile implementing APF to the MIT RACECAR, these problems were solved by implementing a local minimum detection method. This method, simply checks the calculated repulsive and attractive forces and if it determines that the robot is stuck in a local minimum point, it adds a vector that will divert the vehicle from going towards to the obstacle.\n\n\n\n\n\n\n\n\\chapter{Testing and Results}\n\nReal life experimentation of trajectory planning algorithms on MIT RACECAR requires additional work. Until this part, MIT RACECAR platform is inspected by its hardware and software. Also, numerous helper package of ROS is expressed and how trajectory planning algorithms can be applied. For the real time testing of different trajectory planning algorithms, there should be a measurement for determining how well the algorithm works. For that purpose, an environment was set up in Artificial Intelligence and Intelligent Systems(AI2S) laboratory. As the scenery, a double lane curvy road with obstacles in different locations was chosen. MIT RACECAR was expected to stay in lane and when it encounters to an obstacle, to pass the obstacle by changing the lane. \n\n\nAs a result of the chosen scenery, it is needed to detect lanes on the road. For achieving this, ZED Camera was used for image processing. Image processing in this project is achieved by OpenCV library. An image processing algorithm that detects lanes and returns goal points was designed with Python. Goal points are generated from detected lanes with respect to the look ahead distance that is determined by velocity of the vehicle dynamically.\n\nAnother aspect of the project is obstacle detection. Obstacle detection is done with the help of RPLidar A2 2D lidar. RPLidar provides users a ROS package that handles communication between computer and lidar and publishes obstacle information as a ROS topic. Lastly, all of these outputs are given to the path planning algorithm as target points and obstacles.\n\n\n\n\n\\section{Test Environment}\n\nIn order to create the required environment, a double lane curvy road was draw on the floor of AI2S Laboratory by electrical tapes. While making the road for MIT RACECAR, constraints like width of the vehicle, minimum turning radius of the vehicle are considered. Also, it is tried to avoid environmental effects like flare on the floor because of the lights. Since, the reference is being obtained from image processing, the flare is changing the quality of the reference signal in a bad manner and causes the vehicle to go out of the lane. \n\n\nAlso, the turning radius is an important constraint in such small environments, especially when obstacle avoidance is required. Thus, the curvature of the road was tried to be kept small enough to turn but also, big enough to see the limits of the algorithm.\n\nDouble lane structure of the road was chosen for lane changing when encountered by an obstacle. This structure also provides a good use case like lane changing and overtaking problems as future works. Besides, the middle lane of the road was chosen red in order to provide easier recognition of the lane. Since red color is easy to recognize with color filters like HSV, this provides a good starting point for the lane detection and leaves more time to focus on trajectory planning algorithms. The most painful part of the generated scenario for this study was that the changeable light conditions in the environment had a serious effect on the lane recognition algorithm's performance.\n\n\\begin{figure}[h!] \n \\centerline{\\includegraphics[width=\\linewidth]{4.testing_results\/figures\/4_environment.png}}\n \\vspace{5mm}\n \\caption{Testing environment}\n \\label{fig:testing_env}\n\\end{figure}\n\n\\clearpage\n\n\n\n\n\n\n\n\n\\section{Lane Detection Algorithm}\n\nLane detection algorithm, which is a crucial part of the scenery that is chosen for the project, will be explained in this section. Lane detection algorithm consists of OpenCV functions that will not be explained since it is out of the scope of the project. The algorithm is designed to have two main parts for its being modular and easy to understand.\n\n\n\\begin{figure}[h!] \n \\vspace{5mm}\n \\centerline{\\includegraphics[width=\\linewidth]{4.testing_results\/figures\/main_flow.png}}\n \\vspace{5mm}\n \\caption{Main structure of the lane detection algorithm}\n\\end{figure}\n\n\nThe first part of the algorithm, the pre-processing part, is responsible for extracting lane information from camera input by eliminating everything except lanes itself from the image. The first idea for the algorithm was based on the idea to extract black lanes and red lane individually. For this purpose, a complex image processing algorithm that can be seen in Figure \\ref{fig:pre_1} was proposed. However, the computational cost of this proposed method was not affordable for Jetson TX2. As a result of this problem, feedback loop of the system is updated only on 5-8 times average in one second and this situation was resulting in a hard control problem and slow response system. \n\n\n\\clearpage\n\n\n\\begin{figure}[h!] \n \\centerline{\\includegraphics[width=\\linewidth]{4.testing_results\/figures\/pre_1.png}}\n \\vspace{5mm}\n \\caption{Flow of the first proposed image pre-processing algorithm}\n \\label{fig:pre_1}\n\\end{figure}\n\n\nIn order to overcome these problems, a new, more plain algorithm that can be seen in Figure \\ref{fig:pre_2} was proposed. The idea of the new algorithm is that it is not needed to find all the lanes individually, it is only needed to find red lane which is easier to find respectively. As a result, the new proposed method is less accurate but more effective and faster, and this loss of accuracy is a neglectable amount.\n\n\n\\begin{figure}[h!] \n \\centerline{\\includegraphics[width=\\linewidth]{4.testing_results\/figures\/pre_2.png}}\n \\vspace{5mm}\n \\caption{Flow of the final image pre-processing algorithm}\n \\label{fig:pre_2}\n\\end{figure}\n\n\n\n\\clearpage\n\n\n\nThe pre-processing part of the algorithm uses mainly HSV masking, morphological operations and filtering contours based on sizes and area. The flow of the algorithm with an example input image can be inspected in Figure \\ref{fig:pre_2}. \n\n\n\\begin{figure}[h!] \n \\centerline{\\includegraphics[width=\\linewidth]{4.testing_results\/figures\/pre_example.png}}\n \\vspace{5mm}\n \\caption{Example output of pre-processing image with stages}\n \\vspace{5mm}\n \\label{fig:pre_2}\n\\end{figure}\n\n\nThe second part of the algorithm takes the binary image that is the output of the pre-processing part as input. This part of the algorithm firstly takes the perspective transformation of the lane image to the Bird-Eye view. After taking transformation, the contour information of the image is extracted and applied some filtering again. After all of these processes, a basic second degree polynomial is fit for the lane. This is required because in some cases, only a small part of the lane is visible and the coordinates of the target point at the look ahead distance is required for path planning. Additionally, some coordinate transformations is applied to the target point and the point is passed as output of the lane detection algorithm. The flow of the entire algorithm can be seen in Figure \\ref{fig:entire_flow} and an example output of the lane detection algorithm is also can be seen in Figure \\ref{fig:detect_out}. \n\n\n\n\\clearpage\n\n\\begin{landscape}\n\n\\begin{figure}[h!] \n \\centerline{\\includegraphics[width=\\linewidth]{4.testing_results\/figures\/entire_flow.png}}\n \\vspace{5mm}\n \\caption{The flow of the entire lane detection algorithm}\n \\label{fig:entire_flow}\n \\vspace{5mm}\n\\end{figure}\n\n\n\\clearpage\n\n\\end{landscape}\n\n\n\n\n\n\\begin{figure}[h!] \n \\centerline{\\includegraphics[width=\\linewidth]{4.testing_results\/figures\/detect_out.png}}\n \\vspace{5mm}\n \\caption{An example output of the lane detection algorithm}\n \\vspace{5mm}\n \\label{fig:detect_out}\n\\end{figure}\n\n\n\\clearpage\n\n\n\\section{Testing and Results}\n\n\nIn this chapter, an overall qualitative assessment of the trajectory planning algorithms is given. This assessment relies on how successfully the vehicle followed the lanes, how many obstacles the vehicle pass through without collision and some special comments on the algorithm behavior. Since there is no global position data source in AI2S laboratory environment, an overall numeric error can not be calculated. \n\n\nWhile testing the algorithms, it is tried to keep the environment same for all the algorithms. Nevertheless, some environmental circumstances like lighting condition can be changed. During the testing a Rviz is used which can be seen in Figure \\ref{fig:rviz} for visualizing the vehicle condition from the vehicle's perspective, global plan and local plan that is manipulated by trajectory algorithm. \n\n\n\\begin{figure}[h!] \n \\centerline{\\includegraphics[width=\\linewidth]{4.testing_results\/figures\/global_local.png}}\n \\vspace{5mm}\n \\caption{An example Rviz view with global plan (red) and local plan (purple)}\n \\vspace{5mm}\n \\label{fig:rviz}\n\\end{figure}\n\n\n\nIn addition, how the trajectory planning manipulates the target points which are determined by lane detection can be seen in Figure \\ref{fig:manipulator}. At the top side of the figure, how the vehicle sees the environment can be seen and below the image from camera and lane detection algorithm can be seen. While the red arrow is the output of the lane detection algorithm, the purple arrows indicates the planned trajectory that will avoid the obstacle without leaving the road.\n\n\n\\clearpage\n\n\n\n\\begin{figure}[h!] \n \\centerline{\\includegraphics[width=\\linewidth, height=23cm]{4.testing_results\/figures\/manipulator.png}}\n \\vspace{5mm}\n \\caption{An example view that trajectory planning is running (red arrow is the output of the lane detection, the purple arrows are the planned trajectory)}\n \\label{fig:manipulator}\n\\end{figure}\n\n\n\\clearpage\n\n\nThe final assessment about the trajectory planning algorithms can be inspected in Table \\ref{tab:assessment}. But as a matter of fact, it should be said that this assessment is only according to the chosen scenery, it is not about which trajectory planning algorithm is better than the others. Besides, determining which algorithm is superior to others depends on the application scenery. As a conclusion, artificial potential field algorithm has better results according to the project requests and it is chosen to continue to the project with APF algorithm from now on. Since it provides reasonable performance with low cost, when considered possible future development of the project, the APF method is chosen.\n\n\\begin{table}[h!]\n\\centering\n\\begin{tabular}{@{}\n>{\\columncolor[HTML]{EFEFEF}}c \n>{\\columncolor[HTML]{EFEFEF}}c \n>{\\columncolor[HTML]{EFEFEF}}c @{}}\n\\toprule\n\\textbf{\\begin{tabular}[c]{@{}c@{}}Trajectory Planning\\\\ Algorithm\\end{tabular}} &\n \\textbf{Obstacles Avoided} &\n \\textbf{Comments} \\\\ \\midrule\n\\begin{tabular}[c]{@{}c@{}}Dynamic Window\\\\ Approach\\end{tabular} &\n 5 out of 7 &\n \\begin{tabular}[c]{@{}c@{}}DWA planner is easy to implement and tune. \\\\ Its performance was acceptable and stable. \\\\ Also, while avoiding obstacles, \\\\ it could stay in the course, but sometimes, \\\\ its effort was not enough to avoid obstacles. \\\\ Also, it gets stuck in complex situations\\\\ like narrow pass ways\\end{tabular} \\\\ \\midrule\n\\begin{tabular}[c]{@{}c@{}}Time-Elastic Band \\\\ Planner\\end{tabular} &\n 7 out of 7 &\n \\begin{tabular}[c]{@{}c@{}}TEB planner is very successful to model the \\\\ vehicle, and it is suitable for complex \\\\ environments. But, it is very hard to tune \\\\ effectively and sometimes having so many\\\\ parameters to tune is turning into \\\\ a disadvantage instead of an advantage. \\\\ Also, since it heavily relies on \\\\ an optimization problem, computational \\\\ cost gets very high, especially \\\\ in complex environments.\\end{tabular} \\\\ \\midrule\n\\begin{tabular}[c]{@{}c@{}}Artificial Potential\\\\ Field\\end{tabular} &\n 5 out of 7 &\n \\begin{tabular}[c]{@{}c@{}}APF is the easiest to implement and tune \\\\ by far. Thanks to its basic logic based \\\\ on simple math, it provides an acceptable \\\\ result with low cost. And also, \\\\ in line with the application scenery that is \\\\ selected for this project, it is not \\\\ too much deviates the vehicle from the road.\\end{tabular} \\\\ \\bottomrule\n\\end{tabular}\n\\caption{The assessment of the experiments of the trajectory generation algorithms}\n\\label{tab:assessment}\n\\end{table}\n\\chapter{Conclusion and Future Works}\n\n\nPath planning algorithms are gaining more and more importance as autonomous vehicles become more widespread and important nowadays. There are many types of path planning applications used as the navigation unit of autonomous vehicles or for security purposes in semi-autonomous vehicles. In this project, which aims to implement and test a few of the path planning applications in real-time, a testing environment was first established and an image processing-based lane tracking algorithm was developed for this environment as reference signal to the vehicle. After the test environment was created, DWA, TEB and APF methods were implemented and tested separately. Since problems such as overtaking will be studied in the later parts of the project, it is of great importance to choose an algorithm that is predictable and low in cost. For this reason, it was deemed appropriate to continue the next stages of the project with APF. However, this does not mean that APF is better than others. However, all route planning applications have advantages and disadvantages over each other. \n\nIn addition, in the next stages of the project, it is aimed to establish an end-to-end neural network structure and to carry out an end-to-end control with this structure. It is aimed to give sensor data such as camera and lidar as input to neural network and to obtain control signals such as vehicle speed and steering angle. \n\n\n\n\n\n\n\n\n\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzbvfa b/data_all_eng_slimpj/shuffled/split2/finalzzbvfa new file mode 100644 index 0000000000000000000000000000000000000000..0c2195ba9d4c010f9130aa837b064eedb150e92c --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzbvfa @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\nBetavoltaic effect refers to the electric power production by a p-n junction bombarded by beta-particles that ionize the semiconductor material. Among the advantages of beta-batteries are their long service duration, amounting to years or even decades, and the possibility to use in the hard-to-reach areas. Betavoltaics and photovoltaics are related disciplines. In both cases, electric power results from the separation of electron-hole pairs produced by beta-electrons or photons by a p-n junction in the presence of a load in the circuit. In comparison to photovoltaics, publications in the field of the basic principles and applications of betavoltaic elements have been less numerous initially (see, e.g., Refs.~\\cite{Rap54, Pfa54, Rap56, Fli64, Ols73, Ols74, Olstech}), but started to attract the attention of the researchers in the recent years \\cite{And00, Bow02, Ada12, Ols12}. \n\nThe main task in betavoltaic design is the choice of a beta-source\/semiconductor combination, which should meet certain requirements. In particular, the beta-particles produced by the source must be absorbed efficiently by the semiconductor. Within the semiconductor, the diffusion length of the electron-hole pairs generated by the beta-flux should be large enough to allow them to reach the p-n junction with as little losses as possible. Because only the relatively low-energy beta-electrons are utilized effectively (with energies varying between 5 and 70 keV) for the realistic semiconductor thicknesses, three main beta-sources are presently employed in betavoltaic applications: Tritium \\Tr, Nickel $^{63}$Ni, and Promethium \\Pm. The respective mean energies of the electrons produced by these sources are 5.7, 18, and 62 keV.\n\nThe efficiency, $\\eta$, of a betavoltaic converter is proportional to the collection coefficient, $Q$, of the electron-hole pairs generated by the beta-flux. In Refs.~\\cite{Pfa54, Olstech}, $Q$ was calculated under the assumption that the generation function of electron-hole pairs by a beta-flux $g(x) \\propto \\exp(-\\alpha x)$. In reality, the generation function is close to zero within the so-called ``dead layer'' under the front surface, and exhibits a maximum at some distance $x_m$ from the surface \\cite{Dmi78}. This implies that this exponential approximation is correct starting from some $x$-value greater than $x_m$. The emergence of the maximum in the $g(x)$ curve is due to the fact that, initially, the primary electrons pass through the semiconductor with only weak scattering. The dead layer thickness $x_m$ increases with the energy of the incident beta-electrons. For GaAs, $x_m$ is in the range 0.1 -- 1 $\\mu$m \\cite{Dmi78}.\n\nAlthough the works \\cite{Pfa54, Olstech} do report analytical expressions for $Q$ (obtained under the assumption of the absence of the dead layer), the values of $Q = 1$ and 0.7 were used in the calculations of beta-conversion efficiency \\cite{Olstech, Ols12}. While the value $Q = 1$ corresponds to the limiting conversion efficiency that is maximal in principle, the choice $Q = 0.7$ was not explained in \\cite{Olstech, Ols12}.\n\nIn this work, we derive an expression for $Q$ taking the dead layer into account, and also using the realistic values of the nonradiative Shockley-Reed-Hall (SRH) recombination lifetime, $\\tau_{SR}$, for direct-bandgap semiconductors. In such materials, the values of $\\tau_{SR}$ are usually short, and are in the range of $10^{-9}-10^{-7}$ s. We use the so obtained collection coefficient to derive the expression for the realistically attainable beta-conversion efficiency $\\eta$ of various combinations of beta-sources and direct-bandgap semiconductors. When calculating the efficiency, we focus on GaAs as a typical example. We show that decreasing $\\tau_{SR}$ and increasing the dead layer thickness leads to a strong reduction of $Q$ below 1, and to the corresponding reduction of the beta-conversion efficiency.\n\n\\section{Analysis of the collection coefficient}\nWe assume that the electron-hole pairs are generated only weakly within the dead layer, $x < x_m$, while for $x > x_m$, the generation function has the form $g(x) = I_0\\,\\exp(-\\alpha (x-x_m))$, where $I_0$ is the electron-hole pair generation rate in the $x_m$-plane, and $\\alpha^{-1}$ is the characteristic decay length. Furthermore, we assume that $d_p < x_m$ and $S_d \\ll D\/L$, $d_p$ being the junction depth, $S_d$ the recombination rate on the back surface of the base, and $L$ and $D$ the diffusion length and coefficient of the excess electron-hole pairs generated in the base region. The sketch of our structure is summarized in Fig.~\\ref{fig1}.\n\n\n\\begin{figure}[t!] \n\\includegraphics[scale=0.4]{fig1.eps}\n\\caption{Schematic illustration of a p-n junction of thickness $d = d_p + d_b$, where $d_p$ is emitter depth and $d_b$ is base thickness. The dead layer of thickness $x_m$ extends into the base region.}\n\\label{fig1}\n\\end{figure}\n\nApart from the SRH mechanism with the lifiteme $\\tau_{SR}$, the electron-hole pairs in GaAs also recombine radiatively; the characteristic time of this process is $\\tau_r = (AN_d)^{-1}$, where $A$ is the radiative recombination coefficient, and $N_d$ is the base doping concentration. Therefore, the diffusion length can be written as\n\\begin{equation}\nL = (D\\tau_b)^{1\/2}\\ ,\n\\label{1}\n\\end{equation}\nwith $\\tau_b = \\left(\\tau_{SR}^{-1} + \\tau_r^{-1}\\right)^{-1}$ being the effective lifiteme in the neutral base region.\n\nContinuity equation for the excess concentration of the electron-hole pairs, $\\Delta p_1$, within the dead layer (i.e., for $x < x_m$, region 1), where generation is negligible, has the form\n\\begin{equation}\n\\frac{d^2\\Delta p_1}{dx^2} - \\frac{\\Delta p_1}{L^2} = 0\\ ,\n\\label{2}\n\\end{equation}\nIn the rest of the semiconductor ($x > x_m$, region 2), the continuity equation for the excess electron-hole pair density, $\\Delta p_2$, is\n\\begin{equation}\n\\frac{d^2\\Delta p_2}{dx^2} - \\frac{\\Delta p_2}{L^2} = -\\frac{\\alpha I_0\\,e^{-\\alpha (x-x_m)}}{D}\\ .\n\\label{3}\n\\end{equation}\nThe equations (\\ref{2}) and (\\ref{3}) are supplemented by the boundary conditions\n\\begin{eqnarray}\n&&\\Delta p_1(x = d_p) = 0\\ ,\\ \\ \\frac{d\\Delta p_2}{dx}(x = d) = 0\\ , \\nonumber \\\\ \n&&\\Delta p_1(x = x_m) = \\Delta p_2(x = x_m)\\ ,\\nonumber \\\\\n&&\\frac{d\\Delta p_1}{dx}(x = x_m) = \\frac{d\\Delta p_2}{dx}(x = x_m)\\ .\n\\label{4}\n\\end{eqnarray}\nThe first condition reflects the fact that the electron-hole pairs are separated at the junction depth. The second one indicates the absence of surface recombination at the back of the base. The remaining two expressions are the usual continuity conditions for $\\Delta p(x)$ and $d\\Delta p(x)\/dx$ at $x = x_m$. The collection coefficient is then defined as the ratio of the current at the junction depth, $d_p$, to the pair generation rate in the plane of highest generation at $x = x_m$:\n\\begin{equation}\nQ = \\frac{D}{I_0}\\frac{d\\Delta p_1}{dx}(x = d_p)\\ .\n\\label{5}\n\\end{equation}\nThe solution of (\\ref{2}) and (\\ref{3}) that satisfies the first two conditions (\\ref{4}) can be written as\n\\begin{eqnarray}\n&&\\Delta p_1(x) = C\\sinh\\frac{x - d_p}{L}\\ ,\\nonumber \\\\\n&&\\Delta p_2(x) = C'\\cosh\\frac{x - d}{L} \\nonumber \\\\\n&&\\ \\ \\ \\ \\ \\ \\ \\ \\ \\ + B\\left(e^{-\\alpha(x - x_m)} - \\beta\\,e^{-x\/L}\\right)\\ ,\\nonumber \\\\\n&&B = \\frac{\\alpha\\,I_0\\,L^2}{D\\left(1 - \\alpha^2L^2\\right)}\\ ,\\ \\ \\beta = \\alpha L\\exp{\\left[\\left(\\frac{1}{L} - \\alpha\\right)d\\right]}\n\\end{eqnarray}\nwith constants $C$, $C'$ to be determined from the remaining two conditions (\\ref{4}). This procedure yields:\n\\begin{eqnarray}\n&&Q = \\alpha L\\,\\times \\nonumber \\\\\n&&\\frac{\\alpha L\\left(\\cosh\\frac{d - x_m}{L} - e^{-\\alpha(d - x_m)}\\right) -\\sinh\\frac{d - x_m}{L}}{\\left[(\\alpha L)^2-1\\right]\\cosh\\frac{d - d_p}{L}}\\ .\n\\label{8}\n\\end{eqnarray}\nIf $d - x_m \\gg L$ and $\\alpha(d - x_m) \\gg 1$, this expression simplifies to\n\\begin{equation}\nQ = \\frac{\\alpha L}{1 + \\alpha L}e^{(d_p - x_m)\/L}\\ .\n\\label{8a}\n\\end{equation}\n\n\\begin{figure}[t!] \n\\includegraphics[scale=0.25]{fig2.eps}\n\\caption{(a) Collection coefficient, $Q$, as a function of the diffusion length, $L$, for different absorption coefficients, $\\alpha$, in the limit $\\alpha (d - x_m) \\gg 1$, $d - x_m \\gg L$, see Eq.~(\\ref{8a}). The values used, $\\alpha = 10^5, 6\\cdot 10^3$, and $6\\cdot 10^2$\\,cm$^{-1}$, approximately correspond to the respective mean beta-energies of $5.7, 20$, and $60$\\,keV for GaAs-based p-n junction \\cite{Tri67}. The dashed curves are calculated for different dead layer thicknesses, $x_m$, and $d_p = 10^{-5}$ cm. The solid curves are from the standard relation $Q = \\alpha L\/(1 + \\alpha L)$, valid in the absence of the dead layer. (b) Collection coefficient (\\ref{8a}) for different junction depth values for $x_m = 10^{-5}$\\,cm and $\\alpha = 10^5$\\,cm$^{-1}$, corresponding to the beta-particle energy of about 5.7\\,eV in the \\Tr\/GaAs combination.}\n\\label{fig2}\n\\end{figure}\n\nFig.~\\ref{fig2} shows the dependence of the collection coefficient $Q$ on the diffusion length from Eq.~(\\ref{8a}). As seen in this figure, the strongest reduction of $Q$ due to the presence of the dead layer is for the case of the \\Tr\\ beta-source. The smallest discrepancy in the $Q$-values obtained with and without taking into account the dead layer is found for the curves corresponding to $\\alpha = 6\\cdot 10^2$\\,cm$^{-1}$, realized in the case of the \\Pm-source. In this case, to obtain $Q > 1\/2$, one would need the diffusion length $L > 35\\ \\mu$m. The values $Q \\approx 1$ can be achieved only in Si p-n junctions with long minority carrier lifetimes \\cite{Gor00}.\n\nIn Fig.~\\ref{fig2}(b), the junction depth was varied at a fixed electron energy (and thus constant $\\alpha$) and dead layer thickness. As seen in this figure, the collection coefficient increases not only upon increasing $L$, but also upon approaching the junction depth to the $x_m$-value. This effect is especially important for small diffusion length $L$.\n\nA further conclusion from Fig.~\\ref{fig2} is that collection of the electron-hole pairs generated by the electron flux will be quite efficient in the case when the diffusion length exceeds the dead layer thickness, $L > x_m$. An alternative way to increase $Q$ is to use deeper junctions with $d_p \\approx x_m$.\n\nLet us find the relation between the diffusion length and SHR lifetime $\\tau_{SR}$ for the case of GaAs. The radiative recombination coefficient $A$ in GaAs is an effective parameter defined by the relation $A = A_0(1 - \\gamma_r)$ \\cite{Din11}, where $A_0 \\approx 6\\cdot 10^{-10}$ cm$^3$\/s \\cite{Sach14}, and $\\gamma_r$ is the photon re-absorption coefficient. In our calculations, we assumed the value $A = 2\\cdot 10^{-10}$\\,cm$^3$\/s, as can be derived for poorly reflecting GaAs-based plane-parallel p-n structures without multiple reflection using the approach from \\cite{Din11}. In the work \\cite{Sach14}, it was shown that for realistic lifetimes $\\tau_{SR}$, the open-circuit voltage $V_{OC}$ of GaAs-based p-n junctions increases with the base doping level, $N_d$, and, taking into account the interband Auger recombination, it has a maximum at $N_d \\approx 10^{17}$\\,cm$^{-3}$.\n\nLet us first assume that the GaAs p-n junction base is of p-type, and the diffusion coefficient of electron-hole pairs is 50 cm$^2$\/s. Then, for $A \\approx 2\\cdot 10^{-10}$\\,cm$^3$\/s, $N_d = 10^{17}$ cm$^{-3}$, and lifetimes $\\tau_{SR} = 10^{-9}, 10^{-8}$, and $10^{-7}$ s, diffusion length $L$ has the respective values of 2.2, 6.45, and 12.9 $\\mu$m. \n\n\\begin{figure}[t!] \n\\includegraphics[scale=0.25]{fig3.eps}\n\\caption{Collection coefficient $Q$ of a \\Tr\/GaAs betavoltaic pair as a function of the junction depth for (a) p-type base and (b) n-type base.}\n\\label{fig3}\n\\end{figure}\n\nFig.~\\ref{fig3}(a) shows the dependence of the collection coefficient, $Q$, of a pair \\Tr\/GaAs as a function of the junction depth, $d_p$, for these three values of $\\tau_{SR}$ at $x_m = 0.15\\,\\mu$m \\cite{Dmi78} and junction thickness $d = 10\\,\\mu$m. As seen in this figure, $Q$ is close to 1 for $\\Delta x = x_m - d_p < 0.1\\,\\mu$m. For $\\Delta x > 0.1\\,\\mu$m, the $Q$-value decreases with $\\Delta x$, but remains rather large.\n\nPresented in Fig.~\\ref{fig3}(b) is the collection coefficient vs. $d_p$ for the case when the base region of the p-n junction is of the n-type. In this case, for $\\tau_{SR} = 10^{-9}, 10^{-8}$, and $10^{-7}$\\,s, and $A \\approx 2\\cdot 10^{-10}$\\,cm$^3$\/s and $N_d = 10^{17}$\\,cm$^{-3}$, and taking into account that $D = 7$\\,cm$^2$\/s, the diffusion length $L = 0.83, 2.41$, and $4.83\\,\\mu$m, respectively. As seen in the figure, in this case $Q$ is also quite large. For $\\tau_{SR} = 10^{-7}$ and $10^{-8}$\\,s, $Q$ is still close to 1, while for $\\tau_{SR} = 10^{-9}$\\,s, $Q$ exceeds 0.75 even for small $\\Delta x$.\n\nIt should be noted that, because of rather strong absorption of the electrons emitted by the \\Tr-source by the auxiliary layers of a betavoltaic element (such as protection coating or contact layers), additional reduction of the beta-generated current can take place, leading to the efficiency reduction.\n\nLet us now analyze the collection coefficient for the \\Pm\/GaAs pair. In this case, according to \\cite{Tri67}, $\\alpha \\approx 600$\\,cm$^{-1}$, i.e., excess electron-hole density decays much more slowly than in the \\Tr\/GaAs case. For this the inequality $\\alpha L \\gg 1$ is alway satisfied even for the shortest lifeteme of $10^{-9}$\\,s. In contrast, for \\Pm\\ source, $\\alpha L = 1.5$ for $L = 25\\,\\mu$m, while $\\alpha L = 0.06$ for $L = 1\\,\\mu$m, so that $Q$ is always notably smaller than 1.\n\nBut this is not the only reason for the reduction of $Q$ in realistic \\Pm\/GaAs structures. When manufacturing solar cells based on the direct-bandgap semiconductors, such as GaAs, full thicknesses of p-n junctions are chosen rather small (of the order of a few $\\mu$m). Such structures were used in \\cite{And00}. In contrast, for the \\Pm\/GaAs pair used in betavoltaics, the situation might be very different, especially for large values of $L$. In this case, the product $\\alpha d$ will be small, so that for full absorption of beta-flux much thicker p-n junctions are required compared to those typically used in photovoltaics.\n\n\\begin{figure}[t!] \n\\includegraphics[scale=0.25]{fig4.EPS}\n\\caption{Collection coefficient of a \\Pm\/GaAs betavoltaic element as a function of junction depth for different Shockley-Reed lifetimes and element thicknesses for the case of (a) p-type base and (b) n-type base. }\n\\label{fig4}\n\\end{figure}\n\nShown in Fig.~\\ref{fig4} is the collection coefficient as a function of $d_p$ for a \\Pm\/GaAs pair calculated for different lifetimes $\\tau_{SR}$ and junction thicknesses $d$ of 10 and 100\\,$\\mu$m. In this case, according to \\cite{Dmi78}, $x_m = 3\\cdot 10^{-4}$\\,cm$^3$\/s. Panels (a) and (b) correspond to the cases of p- and n-base conduction types, respectively. As seen in the figure, rather high values of $Q \\ge 0.4$ for the \\Pm\/GaAs pair can be achieved only for the junction thickness $d \\approx 100\\,\\mu$m. Also, collection coefficient decreases dramatically as $\\tau_{SR}$ decreases.\n\nIt should be noted that similar results for the attainable $Q$ are expected for other direct-bandgap A$_3$B$_5$ semiconductors, in particular, the ones based on the three-component compounds.\n\n\\section{Open-circuit voltage analysis}\nWhen estimating the limiting efficiency value \\cite{Olstech, Ada12}, we used the Shockley-Queisser approach \\cite{Sho61}, in which not only the current density, but also the open-circuit voltage, $V_{OC}$, is assumed to be maximal. Therefore, our next task is to calculate the open-circuit voltage, $V_{OC}$, with realistic values of $\\tau_{SR}$. It is given by the standard expression\n\\begin{equation}\nV_{OC} = \\frac{k_BT}{q}\\ln\\frac{N_d\\Delta p^*}{n_i^2}\\ ,\n\\label{11}\n\\end{equation}\nwhere $\\Delta p^* = \\Delta p(x = d_p + w)$ is the excess minority carrier density in the base at the boundary between the space-charge region and quasilinear region of thickness $w$, $N_d$ is the equilibrium density of the majority carriers in the quasineutral base region, and $n_i$ is the intrinsic charge carrier density. It is related to the effective densities of states in the conduction and valence bands, $N_c$ and $N_v$, as\n\\begin{equation}\nn_i = \\sqrt{N_cN_v}\\exp\\left(-\\frac{E_g}{2k_BT}\\right)\\ .\n\\label{17}\n\\end{equation}\n\nWe assume that both $d_p$ and $w$ are much smaller than the diffusion length $L$. This allows us to approximate \n\\begin{equation}\n\\Delta p(x = 0) \\approx \\Delta p^*\\ .\n\\end{equation} Such an approximation introduces a negligible error into $V_{OC}$ from Eq.~(\\ref{11}) in view of its logarithmic dependence on $\\Delta p^*$.\n\nWe will assume that recombination dominates in the quasineutral base region and in the space-charge region. Then, $V_{OC}$ can be obtained using the approach from \\cite{Sach14}. Taking into account the generation-recombination processes, we first write the continuity equation for the excess carrier density supplemented by the boundary conditions:\n\\begin{eqnarray}\n&&\\frac{d^2\\Delta p}{dx^2} - \\frac{\\Delta p}{L^2} - r(x)\\,\\Delta p(x) + g(x) = 0\\ , \\nonumber \\\\\n&&\\frac{d\\Delta p}{dx}(x = d) = 0\\ ,\\nonumber\\\\\n&& D\\frac{d\\Delta p}{dx}(x = 0) = S_0\\,\\Delta p^*\\ ,\n\\label{9}\n\\end{eqnarray}\nwhere the third term describes recombination processes in the space-charge region of the abrupt junction, and the last one corresponds to the beta-induced generation. The first boundary condition is consistent with our assumption $S_d \\ll D\/L$ from the beginning of the previous section, and the second one is responsible for recombination effects in the $x = d_p + w$ plane.\n\nIntegration of the continuity equation results in the balance equation for the generation-recombination currents, according to which the current density for electronic excitation is proportional to the integral of the generation term,\n\\begin{equation}\nJ_\\beta = q\\,\\int_0^d dx\\,\\frac{\\Delta p(x)}{\\tau_b} + q\\left(S_0 + R_{SC}\\right)\\,\\Delta p^*\\ ,\n\\label{10}\n\\end{equation}\nwhere $q$ is the elementary charge. The right-hand side in (\\ref{10}) is responsible for the recombination in the bulk and on the front side of the emitter and within the space-charge region. The space-charge region recombination rate is given by \\cite{Sze}\n\\begin{eqnarray}\n&&R_{SC}(\\Delta p^*) = \\frac{L_D}{\\sqrt{2}\\tau_{SR}}\\int_{y_{pn}}^{-1} dy\\,N_d\\,\\left(1 - y + e^y\\right)^{-1\/2}\\times \\nonumber\\\\\n&&\\Big[N_d e^y + n_i e^{E_r\/k_BT} + b\\left(\\frac{n_i^2}{N_d} + \\Delta p^*\\right)e^{-y} \\nonumber\\\\\n&&\\ \\ \\ \\ \\ \\ \\ + b n_i e^{-E_r\/k_BT}\\Big]^{-1}\\ ,\n\\nonumber\n\\end{eqnarray} \nwhere $b = \\sigma_p\/\\sigma_n$ is the ratio of the capture cross-sections of holes and electrons by a recombination level, $E_r$ is the recombination level energy measured from the middle of the bandgap, $y_{pn}$ is the dimensionless potential at the p-n boundary, $L_D$ is the Debye length.\n\nTo evaluate the first integral in (\\ref{10}), we have employed the following approximative procedure. First, we write the solution of the continuity equation (\\ref{9}) as a sum of homogeneous and inhomogeneous parts,\n\\begin{equation}\n\\Delta p(x) = \\frac{e^{-x\/L} + e^{(x-2d)\/L}}{1 + e^{-2d\/L}}\\Delta p^* + \\Delta p_i(x)\\ ,\n\\end{equation}\nwhere the homogeneous term satisfies the first boundary condition in (\\ref{9}) and gives the value $\\Delta p(x = 0) = \\Delta p^*$. The inhomogeneous contribution $\\Delta p_i(x)$, with $\\Delta p_i(x = 0) = 0$, is notably different from zero only within a relatively thin layer below the front surface of the emitter, where the generation-recombination processes take place. Therefore, the contribution to the integral of the second term can be neglected in comparison to the integral of the homogeneous term, allowing us to write\n\\begin{equation}\n\\int_0^d dx\\Delta p(x) \\approx \\Delta p^*L\\tanh(d\/L)\\ .\n\\end{equation}\nThis approximation should produce a negligible error in $V_{OC}$ in view of its logarithmic dependence on $\\Delta p^*$. Substitution of this result into Eq.~(\\ref{11}) taking into account that $L^2 = D\\tau_b$ yields\n\\begin{equation}\nJ_\\beta = q\\Delta p^*\\left[\\frac{D}{L}\\tanh\\left(\\frac{d}{L}\\right) +S_0 + R_{SC}(\\Delta p^*)\\right]\\ .\n\\label{14}\n\\end{equation}\n\nThe current density $J_\\beta$ is inversely proportional to the energy required to create one electron-hole pair, $\\varepsilon$, which is approximately related to the bandgap $E_g$ as \\cite{Klein68}\n\\begin{equation}\n\\varepsilon = 2.8\\,E_g + 0.5\\,\\text{eV}\\ .\n\\label{10a}\n\\end{equation}\nDenoting is the current density in the case of Si ($E_g = 1.12$\\,eV) by $J_0$, the current density in the case of arbitrary bandgap can be approximated as \n\\begin{equation}\nJ_\\beta =J_0\\,Q\\cdot 3.64\\,\\text{eV}\/\\varepsilon\\ .\n\\end{equation}\nWe note that, usually, $J_0$ is in the $1$ -- $10^2\\,\\mu$A\/cm$^2$ range \\cite{Olstech}. The value of $\\Delta p^*$ found from Eq.~(\\ref{14}) should be substituted into Eq.~(\\ref{11}) to obtain the open-circuit voltage $V_{OC}$.\n\n\\begin{figure}[t!] \n\\includegraphics[scale=0.25]{fig5.EPS}\n\\caption{Open-circuit voltage as a function of the base doping level for different Shockley-Reed lifetimes in the case of (a) p-type base and (b) n-type base for $E_g$ = 1.43\\,eV, $T = 300$\\,K, and $J_0 = 10$\\,$\\mu$A\/cm$^2$.}\n\\label{fig5}\n\\end{figure}\n\nFig.~\\ref{fig5} shows the dependence of $V_{OC}$ of a GaAs-based p-n junction on the base doping level, $N_d$, neglecting the surface recombination, that is, $S_0 \\approx 0$. As seen in Fig.~\\ref{fig5}, $V_{OC}$ increases with $N_d$. On the one hand, the values of $V_{OC}$ for the pair \\Tr\/GaAs is notably smaller than in the solar cells \\cite{Sach14}, because the beta-produced current densities are at least two order of magnitude smaller than the short-circuit current densities in photovoltaic cells. On the other hand, the open-circuit voltages in Fig.~\\ref{fig5} exceed the values obtained experimentally in \\cite{And00}. The reason is that, in \\cite{And00}, the current density $J_0$ was of the order of $1\\,\\mu$A\/cm$^2$, whereas in our calculations, we have taken $J_0 = 10\\,\\mu$A\/cm$^2$. If the values $J_0 = 1\\,\\mu$A\/cm$^2$, $N_d = 5\\cdot 10^{16}$\\,cm$^{-3}$, and $\\tau_{SR} = 10^{-9}$\\,s are used, we obtain $V_{OC} = 0.44$\\,V, which practically coincides with the value given in \\cite{And00}.\n\n\n\\section{Refined calculation of the limiting betaconversion efficiency}\nAccording to Olsen \\cite{Olstech}, the efficiency of a betavoltaic element, $\\eta$, is\n\\begin{equation}\n\\eta = \\eta_\\beta\\,\\eta_C\\,\\eta_S\\ ,\n\\end{equation}\nwhere\n\\begin{equation}\n\\eta_\\beta = N_\\beta\/N_0\n\\end{equation}\nis the fraction of beta-flux that reaches the semiconductor,\n\\begin{equation}\n\\eta_C = (1 - r)\\,Q\n\\end{equation}\nis the coupling efficiency, given by the product of absorption probability of a beta-particle ($r$ is the electron reflection coefficient from the semiconductor surface) and collection efficiency $Q$ of electron-hole pairs, and, finally, the semiconductor efficiency\n\\begin{equation}\n\\eta_S = q\\,V_{OC}\\,FF\/\\varepsilon\\ ,\n\\end{equation}\nwhere $q$ is the elementary charge, $V_{OC}$ is the open-circuit voltage, $FF$ is the fill factor, $\\varepsilon$ is the energy necessary to generate one electron-hole pair from Eq.~(\\ref{10a}).\n\nLet us obtain $V_{OC}$ within the Shockley-Queisser approximation, where $\\tau_{SR} \\to \\infty$, $S_0 and R_{SC} \\to 0$, and the only recombination mechanism present is radiative recombination, characterised by the coefficient $A$. In this case, $V_{OC}^{lim}$ can be found analytically from (\\ref{10}) and (\\ref{14}):\n\\begin{equation}\nV_{OC}^{lim} = \\frac{k_BT}{q}\\ln\\frac{J_\\beta}{qAdn_i^2}\\ , \\\\\n\\label{16}\n\\end{equation}\n\nThe fill factor can be found using the expression from \\cite{Olstech}\n\\begin{equation}\nFF = \\left[v_{OC} - \\ln(v_{OC} + 0.72)\\right]\/(v_{OC} + 1)\\ ,\n\\label{18}\n\\end{equation}\nwhere $v_{OC} = V_{OC}\/k_BT$.\n\nTo calculate the limiting beta-conversion efficiency, we take $Q = 1$, $r = 0$, $\\eta_\\beta = 1$, corresponding to the bidirecional source in the terminology of \\cite{Olstech}. In this case\n\\begin{equation}\n\\eta_{lim} = \\frac{q\\,V_{OC}^{lim}\\,FF_{lim}}{2.8\\,E_g + 0.5}\\ ,\n\\label{19}\n\\end{equation}\nwhere $V_{OC}^{lim}$ is given by (\\ref{16}). \n\nWhen calculating $\\eta_{lim}$, several issues may arise. First, the parameters $N_c$, $N_v$, and $A$ are material-specific in every semiconductor. Second, when evaluating $V_{OC}^{lim}$ and $FF_{lim}$, Olsen had used, for each source, concrete current density $J_0$ of the order of $10^2\\,\\mu$A\/cm$^2$ for \\Pm\\ and $1\\,\\mu$A\/cm$^2$ for \\Tr. Finally, $V_{OC}^{lim}$ depends on the p-n junction thickness $d$. Therefore, all parameters in (\\ref{19}) must be specified. Since such key parameters as $A$, $N_c$, and $N_v$ are known only for concrete semiconductors and concrete bandgap values $E_g$, in the best-case scenario, the dependence $\\eta_{lim}(E_g)$ can be found as a set of support points for the known semiconductors with different $E_g$. Fitting this with a smooth curve might not be accurate enough.\n\nIn this work, we calculated $\\eta_{lim}$ only for the case of GaAs using Eq.~(\\ref{19}). For $A = 2\\cdot 10^{-10}\\,cm^3$\/s and $d = 10\\,\\mu$m gives for $J_0 = 10^2\\,\\mu$A\/cm$^2$ the value $\\eta_{lim} \\approx 17$\\,\\%, and for $J_0 = 1\\,\\mu$A\/cm$^2$, $\\eta_{lim} \\approx 14$\\,\\%. Note that the values of $\\eta_{lim}$ obtained here notably exceed the ones obtained by Olsen in \\cite{Olstech, Ols12}. In the rest of this work, we will use the values obtained for the \\Pm\/GaAs and \\Tr\/GaAs combinations, respectively.\n\n\\begin{figure}[t!] \n\\includegraphics[scale=0.25]{fig6.EPS}\n\\caption{Beta-conversion efficiency of a \\Tr\/GaAs pair as a function of junction depth for different Schokley-Reed lifetimes for the case of (a) p-type base and (b) n-type base.}\n\\label{fig6}\n\\end{figure}\n\n\\begin{figure}[t!] \n\\includegraphics[scale=0.25]{fig7.EPS}\n\\caption{Beta-conversion efficiency of a \\Pm\/GaAs element vs. junction depth for different Schokley-Reed lifetimes and element thcknesses for (a) p-type base and (b) n-type base.}\n\\label{fig7}\n\\end{figure}\n\n\\section{Calculation of the attainable betaconversion efficiency}\nFig.~\\ref{fig6} shows the attainable efficiency as a function of $d_p$ for the \\Tr\/GaAs combination, obtained from\n\\begin{equation}\n\\eta = \\eta_{lim}Q\\frac{V_{OC}}{V^{lim}_{OC}}\\ ,\n\\label{20}\n\\end{equation}\nwhere $\\eta_{lim} \\approx 14$\\,\\%, $Q$ is given by Eq.~(\\ref{8}), $V_{OC}$ is found from Eq.~(\\ref{11}), and $V_{OC}^{lim}$ from Eq.~(\\ref{16}).\n\nWhen plotting Fig.~\\ref{fig6}, we varied the lifetime at a constant $d = 10\\,\\mu$m. Panels (a) and (b) correspond to the base of the p- and n-type, respectively. As seen in Fig.~\\ref{fig6}, the attainable efficiency values are rather high and are in the range of (6.4 - 12.5)\\%.\n\nIt should be noted that our results for \\Tr\/GaAs pair agree well with those given in the review \\cite{Ols12} citing Refs.~\\cite{And00, Bow02, Ada12}, namely, $\\eta =$ (4 - 7) \\%. In these works, a \\Tr-source was used with the $A_3B_5$-based semiconductors. But, as evident from the figures shown, the possibilities of increasing the efficiency of \\Tr\/$A_3B_5$ betaconversion are far from being exhausted.\n\nShown in Fig.~\\ref{fig7} is the attainable beta-efficiency (\\ref{20}) as a function of $d_p$ for \\Pm\/GaAs pair with $\\eta_{lim}$ = 17\\,\\%. The $\\tau_{SR}$ values used were $10^{-9}, 10^{-8}$, and $10^{-7}$\\,s, and GaAs thicknesses were 10 and 100 $\\mu$m. Fig.~\\ref{fig7}(a) and (b) correspond to the p- and n-types of the base conductivity. As seen in this figure, $\\eta$ reduces rather strongly as $\\tau_{SR}$ is decreased. For the highest $\\tau_{SR} = 10^{-7}$\\,s, $\\eta$ decreases with decreasing $d$. The highest efficiency attainable, $\\eta = 7.25$\\,\\%, is achieved for $\\tau_{SR} = 10^{-7}$ s and $d = 100\\,\\mu$m, and the lowest value of $0.51$ \\% is realized for $\\tau_{SR} = 10^{-9}$ s and $d = 10\\,\\mu$m.\n\nThus, we conclude that a \\Pm\/GaAs-based betaconverter is not as efficient as a \\Tr\/GaAs-based one. Perhaps, the very small efficiency of the \\Pm\/GaAs battery obtained in \\cite{Fli64} is due to the small thickness of GaAs and small lifiteme $\\tau_{SR}$. The same applies also to the cases when, instead of GaAs, other direct-bandgap semiconductors are used.\n\n\\section{Conclusions}\nOur analysis, focusing on the attainable collection coefficient $Q$ and open-circuit voltage values $V_{OC}$, has revealed the following features of current collection of the GaAs-based beta-elements.\n\nEfficient collection of the electron-hole pairs generated by a beta-flux can be achieved when the diffusion length exceeds the dead layer thickness, $L > x_m$. An alternative way to increase collection coefficient is to use deep junctions, for which $d_p \\simeq x_m$.\n\nAdditional mechanisms responsible for the reduction of current generated by beta-electrons are possible, leading to smaller betaconversion efficiency. They may be due, for instance, to the strong absorption of the beta-electrons by auxiliary layers of a betavoltaic element.\n\nUsing the Shockley-Queisser approximation, we have derived the limiting betaconversion efficiency, $\\eta_{lim}(E_g)$. Our analysis has shown that, because the main parameters affecting the efficiency are very different for different semiconductors, the $\\eta_{lim}(E_g)$ curve can be build as a set of support points for semiconductors with different bandgaps, and not as a smooth curve.\n\n\\Pm\\ beta-source performs more poorly than \\Tr-source, because the electron-hole pair generation depth in the case of \\Pm-source is large, whereas the diffusion length of GaAs is small. Therefore, the majority of electron-hole pairs generated in the base recombine before reaching the p-n junction.\n\nIn the case of \\Tr-source, the picture is different. The collection coefficient is rather high, because of the small generation depth of electron-hole pairs. Therefore, the realistic betaconversion efficiency for the \\Tr\/GaAs pair will be rather high for relevant parameters (lifitemes and diffusion coefficients) of the semiconductor.\n\nSimilar results are expected also in the case, when other direct-bandgap semiconductors are used instead of GaAs.\n\n\\acknowledgments\nM.E. would like to thank Natural Sciences and Engineering Research Council of Canada (NSERC) for financial support.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nThe cost of lattice QCD simulations with dynamical fermions is dominated by the solution of the Dirac equation in both the ensemble generation phase, where configurations of gauge fields are generated, and the measurement phase, where expectation values of physical observables are measured. \nThe Dirac matrix, which is the gauge field dependent discretization of the fermionic part of the continuous QCD action, is a large sparse linear system and inverting the corresponding Dirac equation poses tremendous numerical difficulty. \nFor domain wall fermions(DWF) the conjugate gradient(CG) algorithm proves to be a stable algorithm to solve the Dirac equation but the convergence rate is limited by the condition number of the Dirac matrix, which is typically large in simulations with physical pion mass. \n\nFor the measurement phase various eigen-space methods, including EigCG\\cite{Stathopoulos2010} and implicitly restarted Lanczos algorithm with Chebyshev polynomial\\cite{YSaad1980}, have been developed successfully to speed up the inversion.\nLow-lying eigenvectors(eigenvectors corresponding to small eigenvalues) of the Dirac matrix are generated and the previously large condition number is effectively reduced to improve the convergence rate of CG.\nIn this phase for one gauge field configuration typically a large number of Dirac equations with the same Dirac matrix but different right hand sides(RHS, or sources) are solved. The large number of sources amortizes the cost of eigenvector generation and the total computation time is reduced.\n\nThis is not the case for the ensemble generation phase. During a typical hybrid Monte Carlo(HMC) evolution of a gauge field as few as one Dirac equation is solved for a single Dirac matrix. This renders it not worthwhile to generate the low-lying eigenvectors for a particular Dirac matrix.\n\nThe development of supercomputers has greatly increased the number of floating point operations per second(flops) that can be performed on each processor(node).\nModern lattice simulations usually divide the gauge field and pseudo-fermion fields into sub-fields that are stored and computed locally on different processors of a large parallel computer.\nThis increases the total theoretical floating point operation capability.\nInter-processor data transfer(communication), however, is needed to perform coherent operations, including the Dirac matrix multiplication. Computations locally performed on one processor require contents of the sub-fields that are stored and updated on other processors.\nFor a specific operation if the rate of communication could not keep up with the local flops then communication becomes the bottleneck and the high flops are not utilized. \n\nFor standard CG solver with DWF one Dirac matrix multiplication is performed for each iteration. The precise requirement varies with the size of the lattice and processor grid, but roughly this requires one byte of communication for each local floating point operation.\nOn some of the newest machines, for example the SUMMIT machine at Oak Ridge National Laboratory(ORNL), inter-processor communication speed is much less than the requirement set by their high local floating point operation capability.\n\nIn \\cite{Luscher2004} a domain decomposition algorithm is proposed for Dirac equation with Wilson fermion. Local inversions are performed on two halves of the lattice iteratively. However, attempts to apply the same or similar algorithms to the inversion of the DWF Dirac equation have not been successful. \n\n\nIn this work we report on our investigation into a preconditioned CG solver for solving the DWF Dirac equation for the ensemble generation phase of the simulation. We find a preconditioner that decreases the number of CG iterations needed for a solution, while increasing the local computation required per iteration, thus changing the balance of local computation to off-processor communication.\n\n\n\n\\section{Method}\n\\subsection{Multisplitting Algorithm}\nIn \\cite{OLeary1985} a \\textit{multisplitting} algorithm is proposed for solving generic large linear systems distributed across a parallel computer.\nCompared to the domain decomposition algorithm in \\cite{Luscher2004}, it does not require checkerboarding.\nBefore each iteration the boundary content of the solution field on each of the processors is communicated to its neighbors.\nDuring each iteration, the algorithm uses this communicated neighboring solution field as the Dirichlet boundary condition to perform the inversion of a local matrix on each processor. \nAfter each iteration, the updated boundary content is again communicated to prepare for the next iteration.\n\n\\begin{figure}[]\n\t\\centering\n\t\\includegraphics[width=0.8\\textwidth]{ms_dec.pdf}\n\t\\caption{Decomposition of the matrix $A$, the solution vector $x$ and the right-hand-side(RHS) vector $b$ into local parts on each node.}\\label{fig:ms_dec}\n\\end{figure}\nFollowing \\cite{Jezequel2012}, suppose the equation to be solved is $Ax=b$. For a \\textit{particular} processor the matrix $A$ and vectors $x$ and $b$ are decomposed according to figure \\ref{fig:ms_dec}, where $x_s$ and $b_s$ are the part that is locally stored on this processor. On each processor the original equation turns into\n\\begin{equation}\\label{local}\n\tA_sx_s+A_lx_l+A_rx_r=b_s.\n\\end{equation} \nThe $A_lx_l+A_rx_r$ part involves off-processor content and is calculated before each iteration via communication. $A_s$ is the part of the matrix that requires only the locally stored part of $x$ on a certain processor $s$, i.e. $x_s$. Then for each iteration the algorithm solves the equation \n\\begin{equation}\\label{eq:ms}\n\tA_sx_s=b_s-A_lx_l-A_rx_r\n\\end{equation}\nlocally for $x_s$ on this processor. The updated solution $x_s$ will then be communicated to the neighboring processors. This whole procedure can be done concurrently on all nodes once the communication work to calculate $A_lx_l+A_rx_r$ is done.\n\n\n\n\\subsection{Domain Wall Fermions}\nThe domain wall fermion(DWF)\\cite{Jansen1996} formulation is based on Wilson fermion and a fictitious fifth dimension. Modern numerical implementations of DWF utilize the fact that only the matrix elements that connect the \\textit{even} sites to \\textit{odd} sites and those connecting \\textit{odd} sites to \\textit{even} sites depend the gauge field. The matrix entries that connect \\textit{even} sites to \\textit{even} sites and those connect \\textit{odd} sites to \\textit{odd} sites are constant. Here the even-odd parity is defined by the 4D components of a site:\n\\begin{equation}\n\\mathrm{parity}\\equiv (x+y+z+t)\\mod 2. \n\\end{equation}\nIn the 4D even-odd preconditioning form the M\\\"obius DWF Dirac equation can be written as, \n\\begin{equation}\n \\begin{pmatrix}\n M_5 & M^4_{eo} \\\\\n M^4_{oe} & M_5 \\\\\n \\end{pmatrix}\n \\begin{pmatrix}\n \\psi_e \\\\\n \\psi_o\n \\end{pmatrix}\n =\n \\begin{pmatrix}\n \\phi_e \\\\\n \\phi_o\n \\end{pmatrix}, \n\\end{equation}\nwhere the subscript $e\/o$ refer to even and odd sites. This is equivalent to solving the following even-odd preconditioned equation,\n\\begin{equation}\\label{dirac_equation}\nD_{PC}\\psi_e=\\hat\\phi_e,\\ D_{PC}\\equiv M_5-M^4_{eo}M_5^{-1}M^4_{oe}, \\hat\\phi_e\\equiv\\phi_e-M^4_{eo}M_5^{-1}\\phi_o. \n\\end{equation}\nHere $M^4_{eo\/oe}$ includes the Wilson hopping term $D^w_{x,y}$ that connects 4D space-time sites to their nearest neighbors,\n\\begin{equation}\nM^4_{oe\/eo}=D^w_{x,y}M_\\phi,\\ D^w_{x,y}\\equiv\\sum_\\mu\\left[(1+\\gamma_\\mu)U^\\dagger_{x-\\hat{\\mu},\\mu}\\delta_{x-\\hat\\mu,y}+(1-\\gamma_\\mu)U^\\dagger_{x,\\mu}\\delta_{x+\\hat{\\mu},y}\\right],\n\\end{equation}\nand $M_5$ and $M_\\phi$ are constant matrices that are diagonal in the four Euclidean space-time dimensions. Details of these matrices can be found in \\cite{Brower2014}.\n\nThe CG algorithm requires the matrix to be hermitian and positive definite. A common practice is to multiply both sides of (\\ref{dirac_equation}) with $D^\\dagger_{PC}$ and solve the equation with the normal operator $D^\\dagger_{PC}D_{PC}$ and the new RHS $D_{PC}^\\dagger\\hat\\phi_e$ instead,\n\\begin{equation}\\label{normal_equation}\nD^\\dagger_{PC}D_{PC}\\psi_e=D_{PC}^\\dagger\\hat\\phi_e. \n\\end{equation}\n\n\\subsection{Dirichlet Boundary Condition on the 4-Hop Normal Operator}\nThere are four Wilson hopping terms, one in each $M_{eo\/oe}^4$, in the normal operator $D^\\dagger_{PC}D_{PC}$,\n\\begin{equation}\\label{eq:DdagD}\n D^\\dagger_{PC}D_{PC}=\\big[M_5-\\textcolor{red}{M^4_{eo}}M_5^{-1}\\textcolor{red}{M^4_{oe}}\\big]^\\dagger\\big[M_5-\\textcolor{red}{M^4_{eo}}M_5^{-1}\\textcolor{red}{M^4_{oe}}\\big].\n\\end{equation}\n\nTo apply the multisplitting algorithm to equation (\\ref{normal_equation}) Dirichlet boundary conditions are to be enforced on the normal operator $D^\\dagger_{PC}D_{PC}$, i.e. the local part(the $A_s$ in (\\ref{local})) of this normal operator needs to be constructed. As the vector content is distributed across the processors according to its 4D space-time location, this local part for $D^\\dagger_{PC}D_{PC}$ includes \\textit{snake} terms that hop out of the boundary and hop back in as the various components in (\\ref{eq:DdagD}) are evaluated. Figure \\ref{fig:snake} illustrates this and gives some examples of the snake terms. These terms are truncated if Dirichlet boundary conditions are enforced on each of the four $M^4_{eo\/oe}$ hopping terms sequentially. Our simulation results show that the inclusion of these snake terms is crucial to the convergence.\n\\begin{figure}[]\n\t\\centering\n\t\\includegraphics[width=0.6\\textwidth]{snake.pdf}\n\t\\caption{The normal operator $D^\\dagger_{PC}D_{PC}$ has as many as $4$ Wilson hopping terms. Enforcing Dirichlet boundary condition on it requires the inclusion of the \\textit{snake} terms, e.g. the black arrows.}\\label{fig:snake}\n\\end{figure}\n\n\\subsection{Multisplitting Algorithm as a Preconditioner of CG}\nIn \\cite{Luscher2004} to achieve faster convergence the domain decomposition algorithm is eventually used as a preconditioner of GCR. In this work we use the multisplitting algorithm as a preconditioner of CG.\n\nPseudocode for a generic preconditioned CG is shown below, where we are solving $Ax=b$ and $M$ is the preconditioning matrix. The preconditioning step is marked with blue background. The overall convergence rate of preconditioned CG is estimated by the condition number of $AM^{-1}$. If the condition number of $AM^{-1}$ is smaller then that of the original matrix $A$, faster convergence rate is achieved.\n\\begin{algorithm}\n\\setstretch{1.15}\n\\caption{Preconditioned Conjugate Gradient $Ax=b$}\n\\begin{algorithmic}\n\\State ${r}_0 = {b} - {A x}_0$\n\\State ${z}_0 = {M}^{-1} {r}_0$ \n\\State ${p}_0 = {z}_0$ \n\\State $k = 0$ \n\\While {have not converged}\n\\State $\\alpha_k = {\\langle{r}_k,{z}_k\\rangle}\/{\\langle{p}_k,{A p}_k \\rangle}$ \n\\State ${x}_{k+1} = {x}_k + \\alpha _k {p}_k$ \n\\State ${r}_{k+1} = {r}_k - \\alpha _k {A p}_k$ \n\\State \\colorbox{blue!30}{${z}_{k+1} = {M}^{-1} {r}_{k+1}$\n\\State $\\beta _k = {\\langle {z}_{k+1}, {r}_{k+1}\\rangle}\/{\\langle {z}_k,{r}_k \\rangle}$ \n\\State ${p}_{k+1} = {z}_{k+1} + \\beta _k {p}_k$ \n\\State $k = k + 1$ \n\\EndWhile\n\\end{algorithmic}\n\\end{algorithm}\n\nNow for this preconditioning step we use the multisplitting algorithm to solve for $z_{k+1}$ in\n\\begin{equation}\n Az_{k+1}=r_{k+1}.\n\\end{equation}\nTo avoid inter-processor communication, a zero initial guess($x_l=x_r=0$) is used in (\\ref{eq:ms}) and only the first iteration is performed. With $r_{k+1}$ as the RHS and $z_{k+1}$ the solution,\n\\begin{equation}\n A_s x_s = b_s -A_lx_l - A_r x_r \\rightarrow A_s z_{k+1,s} = r_{k+1,s}.\n\\end{equation}\nThis is equivalent to using the local part of the matrix $A$, $A_s$, on each processor as the preconditioner $M$ in the preconditioned CG,\n\\begin{equation}\n M=\\bigoplus_s A_s,\\ s=\\mathrm{node\\ index}.\n\\end{equation}\nThe local nature of $A_s$ makes it possible to perform the preconditioning step concurrently on all the processors without communication. We refer to this as multisplitting preconditioned CG(MSPCG).\n\n\n\\section{Results}\n\nThe multisplitting preconditioned CG is applied to solve Dirac equations on three 2+1 flavor lattice ensembles generated with M\\\"obius domain wall fermions, all with physical input quark masses. Standard CG is used to perform the inversion in the preconditioning step. Instead of adopting a precision based stopping condition, a fixed number of CG iterations, which will be referred as \\textit{inner iterations}, are performed for these preconditioning solves. The iterations performed in the overall preconditioned CG will be referred as \\textit{outer iterations}. In table \\ref{table:result} the numbers of outer iterations needed for the preconditioned CG to converge are reported on the different lattice ensembles, together with the stopping condition for the outer CG(precision) and the processor grid size used. The numbers of iterations to reach the same precision with standard CG are also included for comparison, where the inner iteration number is marked with \\textit{plain}.\n\nTypically on these ensembles with $6$ inner iterations the preconditioned CG reduces the outer iteration count by a factor of $3$. More inner iterations reduce the outer iteration count more but the reduction saturates as the inner iteration count increases: with large number of inner iterations the inner CG solves the preconditioning inversion completely and no further numerical benefit can be exploited.\n\n\\begin{table}\n\\renewcommand{\\arraystretch}{1.5}\n\\centering\n\\begin{tabular}{cccccc}\n\\hline\n\\hline\nlattice size & $a^{-1}[\\mathrm{GeV}]$ & precision & processor grid size & inner iterations & outer iterations \\\\\n\\hline\n\\hline\n\\multirow{4}{*}{$32^3\\times 64$} & \\multirow{4}{*}{$1.37$} & \\multirow{4}{*}{$10^{-8}$} & $-$ & plain & $13594$ \\\\\n&& & $2^3\\times 4$ & $3$ & $9106$ \\\\\n&& & $2^3\\times 4$ & $4$ & $6020$ \\\\\n&& & $2^3\\times 4$ & $6$ & $5126$ \\\\\n\\hline\n\\hline\n\\multirow{4}{*}{$64^3\\times 128$} & \\multirow{4}{*}{$2.36$} & \\multirow{4}{*}{$10^{-10}$} & $-$ & plain & $18092$ \\\\\n&&& $4^3\\times 8$ & $6$ & $6008$ \\\\\n&&& $4^3\\times 8$ & $12$ & $5083$ \\\\\n&&& $4^3\\times 8$ & $18$ & $4948$ \\\\\n\\hline\n\\hline\n\\multirow{2}{*}{$80^2\\times96\\times 192$} & \\multirow{2}{*}{$3.00$} & \\multirow{2}{*}{$10^{-10}$} & $-$ & plain & $16783$ \\\\\n&&& $4^2\\times 8^2$ & $6$ & $5719$ \\\\\n\\hline\n\\hline\n\\end{tabular}\n\\caption{Number of outer iterations need to converge the multisplitting preconditioned CG for the lattice ensembles tested in this work. \\textit{Inner iterations} refers to the fixed number of CG iterations performed for the preconditioning inversion. Rows marked with \\textit{plain} indicate the iteration count for the same standard CG to converge.}\\label{table:result}\n\\end{table}\n\n\\section{Conclusion}\nOur results show the MSPCG reduces the number of outer iterations needed to solve the DWF Dirac equation, reducing the inter-processor communication at the expense of performing more local inner iterations. \nWe observe that executing a fixed number of inner CG iterations for the preconditioning inversion, instead of using a precision based stopping condition, does not jeopardize the convergence of the outer CG. \nThis is true even when as few as $3$ inner iterations are performed.\nAs a consequence the inner iteration count is a parameter that can be tuned to achieve maximum speed up in the trade-off between inter-processor communication and local computation. \n\nWe note that while the multisplitting algorithm can split the general matrix $A$ in a variety of ways, the splitting presented here, used as a preconditioner in CG, makes it equivalent to the additive Schwarz algorithm. (The additive Schwarz algorithm has been used for the Dirac equation inversion for the fermions\\cite{Osaki2010, Babich2011}.) We use the name MSPCG, as it is through the process of applying the multisplitting algorithm to the DWF Dirac equation that we realize the necessity of including the snake terms in the local matrix.\n\n\n\n\\urlstyle{tt}\n\\printbibliography\n\n\\end{document}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nCoagulation processes arise in various areas of physics; one may think of\npolymerisation, growth of ordered domains in non-equilibrium magnetic systems \n\\cite{bray}, dynamics of droplets when water condenses on non-wetting \nsurfaces \\cite{dgy}, etc. \nThe substance, or ``mass'' that aggregates is very frequently not conserved\nduring the process: for example, \nagglomerating insoluble inclusions in molten metal\nmay be lost from the melt by attachment to the wall of the vessel \\cite{wmg}.\nTherefore the theoretical investigation of the kinetics of such non-conserving\ncoagulation processes is of great importance. \nMoreover, the models developed for the description of such systems may \nshow interesting behaviour: the Smoluchowsky equation with certain coagulation\nkernels exhibits gelation transition and, in general, even the simplest\nmodels with conserved mass may have non-trivial solutions, see\ne.g. Ref. \\cite{wmg} and references therein. \nBeside quite realistic ones, \nthere is a special class of (possibly non-conserving) coagulation models \nwhere only the actually smallest one among the masses is active \nwhile the other masses are temporarily inert. \nThis type of {\\it extremal dynamics} can be regarded as a rough \napproximation for models where the reaction rates are \ndecreasing functions of the mass of particles. \nIn what follows, we shall survey three processes with extremal\ndynamics in detail. \nWe mention, however, that models of this type have also been \nintroduced in the\ncontext of dynamics of growing and coalescing droplets \\cite{dgy} or\nmultispecies pair annihilation reactions \\cite{dht}. \n\nIn the one-dimensional Glauber-Ising model started from a random initial state\nat zero temperature, the domain walls move as independent random walkers and\nannihilate upon meeting. While the closest pairs of walls come together and\nannihilate, the other domain walls hardly move. A simplified model of\nevolution of distances $X_i$ between adjacent walls can be formulated as\nfollows \\cite{nagai,kon}. The shortest interval $X_m$ is eliminated\ntogether with the two adjacent intervals $X_1$ and $X_2$ and replaced by \n$\\tilde X=X_1+X_2+X_m$. \nAs the density of walls tends to zero,\nthe distributions of intervals at different times become self-similar,\ndepending on a single time-dependent length scale, and the corresponding\nscaling function can be calculated exactly \\cite{nagai,bdg,rutenberg}. \nAnother quantity of interest is the fraction of space which has never been\ntraversed by a domain wall. The length $Y_i$ of such parts of intervals\ntransforms in the way $\\tilde Y=Y_1+Y_2$ when the shortest interval is\neliminated. \nThe characteristic value of $X$ depends on\nthe fraction $c$ of the initial intervals that have not yet been eliminated \nas $X\\sim c^{-\\alpha}$, obviously, with $\\alpha=1$, while \nit has been found that $Y\\sim c^{-\\beta}$, where the persistence \nexponent $\\beta=0.824924 12\\dots$ is the zero of a \ntranscendental equation \\cite{bdg}. \nIn addition to this, the autocorrelation exponent has also been exactly\ncalculated in this model \\cite{bd}. To obtain this quantity, the overlap $Z_i$\nof an interval with its initial state that transforms as $\\tilde\nZ=Z_1+Z_2-Z_m$ had to be considered.\nLater, a generalisation of persistence has been studied in the\nsame model, which required to introduce an auxiliary variable transforming \nas $\\tilde Y=Y_1+Y_2+pY_m$ \\cite{mb}. Here, \nthe generalised persistence exponent has\nbeen found to vary monotonically with the partial survival factor $p$\nin the range $-1\\le p\\le 1$.\n\nThe next example is the strong disorder renormalisation group transformation \nof inhomogeneous quantum spin chains \\cite{mdh}. Here, the degrees of freedom\nrelated to the largest coupling (a bond between neighbouring spins or a local\nexternal field) are eliminated one after the other. \nIn terms of logarithmic couplings, $X_i$, the\nrenormalisation rule generally reads as $\\tilde X=X_1+X_2-X_m$, \nwhere $\\tilde X$ is a newly formed effective variable and $X_1$,$X_2$ \nare variables adjacent to the smallest one, $X_m$.\nFor the relation between these variables and the couplings in the particular \n Hamiltonians we refer the reader to Ref. \\cite{fisher}. \nA variable $Y_i$ that transforms according to the rule \n$\\tilde Y=Y_1+Y_2$ under such a\nrenormalisation step can be interpreted in the case of a particular model, the\ntransverse field Ising chain, as the magnetic moment of a spin. \nFor this process with i.i.d. random initial variables\n$X_i$, which corresponds to critical spin chains, the distribution of $X$\nflows again to a fixed point where it shows scaling behaviour. \nThe characteristic value of $X$ increases in the course of the process \nas $X\\sim c^{-\\alpha}$ with \n$\\alpha=1\/2$, while the variable $Y$ grows as $Y\\sim c^{-\\beta}$ with\n$\\beta=(1+\\sqrt{5})\/4=0.809016\\dots$ \\cite{fisher}. \nNote that the coagulation rules in the above two models \ndiffer only in the sign of $X_m$, \nwhich leads to different exponents $\\alpha$ and $\\beta$.\n \nOur third example is a random graph where three edges emanate from each\nnode, and which is built on a regular one-dimensional lattice by adding long\nedges in the following way. To each edge of the one-dimensional\nlattice that we call short edges, a random weight $X_i$ is assigned. \nDefining the length of a path as the sum\nof weights of the edges it contains, the closest pair of nodes of degree 2\nwith respect to this metric is chosen and connected by an edge of unit\nweight. This step is then iterated until all nodes become of degree 3 \n\\cite{juhasz}. \nFor this graph, a renormalisation procedure can be formulated \nwhere loops are eliminated \nstep by step in reversed order compared to the construction procedure. \nFormally, the short edge with the minimal weight $X_m$ is eliminated\ntogether with the nodes it connects, as well as with the \nneighbouring short edges with \nweights $X_1$, $X_2$ and a new effective short edge is formed with a weight\ncalculated asymptotically as $\\tilde X=X_1+X_2$. \nAccording to numerical results, \nthe characteristic value of effective weights grows as \n$X\\sim c^{-\\alpha}$ with $\\alpha=0.826(1)$ \\cite{juhasz}.\nThis exponent characterises at the same time the \ndiameter of finite graphs with $N$ nodes with respect \nto the above metric via $D(N)\\sim N^{\\alpha}$. \n\nAs can be seen, these seemingly different problems can be treated in a common\nframework and can be interpreted as coagulation\nprocesses with extremal dynamics. \nIn the first example, the total sum of the variables $X_i$ is\nconserved while in the latter two cases it is not. \nWe will study in this work a coagulation model \ncontrolled by a parameter $\\omega$ that interpolates \ncontinuously between the first two models and incorporates the third one as a\nspecial case, as well. \nWe are interested in the exponents $\\alpha_{\\omega}$ and $\\beta_{\\omega}$\nfor intermediate values of the parameter $\\omega$ and shall\nprovide accurate estimates for $\\alpha_{\\omega}$ that is obtained \nas the root of a \ntranscendental equation while $\\beta_{\\omega}$ is accurately determined \nby the numerical analysis of a system of non-linear differential equations. \nWe shall see that $\\alpha_{\\omega}$ varies monotonically between \nthe corresponding values of\nthe two marginal models, while, \nunlike the generalised persistence exponent of the model with\npartial survival mentioned above \\cite{mb}, the exponent $\\beta_{\\omega}$\nshows a maximum when $\\omega$ is varied. \nAs can be seen, the transformation rule of the variable \n$Y$ does not depend directly on the\nparameter $\\omega$ but it is influenced indirectly via the correlations \nemerging between $X$ and $Y$, the strength of which is controlled\nby $\\omega$. Therefore our results may contribute to the\nunderstanding of the role of correlations in such models. \nMoreover, these investigations provide an accurate estimate for the diameter\nexponent of the graph quoted above, for which we obtain $\\alpha=0.82617561$ in\nagreement with the previous numerical result. \n\nThe rest of the paper is organised as follows. In Section \\ref{model},\nthe model and its continuum description is introduced. \nIn Sections \\ref{asec} and \\ref{bsec}, the way of approximative\ndetermination of the exponents $\\alpha_{\\omega}$ and $\\beta_{\\omega}$\nis presented. Some calculations are given in the Appendix. Finally,\nresults are discussed in Section \\ref{disc}. \n\n\\section{The model and its continuum formulation}\n\\label{model}\n\n\\subsection{Definition of the model}\n\nLet us consider a finite set of positive \nvectors $V_i=(X_i,Y_i)$ indexed by the\nintegers $i=1,2,\\dots,N$. We assume, moreover, that $N$ is odd. \nThe vectors are independent, identically distributed random variables \ndrawn from a continuous distribution $\\rho(X,Y)dXdY$, for which we require \nthat all moments exist.\nThe first components $X_i$ and the second components $Y_i$ are called\nprimary and secondary variables, respectively. \nAssume, furthermore, that $\\omega\\in [-1,1]$ is a fixed real number. \nNow, the following procedure is considered on this set. \nThe vector $V_m$ with the smallest\nprimary variable is chosen and, at the same time, two further vectors $V_i$ and\n$V_j$ are chosen at random from the set. These three vectors are removed\nand a new vector $\\tilde V$ with components\n\\begin{eqnarray}\n\\tilde X=X_i+X_j+\\omega X_m \\nonumber \\\\\n\\tilde Y=Y_i+Y_j \n\\label{rules}\n\\end{eqnarray}\nis added to the set.\nThereby the number of vectors in the set is reduced by\ntwo. Note that the vectors remain independent after such an operation and\nthat \n\\begin{equation}\n\\tilde X\\ge X_i,X_j,X_m\n\\label{ineq}\n\\end{equation}\neven for $\\omega=-1$. \nThis step is then iterated until a single vector $V_N=(X_N,Y_N)$ is left in\nthe set.\nIn this general formulation, the cases $\\omega=1,-1,0$ correspond to\nthe three models in the order as they were quoted in the Introduction. \nBased on the known asymptotical behaviour of $X_N$ and $Y_N$ for \nlarge $N$ in the marginal cases $\\omega=-1,1$, \nwe expect\n\\begin{equation}\nX_N\\sim N^{\\alpha_{\\omega}} \\quad {\\rm and} \\quad Y_N\\sim N^{\\beta_{\\omega}}\n\\label{powerlaw}\n\\end{equation} \nto hold also for intermediate parameter values $-1<\\omega<1$\nwith some exponents $\\alpha_{\\omega}$ and $\\beta_{\\omega}$ that may \ndepend on $\\omega$. \n\n\\subsection{Continuum formulation}\n\nNow, we consider the continuum limit $N\\to\\infty$ and introduce \nthe probability density $P_{\\Gamma}(X)$ of the primary variable \nthat has the support \n$\\Gamma\\le X<\\infty$ and that depends on the lower boundary \n$\\Gamma$ as a parameter. \nThe function $P_{\\Gamma}(X)$ is normalised as $\\int_{\\Gamma}^{\\infty}P_{\\Gamma}(X)dX=1$ for any $\\Gamma$.\nFollowing Ref. \\cite{bdg}, we consider, furthermore, the expected value \n$\\overline{Y}_{\\Gamma}(X)$ of the secondary\nvariable under the condition that the primary variable is $X$. \nIn the continuum limit, the system is described by these two\nfunctions of $X$, which depend on the lower boundary of the support \n$\\Gamma$ as a parameter.\nThe inequality (\\ref{ineq}) implies that,\nas the fraction of vectors $c_{\\Gamma}$ that have not yet been eliminated decreases in\nthe course of the coagulation process, the lower edge $\\Gamma$ \nof the distribution continuously increases. \nAs it is shown in the Appendix, one may write \nthe following differential equation for $P_{\\Gamma}(X)$:\n\\begin{equation}\n\\frac{\\partial P_{\\Gamma}(X)}{\\partial \\Gamma}=P_{\\Gamma}(\\Gamma)\n\\Theta[X-(2+\\omega)\\Gamma]\\int_{\\Gamma}^{X-(1+\\omega)\\Gamma}P_{\\Gamma}(X')P_{\\Gamma}(X-X'-\\omega\\Gamma)dX',\n\\label{Pdiff}\n\\end{equation}\nwhere $\\Theta(X)$ is the Heaviside step function. \nThe fraction $c_{\\Gamma}$ \nis related to $\\Gamma$ as \n$dc_{\\Gamma}\/c_{\\Gamma}=-2P_{\\Gamma}(\\Gamma)d\\Gamma$ or, equivalently,\n\\begin{equation} \n\\frac{dc_{\\Gamma}}{d\\Gamma}=-2P_{\\Gamma}(\\Gamma)c_{\\Gamma}.\n\\label{cdiff}\n\\end{equation}\nThe function $Q_{\\Gamma}(X)$ defined as \n\\begin{equation}\nQ_{\\Gamma}(X)\\equiv P_{\\Gamma}(X)\\overline{Y}_{\\Gamma}(X),\n\\label{Qfunc}\n\\end{equation}\ncan be shown to obey the differential equation \n\\begin{equation}\n\\frac{\\partial Q_{\\Gamma}(X)}{\\partial \\Gamma}=2P_{\\Gamma}(\\Gamma)\\Theta[X-(2+\\omega)\\Gamma]\\int_{\\Gamma}^{X-(1+\\omega)\\Gamma}Q_{\\Gamma}(X')P_{\\Gamma}(X-X'-\\omega\\Gamma)dX'.\n\\label{Qdiff}\n\\end{equation} \nThe derivation of this equation is given again in the Appendix. \n\n\\subsection{Fixed point solution}\n\nIn the marginal cases $\\omega=-1,1$, it is known that, for any well-behaving\ninitial distributions $\\rho(X,Y)$ with finite moments, the \nsolutions of Eqs. (\\ref{Pdiff}) and (\\ref{Qdiff}) tend to a universal \nfixed point solution $P^*_{\\Gamma}(X)$, $Q^*_{\\Gamma}(Y)$ \nin the limit $\\Gamma\\to\\infty$ that has the scaling property \n\\begin{eqnarray} \nP^*_{\\Gamma}(X)=\\Gamma^{-1}f(X\/\\Gamma) \\nonumber \\\\\nQ^*_{\\Gamma}(X)=\\Gamma^{\\delta_{\\omega}-1}g(X\/\\Gamma),\n\\label{fp} \n\\end{eqnarray}\nwith some number $\\delta_{\\omega}$ that is related to the growth\nexponents as\\footnote{This can be seen from the equation \n$\\overline{Y}^*_{\\Gamma}(\\Gamma)\\equiv\nQ^*_{\\Gamma}(\\Gamma)\/P^*_{\\Gamma}(\\Gamma)=\\Gamma^{\\delta_{\\omega}}g(1)\/f(1)$\nthat indicates the asymptotical relation $Y\\sim X^{\\delta_{\\omega}}$ between the\ntypical values of primary and secondary variables.} \n\\begin{equation} \n\\delta_{\\omega}=\\beta_{\\omega}\/\\alpha_{\\omega}.\n\\label{delta}\n\\end{equation} \nTherefore we expect this to hold also for intermediate parameter values \n$-1<\\omega<1$ with some (a priori unknown) exponent $\\delta_{\\omega}$ \nthat may depend on $\\omega$. \nIndeed, the functions in Eq. (\\ref{fp}) solve Eqs. (\\ref{Pdiff}) and\n(\\ref{Qdiff}) provided that the universal \nscaling functions $f(x)$ and $g(x)$\nsatisfy the following differential equations:\n\\begin{eqnarray}\n\\frac{d[xf(x)]}{dx}=-f_1\\Theta(x-2-\\omega)\\int_1^{x-1-\\omega}f(x')f(x-x'-\\omega)dx'\n\\label{fdiff}\n\\\\\n\\frac{d[x^{1-\\delta_{\\omega}}g(x)]}{dx}x^{\\delta_{\\omega}}=-2f_1\\Theta(x-2-\\omega)\\int_1^{x-1-\\omega}g(x')f(x-x'-\\omega)dx',\n\\label{gdiff}\n\\end{eqnarray}\nwhere the notation $f_1\\equiv f(1)$ has been used. \nFor an alternative derivation of these equations in the case $\\omega=1$, \nsee Ref. \\cite{bdg}.\nUsing the fixed point solution, Eq. (\\ref{cdiff}) can be integrated yielding\nthe asymptotic relation in the large $\\Gamma$ limit: \n\\begin{equation} \n\\Gamma\\sim c_{\\Gamma}^{-\\frac{1}{2f_1}}.\n\\end{equation} \nComparing this with Eq. (\\ref{powerlaw}), we obtain the relation:\n\\begin{equation} \n\\alpha_{\\omega}=\\frac{1}{2f_1}.\n\\label{exprel}\n\\end{equation}\n\n\\section{Approximative determination of $\\alpha_{\\omega}$} \n\\label{asec}\n\nAs can be seen, Eq. (\\ref{fdiff}) does not contain $g(x)$ and together\nwith Eq. (\\ref{exprel}) it constitutes an autonomous problem for the calculation of the exponent $\\alpha_{\\omega}$. \nFor the special case $\\omega=-1$, the solution of Eq. (\\ref{fdiff}) is of\nsimple form: $f(x)=e^{-x+1}$; this yields $\\alpha_{-1}=\\frac{1}{2}$. \nIn the other marginal case, $\\omega=1$, \nthe Laplace transform of the solution is\nknown \\cite{nagai,rutenberg} and $\\alpha_{1}=1$. \nIn the case $-1<\\omega<1$, where Eq. (\\ref{fdiff}) is not soluble, we shall\nconstruct an approximative solution that enables us to give an \naccurate estimate\nof $\\alpha_{\\omega}$. An alternative way related to the numerical analysis of\nthe Laplace transforms is presented in the next section. \n\nSome properties of the scaling function $f(x)$ can be easily established\nby investigating Eq. (\\ref{fdiff}) without knowing the exact solution.\nApparently, the r.h.s. of Eq. (\\ref{fdiff}) and, as a consequence, \n$f(x)$ is non-analytical at $x=x_1\\equiv 2+\\omega$. \nBut, as $f(x)$ itself appears on the r.h.s. as a convolution with a shifted\nargument $x-1-\\omega$, the r.h.s. as well as $f(x)$ \nmust be non-analytical also at \n$x=x_2\\equiv x_1+1+\\omega$. Iterating this argument, it turns out that there\nare infinitely many points where $f(x)$ is non-analytical.\nTo be precise, one can show by recursion that the \n$2n$th derivative of $f(x)$ is discontinuous at\\footnote{For a more direct way to this result in the case $\\omega=1$, where the explicit\nform of the scaling function $f(x)$ is available, see Ref. \\cite{rutenberg}.} \n\\begin{equation} \nx_n=1+(1+\\omega)n, \\qquad n=0,1,2,\\dots.\n\\end{equation} \nFurthermore, the function value of $f(x)$ at some $x'$ is determined \nby $f(x)$ in the restricted domain $(1,x'-1-\\omega)$. \nDue to this property, $f(x)$ can be constructed \nin the intervals $[x_n,x_{n+1}]$ step by step \nstarting with $n=0$. However, the solution is more and more complicated for\nincreasing $n$ as it contains multiple integrals that \ncannot be evaluated analytically. \nIn the domains $[x_n,x_{n+1}]$, $n=1,2,\\dots$, the function $f(x)$ can be written in the following form:\n\\begin{equation} \nf(x)=\\frac{1}{x}\\sum_{i=0}^nf_1^{2i+1}C^{(2i+1)}_{\\omega}(x),\n\\quad x_n\\le x\\le x_{n+1}, \n\\label{series}\n\\end{equation}\nwhereas $f(x)=0$ if $x0$) is the root of the\nfollowing transcendental equation: \n\\begin{eqnarray} \n\\sum_{i=0}^{n-1}[f_1^{(n)}]^{2i+1}C^{(2i+1)}_{\\omega}(x_n)+ \\nonumber \\\\\n+\\frac{x_n}{x_n+\\omega}\n\\left[1-\\sum_{i=0}^{n-1}[f_1^{(n)}]^{2i+1}N^{(2i+1)}_{\\omega}(x_n)\\right]\n\\ln\\left[f_1^{(n)}-\\sum_{i=0}^{n-1}[f_1^{(n)}]^{2i+2}N^{(2i+1)}_{\\omega}(x_n)\\right]=0,\n\\nonumber \\\\\n\\label{trans}\n\\end{eqnarray}\nwhere the function $N_{\\omega}^{(2i+1)}(x)$ has been introduced as\n\\begin{equation} \nN_{\\omega}^{(2i+1)}(x)\\equiv\\int_{x_i}^{x}\\frac{C^{(2i+1)}_{\\omega}(x')}{x'}dx'.\\label{nint}\n\\end{equation}\nWe have numerically calculated the root of Eq. (\\ref{trans}) \nand the $n$th approximant $\\alpha_{\\omega}^{(n)}$ of $\\alpha_{\\omega}$ \nby using Eq. (\\ref{exprel}) for $n=1,2,3$ and\nfor several values of $\\omega$. This has necessitated \nthe numerical evaluation of\nthe integrals in Eq. (\\ref{nint}) for $n>1$. \nResults are shown in Fig. \\ref{fig2} and some numerical values are given in\nTable I. As can be seen, the approximants $\\alpha_{\\omega}^{(n)}$ converge\nrapidly with increasing $n$ and they increase monotonically with $\\omega$. \nThe best estimate for the diameter exponent of the graph cited in the\nIntroduction is $\\alpha_0^{(3)}=0.82617561$. \n\\begin{figure}[h]\n\\includegraphics[width=0.6\\linewidth]{fig2.ps}\n\\caption{\\label{fig2} The third approximant $\\alpha_{\\omega}^{(3)}$ \nof the exponent $\\alpha_{\\omega}$ plotted against $\\omega$.}\n\\end{figure}\n\n\n\\begin{table}[h]\n\\begin{center}\n\\begin{tabular}{|r||c|c|c|c|c|}\n\\hline $\\omega$ & $\\alpha_{\\omega}^{(1)}$ &$\\alpha_{\\omega}^{(2)}$\n&$\\alpha_{\\omega}^{(3)}$ & $\\delta_{\\omega}$ \n& $\\beta_{\\omega}=\\delta_{\\omega}\\alpha_{\\omega}^{(3)}$\\\\\n\\hline\n\\hline -0.9 & 0.54752760 & 0.54752815 & 0.54752815 & 1.48973578 & 0.81567227\\\\\n\\hline -0.8 & 0.59036797 & 0.59037862 & 0.59037860 & 1.38841226 & 0.81968889\\\\\n\\hline -0.7 & 0.62906729 & 0.62911723 & 0.62911708 & 1.30687751 & 0.82217896\\\\\n\\hline -0.6 & 0.66421085 & 0.66434418 & 0.66434376 & 1.23995279 & 0.82375490\\\\\n\\hline -0.5 & 0.69632781 & 0.69659263 & 0.69659189 & 1.18399594 & 0.82476197\\\\\n\\hline -0.4 & 0.72586754 & 0.72630756 & 0.72630667 & 1.13643762 & 0.82540221\\\\\n\\hline -0.3 & 0.75320237 & 0.75385266 & 0.75385199 & 1.09543864 & 0.82579860\\\\\n\\hline -0.2 & 0.77863838 & 0.77952410 & 0.77952421 & 1.05965756 & 0.82602872\\\\\n\\hline -0.1 & 0.80242716 & 0.80356405 & 0.80356560 & 1.02809664 & 0.82614309\\\\\n\\hline 0.0 & 0.82477635 & 0.82617193 & 0.82617561 & 0.99999999 & 0.82617561\\\\\n\\hline 0.1 & 0.84585830 & 0.84751339 & 0.84751989 & 0.97478484 & 0.82614953\\\\\n\\hline 0.2 & 0.86581708 & 0.86772721 & 0.86773715 & 0.95199472 & 0.82608118\\\\\n\\hline 0.3 & 0.88477397 & 0.88693068 & 0.88694457 & 0.93126697 & 0.82598219\\\\\n\\hline 0.4 & 0.90283179 & 0.90522369 & 0.90524198 & 0.91230963 & 0.82586098\\\\\n\\hline 0.5 & 0.92007834 & 0.92269199 & 0.92271501 & 0.89488491 & 0.82572374\\\\\n\\hline 0.6 & 0.93658910 & 0.93940970 & 0.93943769 & 0.87879701 & 0.82557503\\\\\n\\hline 0.7 & 0.95242937 & 0.95544130 & 0.95547441 & 0.86388324 & 0.82541834\\\\\n\\hline 0.8 & 0.96765597 & 0.97084323 & 0.97088154 & 0.85000684 & 0.82525595\\\\\n\\hline 0.9 & 0.98231868 & 0.98566518 & 0.98570869 & 0.83705175 & 0.82508918\\\\\n\\hline 1.0 & 0.99646128 & 0.99995110 & 0.99999976 & 0.82492447 & 0.82492427\\\\\n\\hline\n\\end{tabular}\n\\end{center}\n\\caption{\\label{table1} Approximants of the exponents $\\alpha_{\\omega}$, \n$\\delta_{\\omega}$ and $\\beta_{\\omega}$ for different values of $\\omega$.} \n\\end{table}\n\n\n\\section{Numerical determination of $\\beta_{\\omega}$} \n\\label{bsec}\n\nNext, we turn to the determination of the exponent $\\delta_{\\omega}$ (and, at\nthe same time, $\\beta_{\\omega}$ through Eq. (\\ref{delta})), which\nrequires the analysis of the full problem, i.e. the system of differential\nequations (\\ref{fdiff}) and (\\ref{gdiff}). \nPrior to this, \na few remarks concerning the scaling function $g(x)$ are in order. \nFirst, as a consequence of the definition in Eq. (\\ref{Qfunc}), $g(x)$ apparently \ninherits the singularity properties of $f(x)$ discussed in the previous\nsection. Furthermore, it can be written in a form analogous to\nEq. (\\ref{series}). In the domain $[x_0,x_1]$, it has a simple form:\n\\begin{equation} \ng(x)=g(1)x^{\\delta_{\\omega}-1}, \\qquad x_0\\le x\\le x_1.\n\\label{power}\n\\end{equation}\nSecond, the differential equation (\\ref{gdiff}) gives the scaling function\n$g(x)$ only up to a multiplicative constant. \nThis non-universal constant depends\non the initial distribution $\\rho(X,Y)$ and it is fixed in a \nnon-trivial way by the original equations (\\ref{Pdiff}) and \n(\\ref{Qdiff}) that are valid for any $\\Gamma$. \nThird, the equation (\\ref{gdiff}) contains the a priori unknown \nparameter $\\delta_{\\omega}$ that must be fixed by physical\nconsiderations about the solution that depends on $\\delta_{\\omega}$.\nNamely, the physically acceptable solution must be nonnegative and must have\nthe only reasonable asymptotics allowed by Eq. (\\ref{gdiff}): \n\\begin{equation}\ng_{\\infty}(x)\\simeq const\\cdot xe^{-ax},\n\\end{equation}\nwhere the number $a$ is the same as that appears in Eq. (\\ref{asymp}). \nNumerical analysis of Eq. (\\ref{gdiff}) shows that these requirements are\nfulfilled only for a single value of the parameter $\\delta_{\\omega}$. \n\nFollowing Ref. \\cite{bdg}, it is, however, simpler to analyse \nthe Laplace transform of\nthe equations (\\ref{fdiff}) and (\\ref{gdiff}). \nIntroducing the functions \n\\begin{equation}\n\\phi(p)=\\int_1^{\\infty}e^{-px}f(x)dx, \\qquad \n\\psi(p)=\\int_1^{\\infty}e^{-px}g(x)dx,\n\\label{laplace}\n\\end{equation}\nthe equations (\\ref{fdiff}) and (\\ref{gdiff}) transform to \n\\begin{eqnarray}\np\\phi'(p)=f_1[e^{-\\omega p}\\phi^2(p)-e^{-p}], \n\\label{phidiff} \\\\\np\\psi'(p)=-\\delta_{\\omega}\\psi(p) -g_1e^{-p} + 2f_1e^{-\\omega p}\\psi(p)\\phi(p),\n\\label{psidiff}\n\\end{eqnarray}\nwhere the prime denotes derivation by $p$ and $g_1\\equiv g(1)$. \nThese equations are not soluble in the parameter range $-1<\\omega<1$ but\nasymptotical expressions of the solution can be established. \nThe functions $\\phi(p)$ and $\\psi(p)$ have the small-$p$ expansions:\n\\begin{equation} \n\\phi(p)=\\sum_{n=0}^{\\infty}a_np^n, \\qquad \n\\psi(p)=g_1\\sum_{n=0}^{\\infty}b_np^n. \n\\label{psiseries}\n\\end{equation}\nSubstituting these into Eqs. (\\ref{phidiff}) and (\\ref{psidiff}), we obtain \nthat the expansion coefficients for $-1<\\omega<1$ are given by \n$a_0=1$, $b_0=\\frac{1}{2f_1-\\delta_{\\omega}}$ and by the following recursion\nrelations for $n>0$\\footnote{These series expansions are also valid for $\\omega=-1$ with \n$a_2=5\/2$, and for $\\omega=1$ with $a_1=-2e^{\\gamma}$ \\cite{bdg}, where $\\gamma$ is Euler's constant, given by $\\gamma=-\\int_0^{\\infty}\\ln t e^{-t}dt=0.577215\\dots$.}: \n\\begin{eqnarray}\na_n=\\frac{\\frac{(-1)^n}{n!}(\\omega^n-1) + \n\\sum_{0\\le i,j,k1$. \nIn that case, the growth of the primary variable becomes super-linear, meaning\nthat $\\alpha_{\\omega}>1$. \n\nAn intriguing feature of the process studied in this work is the universality\nwith respect to the initial distribution of the variables:\nFor a fixed $\\omega$,\nany sufficiently rapidly decaying initial distribution tends \nat late times to a\nuniversal distribution that displays scaling.\nAlthough, the process is universal in this sense, we have pointed out that\nit is sensitive to the variations of the reaction rules parameterised by \n$\\omega$. \nThe dependence of $\\alpha_{\\omega}$ on $\\omega$ is obvious since the\ntransformation rule of the primary variable contains $\\omega$ explicitely.\nThe growth of the secondary variable is, however, \naffected by $\\omega$ in a more subtle way. \nFocusing on the secondary variables, the difference to the process\nof primary variables with $\\omega=0$ \nis that, here, not exactly the smallest variable is\nremoved from the set. \nThis is the reason for \nthat $\\beta_{\\omega}$ is unequal to $\\alpha_0$ for $\\omega\\neq 0$. \nNevertheless, for any $\\omega$, the removed secondary variable\nis typically relatively small since $X_i$ and $Y_i$ become\npositively correlated in the course of the process. \nDue to these correlations, the strength of which is controlled by \n$\\omega$, the variation of $\\beta_{\\omega}$ is relatively slight. \nIndeed, it is by an order of magnitude\nsmaller than that of $\\alpha_{\\omega}$.\n\nFor $\\omega=0$, we have shown that $\\alpha_0=\\beta_0$ even if the primary and\nthe secondary variables are initially not perfectly correlated. This can be\nunderstood also on a microscopic level since, in this case, \nthe vectors in the set are sums of an increasing number of \ninitial vectors. Thus, the ratios $\\tilde X_i\/\\tilde Y_i$ tend\nstochastically to a common constant in the limit $\\Gamma\\to\\infty$ \nfor all $i$. In words, the two types of variables become asymptotically\nperfectly correlated for $\\omega=0$. \nNow, we are in a position to understand why the exponent $\\beta_{\\omega}$ \nis maximal at $\\omega=0$.\nAt that point, the correlations are (at least asymptotically) perfect and\nalmost always the smallest one among the secondary variables is removed. \nFor $\\omega\\neq 0$, however, the correlations are no longer perfect and, \nas a consequence, not strictly the smallest secondary variables are eliminated.\nTherefore the fastest growth of $Y$ is realized at $\\omega=0$.\n\nIn a general aspect, the benefit of the analysis carried out in this\nwork is that\nthe numerical technique developed here for obtaining accurate estimates of \nthe growth exponents may also apply to other\nnon-soluble coagulation processes with extremal dynamics. \n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nOne of the challenging problems in quantum gravity is to understand the\nmicroscopic properties of black holes, in particular, the statistical\norigin of the Bekenstein-Hawking entropy.\nNew idea for an explanation of the \norigin of the Bekenstein-Hawking entropy has been provided by recent\ndevelopment in our understanding of non-perturbative superstring theory.\nIt is based on the D-brane description of black\nholes\\cite{Strominger-Vafa} and the AdS\/CFT \ncorrespondence\\cite{Maldacena1,Witten,GKP}.\nThese are much related with each other under\nthe Maldacena duality\\cite{Maldacena1}.\n\nThree-dimensional Einstein equation with negative\ncosmological constant has the solutions called \nthe BTZ black holes\\cite{BTZ1,BTZ2}.\nThese black holes have locally ${\\rm AdS}_{3}$ geometry.\nVia the ${\\rm AdS}_{3}\/{\\rm CFT_{2}}$ correspondence, one can also \nhope to be able to analyze the microscopic properties of \nthe BTZ black holes based on a local field theory on\nthe boundary. Infinite dimensional algebra of two-dimensional conformal\nsymmetry, that is, the Virasoro algebra, provides an important clue\nfor our understanding the correspondence and the Maldacena duality.\nThe pioneering work is Strominger's counting of microscopic states\nof the BTZ black holes\\cite{Strominger}.\nBut the qualitative aspects of this counting still remain obscure .\n\nIn this paper, we will discuss the three-dimensional extremal BTZ \nblack holes in the context of the Maldacena duality.\nAlthough this duality has been conjectural yet, various checks have been\ncarried out. (See \\cite{Maldacena2} and references therein.)\nIn this perspective, the extremal BTZ black holes can be identified\nwith the primary states which are 1\/2 BPS states in the N=(4,4)\ntwo-dimensional supersymmetric $\\sigma$-model.\nThis $\\sigma$-model has a quantity called elliptic genus \nconvenient to count the degeneracy of these states.\nWe explicitly count the microscopic states of the extremal BTZ black\nholes with this identification by using the elliptic genus and\nthe unitary representation theory of the N=4 superconformal\nalgebra. The microscopic entropy of these black holes obtained by\nthis counting agrees with the entropy \\'a la Bekenstein-Hawking.\n\nThis paper is organized as follows.\nIn section 2, we will summarize the\nprevious results about the BTZ black holes from the perspective of the\n${\\rm AdS}_{3}\/{\\rm CFT}_{2}$ correspondence in a pure quantum gravity and\nin non-perturbative superstring theory, i.e., the Maldacena duality.\nIn section 3, after a brief introduction of N=4 superconformal algebra,\nblack hole states are discussed in the unitary representation\ntheory.\nIn section 4, some facts about the elliptic\ngenus of the N=(4,4) supersymmetric $\\sigma$-model are reviewed.\nIn section 5, we count the number of 1\/8 BPS states in the D1-D5 brane\nsystem in IIB supergravity via the elliptic genus of this\n$\\sigma$-model and then finally count the microscopic states of the\nextremal BTZ black holes.\nIn section 6, some other related topics are discussed.\n \n\n\\section{BTZ black holes and ${\\bf AdS_{3}\/CFT_{2}}$\ncorrespondence}\n\n\\subsection{BTZ black holes in a three-dimensional pure quantum \ngravity}\n\nThe BTZ black holes\n\\footnote{Exact solutions of the vacuum \nEinstein equation with a negative cosmological \nconstant $\\Lambda=-1\/l^2$.}\nare three-dimensional black holes specified \nby their mass $M$ and angular momenta $J$, where $|J| \\leq Ml$. \nIn terms of the Schwarzschild coordinates \n$(t,\\phi,r)$, with the ranges \n$-\\infty|J|$, it is called non-extremal.\nAnd in the case of $J = 0$ and $Ml = -l\/8 G$,\nthe geometry corresponds to the global ${\\rm AdS}_{3}$.\n\nThe outer horizon of these solutions has finite area.\nThe semiclassical argument leads to\nthe finite Bekenstein-Hawking entropy:\n\\begin{eqnarray}\nS \\equiv \\frac{A}{4 G} = \\frac{2 \\pi r_{+}}{4G}. \\qquad (A : {\\rm area\n\\ of \\ the \\ outer \\ horizon}) \\label{eq:entropy}\n\\end{eqnarray}\n\n\nQuantization of three-dimensional\npure gravity with negative cosmological constant is discussed in \n\\cite{Nakatsu}.\nIt is prescribed, through the detailed analysis of \nBrown-Henneaux's asymptotic Virasoro symmetry\\cite{Brown-Henneaux}, \nas the geometric quantization of the Virasoro coadjoint orbits\nof the Virasoro central charge $c=3 l\/2 G$.\n\nThe BTZ black holes and the ${\\rm AdS}_{3}$ correspond to the primary\nstates (highest weight states) \nof the Virasoro algebra of \nBrown-Henneaux\\footnote{Strictly speaking,\nthese states correspond to the geometry of the exterior of outer\nhorizon of the BTZ black holes and the geometry without the origin of \nthe ${\\rm AdS_{3}}$ respectively}:\n\\begin{eqnarray}\n{\\rm BTZ}_{(J, M)} &\\Longleftrightarrow& |J, M\\rangle \\equiv \n|h\\rangle \\otimes |\\tilde{h}\\rangle, \\label{eq:pribtz}\\\\ \n{\\rm AdS}_{3} &\\Longleftrightarrow& |vac\\rangle\n\\equiv |0\\rangle\\otimes|0\\rangle, \n\\label{eq:priads}\n\\end{eqnarray}\nwhere\n\\begin{eqnarray}\nh = \\frac{1}{16Gl}(r_{+}+r_{-})^2 + \\frac{c}{24}, \\quad {\\tilde h} = \n\\frac{1}{16Gl}(r_{+}-r_{-})^2 + \\frac{c}{24}.\n\\end{eqnarray}\nThe extremal BTZ black holes correspond to\n\\begin{eqnarray}\n{\\rm BTZ}_{(Ml, M)} \\Longleftrightarrow |Ml, M\\rangle \\equiv \n|h \\rangle \\otimes |\\frac{c}{24} \\rangle. \\label{eq:priext}\n\\end{eqnarray}\n \nThe total Hilbert space of the theory, which includes \nexcited states (secondary states), \nis obtained by the tensor products ${\\cal V}_{h}\\otimes{\\tilde {\\cal\nV}_{{\\tilde h}}}$ of the Verma modules of the Virasoro algebra.\n(${\\cal V}_{h}$ and ${\\tilde {\\cal V}}_{{\\tilde h}}$ are respectively\nthe Verma modules of the left-moving and right-moving sectors.) \nThese Verma modules constitute the unitary irreducible \nrepresentations of the Virasoro algebra. \nWe can identify the states excited by $L_{-n}$ in the Verma module\nwith massive gravitons on the corresponding background geometry.\n\nIn view of the ${\\rm AdS}_{3}\/{\\rm CFT}_{2}$ correspondence,\nthis Hilbert space should be realized by the corresponding boundary CFT. \nIn fact it was done\\cite{Nakatsu} based on \nthe Liouville field $X$ with a specific\nbackground charge. The action is given by\n\\begin{eqnarray}\nS[X]=\\frac1{4\\pi i}\\int_{{\\bf P}^1} \n\\bar{\\partial} X \\wedge \\partial X\n + \\frac{\\alpha_0}{2\\pi}\\int_{{\\bf P}^1} RX, \n\\qquad \\left( \\alpha_0 \\equiv \\sqrt{\\frac{l}{8G}} \\right)\n\\end{eqnarray}\nwhere $R$ is the Riemann tensor of \na fixed K\\\"ahler metric on ${\\bf P}^1$.\nThe stress tensor $T(z)$ has the form\n\\begin{eqnarray}\nT(z)=-\\frac{1}{2} \n\\partial X \\partial X(z)+\\alpha_0\\partial^2 X(z), \n\\end{eqnarray}\nand provides the generators \nof the Virasoro algebra \nwith the central charge $1+12\\alpha_0^2 = 1+3l\/2G$.\nThis central charge is the same as that of Virasoro algebra of \nBrown-Henneaux in the semiclassical limit, i.e., $l\/G \\gg 1$.\nThe Fock space ${\\cal F}_{k}$\\footnote{Similar\narguments hold for the anti-holomorphic (right-moving) part.} is built on \nthe Fock vacuum $|k\\rangle$, which is introduced as \nthe state obtained from the ordinary $SL_{2}({\\bf C})$-invariant vacuum \n$|0\\rangle$ by the relation\n$|k\\rangle = \\lim_{z\\rightarrow 0} e^{i k X(z)}|0\\rangle$.\n\nThe BTZ black hole states (\\ref{eq:pribtz}) can be \nidentified with the following Fock vacuum:\n\\begin{eqnarray}\n{\\rm BTZ}_{(J, M)} \\Longleftrightarrow |J, M\\rangle \\equiv \n |k_{(J, M)}\\rangle \\otimes |\\tilde{k}_{(J, M)}\\rangle, \n\\label{black hole state in 2d}\n\\end{eqnarray}\nwhere $k_{(J,M)}$ and $\\tilde{k}_{(J,M)}$ are given by \n\\begin{eqnarray}\nk_{(J, M)} &\\equiv& \n-i\\sqrt{\\frac l{8G}}+\\frac{r_+ +r_-}{\\sqrt{8Gl}},\n\\nonumber \\\\ \n\\tilde{k}_{(J, M)} &\\equiv& \n -i\\sqrt{\\frac l{8G}}+\\frac{r_+ -r_-}{\\sqrt{8Gl}}. \n\\label{k(J,M)}\n\\end{eqnarray}\n${\\rm AdS}_{3}$ state (\\ref{eq:priads}) can be \nidentified with the $SL_{2}({\\bf C})$-invariant vacuum:\n\\begin{eqnarray}\n{\\rm AdS}_{3} \\Longleftrightarrow |vac\\rangle\n\\equiv |0\\rangle\\otimes|0\\rangle.\n\\end{eqnarray}\n\nThe Fock spaces ${\\cal F}_{k} \\otimes {\\tilde {\\cal\nF}}_{{\\tilde k}}$ built on these primary\nstates give the unitary irreducible representations of the Virasoro\nalgebra with $c=1+3l\/2G$, and coincide with the physical Hilbert\nspace of the previous quantization of three-dimensional pure\ngravity.\n\nTo summarize, in this correspondence of three-dimensional pure gravity\nand the boundary CFT, the BTZ black holes appear as the primary\nstates of the Virasoro algebra with $c = 3l\/2G$ in both descriptions.\nThis result may not be desirable for the counting of microscopic states of\nthe BTZ black holes. We cannot count in principle the\ndegeneracy of these primary states with this boundary theory,\nsince this Liouville field theory has continuum\nspectrum of primary states.\n\n\\subsection{BTZ black holes and Maldacena duality}\n\nNext, we consider the ${\\rm AdS}_{3}\/{\\rm CFT}_{2}$ correspondence \nin superstring theory.\nThrough the analysis\nof the near horizon limit of the BPS solitonic solution of \nIIB supergravity, which describes\nthe bound state of $Q_{1}$ D1-branes and $Q_{5}$ D5-branes,\nMaldacena has conjectured in \\cite{Maldacena1},\n\\begin{center}\nIIB superstring theory on (${\\rm AdS}_{3} \\times {\\rm S}^{3})_{Q_{1}Q_{5}} \n\\times M_{4}$ \\quad ($M_{4}$ = $K3$ or $T^4$)\\\\ \n$\\Updownarrow$ dual \\\\ two-dimensional N=(4,4) supersymmetric\n$\\sigma$-model \\\\ on the Higgs branch of world volume\ntheory of the D1-D5 system.\n\\end{center}\nHere, we indicated the dependence of the radius of \n${\\rm AdS}_{3}$ and ${\\rm S}^{3}$ on $Q_{1}Q_{5}$ \n(see below). We call this duality simply the Maldacena duality.\n\nWe will discuss mainly the case of $M_{4}$ = $K3$ in the following.\nThe N=(4,4) $\\sigma$-model can be regarded as\nthe $\\sigma$-model on the target space of the $k$-th symmetric product of\n$K3$ \\cite{Vafa1,Dijkgraaf}, where\n\\begin{eqnarray}\nk = Q_{1}Q_{5} + 1.\n\\end{eqnarray} \nSince the symmetric product \nis 4$k$-dimensional hyper-K\\\"ahler manifold, it\nhas automatically the N=(4,4) superconformal symmetry.\nThe Virasoro subalgebra and zero mode of the\nSU(2) current algebra may\nbe identified with the Virasoro algebra of \nBrown-Henneaux on the boundary of ${\\rm AdS}_{3}$ and \nthe isometry of ${\\rm S}^{3}$, respectively. We will discuss \nthe N=4 superconformal algebra in more detail in section 3.\n\nThe extremal BTZ black holes can also be obtained\nas the near horizon limit of the similar BPS solitonic solutions of \nIIB supergravity.\nTherefore we can expect that the extremal BTZ black holes \ncan be analyzed by this N=(4,4) $\\sigma$-model.\n\nWe summarize some related facts of IIB supergravity here.\nIIB supergravity on $S^{1} \\times K3$ whose radius and volume are $R$\nand $(2 \\pi)^4 \\alpha^{'2} v$ has the BPS solitonic solution\n(see for example \\cite{Skenderis} and references therein):\n\\begin{eqnarray}\nds_{10}^{2} &=& f_{1}^{-\\frac{1}{2}}f_{5}^{-\\frac{1}{2}}\\{ - dt^2 +\ndx_{5}^{2} + f_{N}(dt+dx_{5})^2\\} \\nonumber \\\\ && + f_{1}^{\\frac{1}{2}}\nf_{5}^{\\frac{1}{2}}(dx_{1}^{2}+dx_{2}^{2}+dx_{3}^{2}+dx_{4}^{2}) + \nf_{1}^{\\frac{1}{2}}f_{5}^{-\\frac{1}{2}} ds_{K3}^{2}, \\label{eq:10dim} \n\\end{eqnarray}\nwith periodic identification $x_{5} \\sim x_{5} + 2 \\pi R$\nin string frame and\n\\begin{eqnarray}\ne^{-2 (\\phi - \\phi_{\\infty})} &=& f_{5}f_{1}^{-1}, \n\\quad C_{05}^{(R)} = \\frac{1}{2}(f_{1}^{-1} - 1), \\nonumber \\\\\nH_{ijk}^{(R)} &=& (*_{6} \\ dC^{(R)})_{ijk} =\n\\frac{1}{2}\\epsilon_{ijkl}\\partial_{l}f_{5} \\qquad (i,j,k,l =\n1,2,3,4),\n\\end{eqnarray}\nwhere $C^{R}$ is Ramond-Ramond 2-form and $*_{6}$ is Hodge dual in\n6-dimension ($t,x_{1},\\cdots,x_{5}$).\nAnd $f_{1}$, $f_{5}$ and $f_{N}$ are following functions with respect\nto radial coordinate, \\\\\n$r = x_{1}^{2}+x_{2}^{2}+x_{3}^{2}+x_{4}^{2}$:\n\\begin{eqnarray}\nf_{1} &=& 1 + \\frac{{\\tilde Q}_{1}}{r^2}, \\qquad {\\tilde Q}_{1} =\n\\frac{\\alpha^{'} g_{st}}{v} Q_{1}, \\\\\nf_{5} &=& 1 + \\frac{{\\tilde Q}_{5}}{r^2}, \\qquad {\\tilde Q}_{5} = \n\\alpha^{'} g_{st}Q_{5}, \\\\\nf_{N} &=& \\frac{{\\tilde N}}{r^2}, \\qquad \\qquad {\\tilde N} =\n\\frac{\\alpha^{'2} g_{st}^{2}}{R^{2} v} N.\n\\end{eqnarray}\n\nThis solution corresponds to the configuration\nof the bound state of $Q_{5}$ D5-branes wrapping on $K3 \\times S^{1}$\nand $Q_{1}$ D1-branes wrapping on $S^{1}$ with $N$ units of KK momenta\nalong $S^{1}$, i.e., $x_{5}$-direction, and preserves four\nsupercharges. So it is a 1\/8 BPS state\\cite{Strominger-Vafa}.\n\nHere we can get the extremal BTZ black hole as the near horizon limit of\nthe geometry (\\ref{eq:10dim})\\cite{Maldacena-Strominger,Skenderis}. \nThe near horizon limit is defined as\n\\begin{eqnarray*}\n\\alpha{'} \\rightarrow 0, \\qquad {\\rm with} \\qquad \nU \\equiv \\frac{r}{\\alpha^{'}}, \\ R \\ {\\rm and} \\ v \\\n {\\rm fixed}.\n\\end{eqnarray*}\nIn this limit, the metric (\\ref{eq:10dim})\ndescribes $({\\rm BTZ}_{(Ml,M)} \\times S^{3})_{Q_{1}Q_{5}}\n\\times K3$ with $Ml = J = N$.\nThe radius of ${\\rm AdS}_{3}$ and ${\\rm S}^{3}$ coincide and become\n$l = g_{st}^{1\/2} \\alpha^{' 1\/2} (Q_{1}Q_{5}\/v)^{1\/4}$. \\\\\nAnd the three-dimensional effective Newton\nconstant on ${\\rm BTZ}_{(N,N\/l)}$ is given by\n$G_{{\\rm eff}}^{(3)} = l\/(4 Q_{1} Q_{5})$.\nThere exists on\nthis background the asymptotic Brown-Henneaux's Virasoro symmetry\nwith central charge,\n\\begin{eqnarray}\nc = \\frac{3l}{2G_{{\\rm eff}}^{(3)}} = 6 Q_{1}Q_{5}.\n\\end{eqnarray}\nThis is the same as the central charge of the N=(4,4) $\\sigma$-model \nat the semiclassical limit $Q_{1}Q_{5} \\gg 1$. \nThe Bekenstein-Hawking entropy becomes\n\\begin{eqnarray}\nS = \\frac{2 \\pi r_{+}}{4 G_{{\\rm eff}}^{3}} = 2 \\pi\n\\sqrt{Q_{1}Q_{5}N}, \\label{eq:extentropy}\n\\end{eqnarray}\nwhich is valid in the semiclassical region $N \\gg Q_{1}Q_{5} \\gg 1$.\n\nIf one accepts the Maldacena duality, the extremal black hole should be\nidentified with the primary state\n\\begin{eqnarray}\n|N+\\frac{c}{24}\\rangle \\otimes |\\frac{c}{24} \\rangle, \\label{eq:extprimary}\n\\end{eqnarray}\nof the N=(4,4) $\\sigma$-model.\nOne can ask whether the entropy (\\ref{eq:extentropy}) can be regarded as \nthe degeneracy of the primary state (\\ref{eq:extprimary}) of this N=(4,4)\n$\\sigma$-model. In the sequel, we will discuss this question and\nanswer in the affirmative. \n\n\\section{N=4 superconformal symmetry}\nN=(4,4) $\\sigma$-model is known to be finite to all orders of\nperturbation and to be conformally invariant at the quantum\nlevel. Thus the states of the $\\sigma$-model on the $k$-th symmetric\nproduct\\footnote{We will\ndenote $k$-th symmetric product of $K3$ as $S^k K3 \\equiv\nK3^{\\otimes k}\/S_{k} \\ (S_{k}$ is a $k$-dimensional symmetric\ngroup).}, $S^k K3$, constitute the unitary\nirreducible representations of the underlying N=4 superconformal\nalgebra (N=4 SCA).\n\n\\subsection{Basics of N=4 superconformal algebra}\nN=4 SCA is generated by $L_{n},\\ J_{n},\\ G_{r}^{i}$ and ${\\bar\nG}_{r}^{i}$ with \n\\begin{eqnarray*}\n[L_{m},L_{n}] &=& (m-n) L_{m+n}+\\frac{k}{2}m(m^2-1)\\delta_{n+m,0}, \\quad\n\\{G_{r}^{i},G_{s}^{j}\\} = \\{\\bar{G}_{r}^{i},\\bar{G}_{s}^{j}\\} = 0, \\\\\n\\{G_{r}^{i},{\\bar G}_{s}^{j}\\} &=& 2\n\\delta^{ij}L_{r+s}-2(r-s)\\sigma_{ij}^{a}J_{r+s}^{a} + \\frac{k}{2}(4\nr^2-1) \\delta_{r+s,0}, \\\\\n\\left[ J_{m}^{a},J_{n}^{b} \\right] &=& i \\epsilon^{abc} J_{m+n}^{c} + \n\\frac{k}{2}m\\delta_{m+n,0}, \\\\\n\\left[ J_{m}^{a},G_{r}^{i} \\right] &=&\n-\\frac{1}{2}\\sigma_{ij}^{a}G_{m+r}^{j}, \\quad \n\\left[ J_{m}^{a},{\\bar G}_{r}^{i} \\right] =\n\\frac{1}{2}\\left(\\sigma_{ij}^{a}\\right)^{*}{\\bar G}_{m+r}^{j}, \\\\\n\\left[ L_{m},G_{r}^{i} \\right] &=&\n(\\frac{m}{2}-r)G_{m+r}^{i}, \\quad \\left[ L_{m},{\\bar G}_{r}^{i} \\right] =\n(\\frac{m}{2}-r){\\bar G}_{m+r}^{i}, \\\\\n\\left[ L_{m},J_{n}^{a} \\right] &=& -n J_{m+n}^{a}. \n\\end{eqnarray*}\n($\\sigma_{ij}^{a}$ is Pauli matrix and $J_{n}^{(\\pm)} \\equiv \nJ_{n}^{1}\\pm i J_{n}^{2}$.) $L_{n},J_{n}^{a}$ and \n$G_{r}^{i}({\\bar G}_{r}^{i})$ represent the\nFourier components of the energy momentum tensor, SU(2) current and four\nsupercurrents, respectively. \n$G_{r}^{i} ({\\bar G}_{r}^{i})$ transforms as SU(2) doublet (its conjugate) \nunder the global SU(2) symmetry which is generated by $J_{0}^{a}$.\nThe level $k$ of the SU(2) current algebra ($\\widehat{{\\rm SU}(2)}_{k}$)\nmust be positive integer for \nunitary representations. The central charge $c$ of the Virasoro subalgebra is \n$6k$. \n\nTwo different boundary conditions of the supercurrents\nprovide two different sectors of this algebra. \nIf $G_{r}^{i}({\\bar G}_{r}^{i})$ has $r \\in\n{\\bf Z}+ \\frac{1}{2} $, it is called the Neveu-Scwarz (NS) sector \nand if $r \\in {\\bf Z}$, it is called the Ramond (R) sector.\nThese sectors are related by the automorphism of the algebra\ncalled spectral flow.\nWe will discuss the R-sector in the following.\n\nUnitary irreducible representations of N=4 SCA have\ntwo distinct types\\cite{Eguchi1}.\nThey are built on highest weight states $|h,l\\rangle$ called massive\nprimary and massless primary in the R-sector.\n\\begin{enumerate}\n\\renewcommand{\\labelenumi}{\\theenumi}\n\\renewcommand{\\theenumi}{(\\roman{enumi})}\n\\item Massive primary state\n \\begin{eqnarray}\n L_{n}|h,l\\rangle &=& G_{n}^{i}|h,l\\rangle = \n {\\bar G}_{n}^{i}|h,l\\rangle = J_{n}^{a}|h,l\\rangle = 0, \\qquad n\n \\geq 1 \\nonumber \\\\\n J_{0}^{(+)}|h,l\\rangle &=& G_{0}^{2} |h,l\\rangle = \n {\\bar G}_{0}^{1}|h,l\\rangle = 0, \\nonumber \\\\\n L_{0}|h,l\\rangle &=& h |h,l\\rangle, \\quad J_{0}^{3} |h,l\\rangle =\n l |h,l\\rangle, \\nonumber \\\\\n h &>& \\frac{k}{4} = \\frac{c}{24}, \n \\qquad l = \\frac{1}{2},1,\\cdots,\\frac{k}{2}-\\frac{1}{2},\\frac{k}{2}. \n \\label{eq:massive}\n \\end{eqnarray}\n\\item Massless primary state\n \\begin{eqnarray}\n L_{n}|h,l\\rangle &=& G_{n}^{i}|h,l\\rangle = \n {\\bar G}_{n}^{i}|h,l\\rangle = J_{n}^{a}|h,l\\rangle = 0, \\qquad n\n \\geq 1 \\nonumber \\\\\n J_{0}^{(+)}|h,l\\rangle &=& G_{0}^{i} |h,l\\rangle = \n {\\bar G}_{0}^{i}|h,l\\rangle = 0, \\qquad i=1,2 \\nonumber \\\\\n L_{0}|h,l\\rangle &=& h |h,l\\rangle, \\quad J_{0}^{3} |h,l\\rangle =\n l |h,l\\rangle, \\nonumber \\\\\n h&=&\\frac{k}{4}=\\frac{c}{24},\\qquad l =0,\\frac{1}{2},\\cdots,\n \\frac{k}{2}-\\frac{1}{2},\\frac{k}{2}. \\label{eq:massless} \n \\end{eqnarray}\n\\end{enumerate}\nThese representations are called massive representation ${\\cal M}^{k}_{(h,l)}$ \nand massless representation ${\\cal M}^{k}_{0 (l)}$ respectively.\n\nThe massive representations have the same number of bosonic and\nfermionic states at each level and the Witten index is equal to \nzero. \nThese representations correspond to\nthe representations which have spontaneously broken supersymmetry.\nThe Witten index of the massless representations is non-zero. \nThese representations are the representations which have\nunbroken supersymmetry. \nThe primary states of massless representations have dimension \n$h=k\/4=c\/24$. These are the ground states of the R-sector.\n\nCharacter of the representation is introduced by\n${\\rm ch}^{(R)}(\\tau,z) = \n{\\rm Tr} (q^{L_{0}-\\frac{c}{24}} y^{2 J_{0}^{3}})$.\n($q=e^{2 \\pi i \\tau}$ and $y=e^{2 \\pi i z}$.) \nTheir explicit form is given in \\cite{Eguchi2}. \n\\begin{enumerate}\n\\renewcommand{\\labelenumi}{\\theenumi}\n\\renewcommand{\\theenumi}{(\\roman{enumi})}\n\\item The character of the massive representation ${\\cal M}^{k}_{(h,l)}$: \n \\begin{eqnarray}\n {\\rm ch}^{(R) k}(h,l ; \\tau,z) =\n q^{h-\\frac{k}{4}-\\frac{l^2}{k+1}}\n \\frac{\\theta_{2}(\\tau,z)^2}{\\eta(\\tau)^3}\n \\chi_{k-1}^{l-\\frac{1}{2}}(\\tau,z), \\label{eq:charmass}\n \\end{eqnarray}\n where $\\chi_{k}^{l}(\\tau,z)$ is the character of \n $\\widehat{{\\rm SU}(2)}_{k}$ of isospin $l$,\n \\begin{eqnarray}\n \\chi_{k}^{l}(\\tau,z) &=& \\frac{q^{\\frac{(l+\\frac{1}{2})^2}{k+2}\n -\\frac{1}{8}}}{\\prod_{n=1}^{\\infty}(1-q^n)(1-y^2\n q^n)(1-y^{-2}q^{n-1})} \\nonumber \\\\\n &&\\times \\sum_{m=0}^{\\infty}q^{(k+2)m^2 + (2 l + 1)}\\left(y^{2\n \\{(k+2)m + l\\}}-y^{-2 \\{ (k+2)m+l+1 \\}}\\right) \\nonumber \\\\\n && = \\frac{\\Theta_{2l+1, k+2}(\\tau, 2z) - \\Theta_{-2l-1, k+2}(\\tau,\n 2z)}{\\Theta_{1,2}(\\tau,2z) - \\Theta_{-1,2}(\\tau,2z)}.\n \\label{eq:charaffine}\n \\end{eqnarray}\n $\\Theta_{l,k}(\\tau,z) = \\sum_{n=-\\infty}^{\\infty} q^{k\n (n+\\frac{l}{2k})^2} y^{k (n+\\frac{l}{2k})}$ is the theta\n function associated with $\\widehat{{\\rm SU}(2)}_{k}$ of isospin $l$.\n\\item The character of the massless representation ${\\cal M}^{k}_{0 (l)}$:\n \\begin{eqnarray}\n {\\rm ch}_{0}^{(R) k}(h=\\frac{k}{4},l;\\tau,z) &=&\n q^{-\\frac{1}{8}}\\frac{\\theta_{2}(\\tau,z)^2}{\\eta(\\tau)^3} \n \\frac{1}{\\prod_{n=1}^{\\infty}(1-q^n)(1-y^2\n q^n)(1-y^{-2}q^{n-1})} \\nonumber \\\\\n \\times \\sum_{m=-\\infty}^{\\infty}&& \\hskip-1cm q^{(k+1)m^2 + 2 l m} \n \\left( \\frac{y^{2 \\{(k+2)m+l-\\frac{1}{2}\\}}}{(1+y^{-1}q^{-m})^2} - \n \\frac{y^{-2 \\{ (k+2)m+l+\\frac{1}{2}\\}}}{(1+y q^{-m})^2} \\right).\n \\label{eq:charless}\n \\end{eqnarray}\n\\end{enumerate}\n\nThese characters enjoy the following properties.\nThe Witten index of the representation \ncan be obtained, if one sets $z=1\/2$, i.e., $y=-1$:\n\\begin{eqnarray}\n{\\rm ch}^{(R) k}(h,l;\\tau,z=\\frac{1}{2}) &=& 0, \\\\\n{\\rm ch}_{0}^{(R) k}(h=\\frac{k}{4},l;\\tau,z=\\frac{1}{2}) &=& \n(-1)^{2 l}(2 l+1).\n\\end{eqnarray}\nThe characters of massive and massless\nrepresentations are related by\n\\begin{eqnarray}\n{\\rm ch}^{(R) k}(h=\\frac{k}{4},l;\\tau,z) &=& \n{\\rm ch}_{0}^{(R) k}(h=\\frac{k}{4},l;\\tau,z) \n+ 2 \\ {\\rm ch}_{0}^{(R) k}(h=\\frac{k}{4},l-\\frac{1}{2};\\tau,z)\n\\nonumber \\\\\n&&+ \\ {\\rm ch}_{0}^{(R) k}(h=\\frac{k}{4},l-1;\\tau,z).\n\\label{eq:relation}\n\\end{eqnarray}\n\n\\subsection{Identification of the black hole states}\n\nAs argued in section 2.2, the extremal BTZ black hole will correspond\nto the primary state (\\ref{eq:extprimary}) of the N=(4,4)\n$\\sigma$-model.\nIt is a Virasoro primary state of the underlying N=4\nSCA. The fact that the extremal BTZ black holes are the 1\/2 \nBPS states with respect to the Poincare supersymmetry in \nthree dimensions\\footnote{These correspond to the 1\/4\nBPS states in Anti-de Sitter supersymmetry in 3-dimension.} implies\nthat this primary state is in the tensor\nproduct of massive and massless representations ${\\cal\nM}^{k}_{(h,l)} \\otimes {\\tilde {\\cal M}}^{k}_{0 ({\\tilde l})}$ which\nis built on \n\\begin{eqnarray}\n|h=N+\\frac{k}{4},l\\rangle \\otimes |{\\tilde h}=\\frac{k}{4},{\\tilde\n l}\\rangle. \\label{eq:prisigma}\n\\end{eqnarray}\n\nActually we can proceed further. Since the extremal \nBTZ black holes do not have the conserved \ncharge corresponding to isospin $l$ and ${\\tilde l}$,\nthe primary state (\\ref{eq:extprimary}) may be identified with the\nVirasoro primary state having vanishing isospins $l={\\tilde\nl}=0$ in \n${\\cal M}^{k}_{(h,l)} \\otimes {\\tilde {\\cal M}}^{k}_{0 ({\\tilde l})}$.\nThe primary states with $l=0$ \nin ${\\cal M}^{k}_{(h,l)}$\nare as follows:\nwhen $l \\in {\\bf Z}$, they are given by \n\\begin{eqnarray}\n&&J_{0}^{(-) \\ l} |h=N+\\frac{k}{4},l\\rangle, \\nonumber \\\\\n{\\rm and} && J_{0}^{(-) \\ l-1} ({\\bar G}_{0}^{2} G_{0}^{1} - \n\\frac{h-k\/4}{l}) |h=N+\\frac{k}{4},l \\rangle, \\label{eq:integer}\n\\end{eqnarray}\nand when $l \\in {\\bf Z}+\\frac{1}{2}$, they are \n\\begin{eqnarray}\n&&J_{0}^{(-) \\ l-\\frac{1}{2}} G_{0}^{1} |h=N+\\frac{k}{4},l\\rangle,\n\\nonumber \\\\\n\\hskip-1.4cm{\\rm and} \\qquad \n&&J_{0}^{(-) \\ l-\\frac{1}{2}}{\\bar\nG}_{0}^{2}|h=N+\\frac{k}{4},l\\rangle.\n\\label{eq:halfint} \n\\end{eqnarray}\nThe primary states with ${\\tilde l}=0$ in\n${\\tilde {\\cal M}}^{k}_{0 ({\\tilde l})}$ is identified with\n${\\bar J}_{0}^{(-) \\ {\\tilde l}} |{\\tilde h}=k\/4,{\\tilde\nl}\\rangle.$\nThe primary state (\\ref{eq:extprimary}) can be identified with the\ntensor product of these states. So, the degeneracy of the state is\nalmost same as the degeneracy of the representation ${\\cal M}^{k}_{(h,l)} \n\\otimes {\\tilde {\\cal M}}^{k}_{0 ({\\tilde l})}$. \n \n\\section{Elliptic genus for $\\sigma$-model on symmetric product\nof K3}\nTo count the number of 1\/2 BPS states in the N=(4,4)\n$\\sigma$-model,\nthe so-called ``elliptic genus'' is a convenient tool.\nWe summarize some properties of the elliptic genus\nemphasizing its modular transform and examine it from the perspective\nof N=4 SCA. \n\\subsection{Elliptic genus as a weak Jacobi form}\nThe elliptic genus of target space $M$ \nis defined by the following trace in the R-R sector of the underlying\nN=(2,2) superconformal field theory\\footnote{One can obtain the following\ntopological indices of the target space $M$, if one sets $z$ to be\nspecific value.\n\\begin{eqnarray}\nZ[M](\\tau,0) &:& {\\rm Elliptic \\ extension \\ of \\ Euler \\ number}\n\\nonumber \\\\\nZ[M](\\tau,\\frac{1}{2})&:& {\\rm Elliptic \\ extension \\ of \\\nHirzebruch \\ signature} \\nonumber \\\\\nq^{\\frac{c}{24}}Z[M](\\tau,\\frac{\\tau+1}{2})&:& \n{\\rm Elliptic \\ extension \\ of \\ Dirac \\ genus} \\nonumber\n\\end{eqnarray}}.\n\\begin{eqnarray}\nZ[M](\\tau,z) = {\\rm Tr}_{{\\rm R}\\textrm{-}{\\rm R}}(-1)^{J_{0}-{\\bar\nJ}_{0}} q^{L_{0}-\\frac{c}{24}} {\\bar q}^{{\\bar\nL}_{0}-\\frac{c}{24}} y^{J_{0}}, \\label{eq:genus}\n\\end{eqnarray}\nwhere $J_{0}$ and ${\\bar J}_{0}$ are the integral N=2 U(1) charges\nof the left-moving and the right-moving sectors\\footnote{N=2 \nSCA can be embedded into N=4 SCA by\n$G_{r} = G_{r}^{1} + {\\bar G}_{r}^{2}$, ${\\bar G}_{r} = \nG_{r}^{2} + {\\bar G}_{r}^{1}$ and $J_{n}=2 J_{n}^{3}$.}. \nThe elliptic genus is\nindependent of ${\\bar \\tau}$ by virtue of supersymmetry of the R-sector.\nThe contribution of the right-moving sector is only from the\nground states.\nBut all states in the left-moving sector contribute to $Z[M](\\tau,z)$.\nSo the elliptic genus $Z[M](\\tau,z)$ is a useful quantity for \ncounting of the 1\/2 BPS states.\n\nThe following theorem is known about this elliptic genus. (See\n \\cite{Kawai2} for detail.)\n\n\\noindent\n{\\bf Theorem 1.} If the target space $M$ of the $\\sigma$-model is an\neven-dimensional Calabi-Yau manifold, then the elliptic genus\n$Z[M](\\tau,z)$ is a weak Jacobi form of weight 0 and index\n$d\/2 (d=\\dim_{{\\bf C}} M)$ without character.\n\nWeak Jacobi form in the above theorem is defined as\nfollows\\cite{Eichler}.\n\n\\noindent \n{\\bf Definition.} A function \n$\\phi(\\tau,z)$ is called a weak Jacobi form of weight $k \\in {\\bf Z}$ \nand index $m \\in {\\bf Z}_{>0}\/2$ without character, if it satisfies\n(i)$\\sim$(iv): \n\\begin{enumerate}\n\\renewcommand{\\labelenumi}{\\theenumi}\n\\renewcommand{\\theenumi}{(\\roman{enumi})}\n\\item $\\phi(\\tau,z)$ is a holomorphic function with respect to $\\tau\n \\in {\\bf H}^{+} \\ ({\\bf H}^{+}:$ upper half plane) and $z \\in\n {\\bf C}$.\n\\item $\\phi(\\frac{a \\tau + b}{c \\tau + d},\\frac{z}{c \\tau + d}) = \n (c \\tau + d)^{k}e^{\\frac{2 \\pi i m c z^{2}}{c \\tau +\n d}}\\phi(\\tau,z). \\quad (a,b,c,d \\in {\\bf Z} \\ {\\rm and} \\ ad-bc=1)$\n\\item $\\phi(\\tau,z + \\lambda \\tau + \\mu) = e^{- 2\\pi i m (\\lambda^2\n \\tau + 2 \\lambda z)} \\phi(\\tau,z). \\quad (\\lambda, \\mu \\in {\\bf Z})$\n\\item $\\phi(\\tau,z)$ has the Fourier expansion of the form \\\\\n \\begin{eqnarray*}\n \\phi(\\tau,z) = \\sum_{n=0}^{\\infty} \\sum_{r = - \\infty}^{\\infty}\n c(n,r) q^n y^r \\quad (q= e^{2 \\pi i \\tau},y = e^{2 \\pi i z}).\n \\end{eqnarray*}\n\\end{enumerate}\n\nWhen $M$ is $K3$, the elliptic genus $Z[K3](\\tau,z)$\nbecomes a weak Jacobi form of weight 0 and index 1 without character.\nAn actual calculation of the N=(4,4) supersymmetric\n$\\sigma$-model on $K3$\\cite{Eguchi4,Kawai1} determines it explicitly as\n\\begin{eqnarray}\nZ[K3](\\tau,z) = 24 \\wp(\\tau,z)K^2(\\tau,z), \\label{eq:pfnK3}\n\\end{eqnarray}\nwhere\n\\begin{eqnarray}\n\\wp(\\tau,z) &:& {\\rm Weierstrass's} \\ \n\\wp {\\rm -function}, \\nonumber \\\\\nK(\\tau,z) &=& i \\frac{\\theta_{1}(\\tau,z)^2}{\\eta(\\tau)^3}.\n\\label{eq:kfn} \n\\end{eqnarray} \n\nWe need the following theorem about weak Jacobi form\\cite{Kawai2,Eichler}. \n\n\\noindent\n{\\bf Theorem 2}. If we assign weights 4, 6 and 2 respectively to\n$E_{4}(\\tau), E_{6}(\\tau)$\\footnote{$E_{4}(\\tau)$ and $E_{6}(\\tau)$\nare the Eisenstein series.} and $\\wp(\\tau,z)$, any weak Jacobi\nform of weight $2l$ ($l \\in {\\bf Z}_{\\geq 0}$) and index $k$ ($k \\in {\\bf\nZ}_{\\geq 0}$) can be expressed as \n\\begin{eqnarray*}\n{\\cal G}_{2 l + 2 k}(E_{4}(\\tau), E_{6}(\\tau), \\wp(\\tau,z))\nK^{2k}(\\tau,z),\n\\end{eqnarray*}\nwhere ${\\cal G}_{2l + 2k}(E_{4},E_{6},\\wp)$ is a homogenious \npolynomial of weight (2$l$ + 2$k$) and its degree as a\npolynomial in $\\wp$ is at most $k$.\n\n$S^k K3$ is 2$k$-dimensional Calabi-Yau manifold. \nThe elliptic genus\n$Z[S^{k}K3](\\tau,z)$ becomes a weak Jacobi form of weight 0 and index $k$. \nAccording to theorem 2, it has the\nfollowing form:\n\\begin{eqnarray}\nZ[S^k K3](\\tau,z) = {\\cal G}_{2k}(E_{4}(\\tau), E_{6}(\\tau),\n\\wp(\\tau,z))K^{2k}(\\tau,z). \\label{eq:generalgenus}\n\\end{eqnarray} \n\nThe homogenious polynomial ${\\cal G}_{2k}$ is determined for lower\nvalues of $k$\\cite{Kawai2}\\footnote{It is worth commenting that \nthe coefficient of the first term in the\nbracket is the Euler number of $S^k K3$, $\\chi(S^k K3)$.}.\n\\begin{eqnarray}\nk=1 &:& 24\\wp K^{2} \\nonumber \\\\\nk=2 &:& (324 \\wp^{2} + \\frac{3}{4} E_{4})K^{4} \\nonumber \\\\\nk=3 &:& (3200 \\wp^{3} + \\frac{64}{3}E_{4}\\wp +\n\\frac{10}{27}E_{6})K^{6}\n\\label{eq:pfnprod}\n\\end{eqnarray}\n\n\\subsection{Elliptic genus of $S^k K3$ and characters of N=4\nsuperconformal algebra}\nThe N=(4,4) $\\sigma$-model on the target space of $K3$ has been\nanalyzed in detail by Eguchi et.al.\\cite{Eguchi4} in the context of \na compactification of string theory on $K3$. The elliptic extension\nof the Hirzebruch signature of $K3$ \ncan be represented by the characters of the N=4\nSCA as\n\\begin{eqnarray}\n&&\\hspace{-2cm} Z[K3](\\tau,\\frac{1}{2}) \\nonumber \\\\\n&=&-2 \\ {\\rm ch}_{0}^{(R) k=1}(h=\\frac{1}{4},l=\\frac{1}{2};\\tau,0) +\n20 \\ {\\rm ch}_{0}^{(R) k=1}(h=\\frac{1}{4},l=0;\\tau,0) \\nonumber \\\\\n&&+ F(\\tau) \\ {\\rm ch}^{(R) k=1}(h=\\frac{1}{4},l=\\frac{1}{2};\\tau,0)\n\\nonumber \\\\\n&=& 24 \\ {\\rm ch}_{0}^{(R) k=1}(h=\\frac{1}{4},l=0;\\tau,0) +\n {\\tilde F}(\\tau) \\ {\\rm ch}^{(R)\nk=1}(h=\\frac{1}{4},l=\\frac{1}{2};\\tau,0),\n\\label{eq:genusK3}\n\\end{eqnarray}\nwhere\n\\begin{eqnarray}\n{\\tilde F}(\\tau) = -2 + F(\\tau) = \\sum_{n=0}^{\\infty}a_{n}q^{n} \\qquad\n(a_{0} = -2, \\ a_{n} \\in {\\bf Z}_{\\geq 0} (n>0)).\n\\end{eqnarray} \nWe have used eq.(\\ref{eq:relation}) to obtain the last equality in \n(\\ref{eq:genusK3}).\nThe degeneracy of the massive primary states in the left-moving sector\nis encoded in ${\\tilde F}(\\tau)$. The coefficient $a_{n}$ is the\ndegeneracy of the massive primary states of $h=n+1\/4$.\n\nThe function ${\\tilde F}(\\tau)$ can be\ndetermined by combining\ntwo expressions of elliptic genus (\\ref{eq:pfnK3}) and (\\ref{eq:genusK3}),\n\\begin{eqnarray}\nZ[K3](\\tau,\\frac{1}{2}) &=& 24 \\ {\\rm ch}_{0}^{(R) k=1}\n(h=\\frac{1}{4},l=0;\\tau,0) \\nonumber \\\\\n&& + {\\tilde F}(\\tau) \\ {\\rm ch}^{(R)\nk=1}(h=\\frac{1}{4},l=\\frac{1}{2};\\tau,0) \\nonumber \\\\\n&=& 24 \\ \\wp(\\tau,\\frac{1}{2})K^{2}(\\tau,\\frac{1}{2}).\n\\end{eqnarray} \nThis gives\n\\begin{eqnarray}\n{\\tilde F}(\\tau) &=& 2 \\ \\frac{\\theta_{2}(\\tau,0)^4 -\n\\theta_{4}(\\tau,0)^4}{\\prod_{n=1}^{\\infty}(1-q^n)^3} - 24 \\ \n{\\tilde h}_{3}(\\tau), \n\\end{eqnarray}\nwhere\n\\begin{eqnarray} \n{\\tilde h}_{3}(\\tau) &=&\n\\frac{1}{\\theta_{3}(\\tau,0)}\\sum_{m=-\\infty}^{\\infty}\n\\frac{q^{\\frac{m^2}{2}}}{1+q^{m-\\frac{1}{2}}}.\n\\end{eqnarray}\n\nNow, we will turn to the case of the symmetric product.\nThe elliptic genus of $S^k K3$ can be \nalso expanded by the characters of the underlying N=4 SCA.\nIn general, it has the following expansion:\n\\begin{eqnarray}\n&&\\hspace{-2cm}Z[S^k K3](\\tau, z+\\frac{1}{2}) \n\\qquad \\left( = {\\rm Tr}(-1)^{- 2 {\\bar\nJ}_{0}^{3}}{\\bar q}^{{\\bar\nL}_{0}-\\frac{c}{24}}q^{L_{0}-\\frac{c}{24}}y^{2 J_{0}^{3}} \\right) \\nonumber \\\\\n&&\\hspace{-1cm}= \\chi(S^k K3) \\ {\\rm ch}_{0}^{(R) k}\n(h=\\frac{k}{4},l=0;\\tau,z) + \n\\sum_{l=\\frac{1}{2}}^{\\frac{k}{2}}F_{l}(\\tau) \\ {\\rm ch}^{(R) k}\n(h=\\frac{k}{4},l;\\tau,z), \\label{eq:charfnprod}\n\\end{eqnarray}\nwhere\n\\begin{eqnarray}\nF_{l}(\\tau) = \\sum_{n=0}^{\\infty}a_{n}^{(l)}q^n, \\qquad \\quad (a_{0}^{(l)}\n\\in {\\bf Z}, \\ a_{n}^{(l)} \\in {\\bf Z}_{\\geq 0} \\ (n>0)). \\label{eq:ffun}\n\\end{eqnarray} \nAlthough the characters of the massless representations with $l \\neq\n0$ may appear in $Z[S^k K3](\\tau,z+1\/2)$, we can reduce them\nto $l=0$ by applying (\\ref{eq:relation}) recursively. $a_{n}^{(l)}$ in\n(\\ref{eq:ffun}) provides the degeneracy of the representation \n${\\cal M}_{(h,l)}^{k} \\otimes {\\tilde {\\cal M}}_{0 ({\\tilde l})}^{k}$ \nwith $h=n+k\/4$ and isospin $l$. $\\sum_{l}a_{n}^{(l)}$ provides \nthe number of the representations ${\\cal M}_{(h,l)}^{k} \n\\otimes {\\tilde {\\cal M}}_{0 ({\\tilde l})}^{k}$ with $h=n+k\/4$. \n\nBecause of eq.(\\ref{eq:generalgenus}), \nthe spectrum of the massive primary states of this $\\sigma$-model on \n$S^k K3$ also\nbecomes discrete and all states have dimension $h = {\\bf Z}_{\\geq 0}\n+k\/4$ in $Z[S^k K3](\\tau,z+1\/2)$. This corresponds to the fact \nthe extremal BTZ black holes have the discrete mass $N\/l$ in the context of\nthe Maldacena duality. \n\nThe function $F_{l}(\\tau)$ \\ (or $\\sum_{l}F_{l}(\\tau)$) in\neq.(\\ref{eq:charfnprod}) can be determined in principle by an \nanalogous way with the case of $K3$.\nBut it is a hard task to obtain the exact functional form of \n$Z[S^k K3](\\tau,z)$ such as (\\ref{eq:pfnprod}) for the case of \ngeneral $k$. \nHowever, as discussed in section 2.2, what we need for \nthe counting of the microscopic states comparable with \nthe Bekenstein-Hawking entropy is \nthe asymptotic form of $a_{n}^{(l)}$ (or $\\sum_{l} a_{n}^{(l)}$) \nat $n \\rightarrow \\infty$, since eq.(\\ref{eq:extentropy}) is valid\nfor the region $N \\gg Q_{1}Q_{5} \\gg 1$.\nWe will consider this asymptotic form in the next section.\n\n\\section{State counting via the N=(4,4) $\\sigma$-model} \nIn this section, we will discuss the degeneracy of \n1\/8 BPS states of the D1-D5\nsystem in IIB superstring theory and the degeneracy of the primary states \ncorresponding to the extremal BTZ black holes.\nIn the previous section, we obtain two different expressions\nof the elliptic genus of $S^k K3$. \nWe will first use the expression in terms of a weak\nJacobi form and count the number of the 1\/8 BPS states by using a\nTauberian theorem. Then using the expression in terms of the \ncharacters of N=4 SCA, we will discuss the\ndegeneracy of the massive primary states and obtain the\nmicroscopic entropy of the corresponding extremal BTZ black holes.\n\n\\subsection{Counting the 1\/8 BPS states}\nLet us start by studying the asymptotic behavior of the elliptic genus\n$Z[S^k K3](\\tau,1\/2)$ as $\\tau \\downarrow 0$\\footnote{\n$\\tau \\downarrow 0 \\stackrel{{\\rm def}}{\\Longleftrightarrow} \\tau = iT\n\\ (T \\in {\\bf R}_{>0}),\\ {\\rm and} \\ T \\rightarrow 0.$}. \nDue to the structure theorem the elliptic genus\nhas the form (\\ref{eq:generalgenus})\n\\begin{eqnarray*}\nZ[S^k K3](\\tau,\\frac{1}{2}) = \\\n{\\cal G}_{2 k}\\left( E_{4}(\\tau),E_{6}(\\tau),\\wp(\\tau,\\frac{1}{2})\n\\right) K^{2 k}(\\tau,\\frac{1}{2}). \n\\end{eqnarray*}\nThe asymptotics can be obtained \nfrom those of the constituents in (\\ref{eq:generalgenus}).\n$\\wp(\\tau,1\/2)$, $E_{4}(\\tau)$ and\n$E_{6}(\\tau)$ behave as \n$\\wp(\\tau,1\/2) \\rightarrow (-1\/12)(-i \\tau)^{-2}, \\\nE_{4}(\\tau) \\rightarrow 1 (-i \\tau)^{-4}$, and \n$E_{6}(\\tau) \\rightarrow (-1)(-i \\tau)^{-6}$. \nTherefore the asymptotics of ${\\cal G}_{2k}$ becomes\n\\begin{eqnarray}\n{\\cal G}_{2k}(\\tau,\\frac{1}{2}) \\rightarrow {\\tilde c}(k) (-i \\tau)^{-2 k}\n\\qquad {\\rm as} \\ \\tau \\downarrow 0,\n\\end{eqnarray}\nwhere ${\\tilde c}(k)$ is a constant which depends on $k$ and the\npolynomial form of ${\\cal G}_{2k}$.\nThe asymptotics of $K^{2}(\\tau,1\/2)$ can be read from \n$\\theta_{2}(\\tau,0) \\rightarrow 1 (-i \\tau)^{-\\frac{1}{2}}$ and \n$\\eta(\\tau) \\rightarrow 1 (-i \\tau)^{-\\frac{1}{2}} e^{-\\frac{\\pi i}{12\n\\tau}}$,\n\\begin{eqnarray}\nK^{2}(\\tau,\\frac{1}{2}) \\rightarrow (-1)(-i \\tau)^{2} e^{\\frac{\\pi i}{2\n\\tau}} \\qquad {\\rm as} \\ \\tau \\downarrow 0.\n\\end{eqnarray}\n\nTherefore, gathering these asymptotics, we obtain\n\\begin{eqnarray}\nZ[S^k K3](\\tau,\\frac{1}{2}) \\rightarrow c(k)(-i \\tau)^{0}\ne^{\\frac{\\pi i k}{2 \\tau}} \\qquad {\\rm as} \\ \\tau \\downarrow 0.\n \\quad \\left( c(k) = (-1)^k {\\tilde c}(k) \\right) \\label{eq:asymz}\n\\end{eqnarray}\n\nThe elliptic genus has the Fourier expansion of the form\n\\begin{eqnarray}\nZ[S^k K3](\\tau,\\frac{1}{2})=\\sum_{n=0}^{\\infty} a_{n}q^n.\n\\label{eq:expgenus}\n\\end{eqnarray}\nAgain, due to the structure theorem, the coefficients satisfy $a_{n}\n\\leq a_{n+1}$. (See Appendix A for the explicit Fourier expansions of \nvarious functions.) Each coefficient $a_{n}$ represent the number of\nthe 1\/2 BPS states of $h=n+k\/4$ in the N=(4,4) $\\sigma$-model.\nThis is the number of\nthe $1\/8$ BPS states of the mass specified by\n$h=n+k\/4$ in the D1-D5 system \\cite{Strominger-Vafa,Maldacena-Strominger}.\nWe can estimate the asymptotic form of $a_{n}$ by using the following\nTauberian theorem\\cite{Kac1,Kac2}.\n\n\\noindent \n{\\bf Theorem 3.} Let $f(\\tau)$ be a function \n\\begin{eqnarray*}\nf(\\tau) = q^{\\lambda} \\sum_{n=0}^{\\infty} a_{n} q^n \\qquad (q=e^{2 \\pi\ni \\tau})\n\\end{eqnarray*}\nwhich satisfies following conditions:\n\\begin{enumerate}\n\\renewcommand{\\labelenumi}{\\theenumi}\n\\renewcommand{\\theenumi}{(\\roman{enumi})}\n\\item $f(\\tau)$ is a holomorphic function on ${\\bf H}^{+}$.\n\\item $a_{n} \\in {\\bf R}$ and $a_{n} \\leq a_{n+1}$ for all $n$.\n\\item There exist $c \\in {\\bf C}$, $d \\in {\\bf R}$ and $N \\in \n {\\bf R}_{>0}$ such that \n \\begin{eqnarray*}\n f(\\tau) \\rightarrow c (-i \\tau)^{-d} e^{\\frac{2 \\pi i N}{\\tau}}\n \\qquad {\\rm as} \\ \\tau \\downarrow 0.\n \\end{eqnarray*}\n\\end{enumerate}\nThen, the behavior of $a_{n}$ at large $n$ is\n\\begin{eqnarray*}\na_{n} \\sim \\frac{c}{\\sqrt{2}}N^{-\\frac{1}{2}(d-\\frac{1}{2})}\nn^{\\frac{1}{2}(d-\\frac{3}{2})} e^{2 \\pi \\sqrt{4 N n}} \\qquad {\\rm as} \\ \nn \\rightarrow \\infty, \n\\end{eqnarray*}\nwhere $a_{n} \\sim b_{n}$ as $n \\rightarrow \\infty$ means $\\lim_{n\n\\rightarrow \\infty} b_{n}\/a_{n} = 1$. \n\nDue to this theorem, the asymptotic form of $a_{n}$ can be read from\nthe estimation (\\ref{eq:asymz})\\footnote{We can\nexpect $c(k)$ is not large number due to the explicit\nexample of lower $k$. (See \\cite{Kawai2}.)}\n\\begin{eqnarray}\na_{n} \\sim \\frac{c(k)}{\\sqrt{2}} \\left( \n\\frac{k}{4} \\right)^{\\frac{1}{4}} n^{-\\frac{3}{4}} e^{2 \\pi \\sqrt{k\nn}}. \\label{eq:bpsnum}\n\\end{eqnarray} \n\n\\subsection{Counting the massive primary states}\nWe can also expand the elliptic genus by the characters of N=4 SCA\n\\begin{eqnarray}\n&&\\hspace{-2cm}Z[S^k K3](\\tau,\\frac{1}{2}) \\nonumber \\\\ \n=&&\\hspace{-0.5cm}\\chi(S^k K3) \\ {\\rm ch}_{0}^{(R) k} \n(h=\\frac{k}{4},l=0;\\tau,0) + \n\\sum_{l=\\frac{1}{2}}^{\\frac{k}{2}}F_{l}(\\tau) \\ {\\rm ch}^{(R) k}\n(h=\\frac{k}{4},l;\\tau,0).\n\\label{eq:charexp}\n\\end{eqnarray}\nEach coefficient of $F_{l}(\\tau) =\n\\sum_{n=0}^{\\infty}a_{n}^{(l)} q^{n}$ counts the number of the \nmassive representation ${\\cal M}_{(h{\\rm =}n+k\/4,l)}^{k}$ \nin $Z[S^k K3](\\tau,z)$\nof the N=(4,4) $\\sigma$-model. \nIn particular $\\sum_{l}a_{N}^{(l)}$ will be identified\nwith the number of the primary state (\\ref{eq:extprimary}).\nTo obtain the asymptotic form $\\sum_{l}a_{n}^{(l)}$, we may again\nutilize the Tauberian theorem. For this purpose we need to know the\nasymptotic behavior of $\\sum_{l}F_{l}(\\tau)$ as $\\tau \\downarrow 0$.\n\nSince we have obtained the asymptotics of the elliptic genus\n(\\ref{eq:asymz}), the asymptotics of $\\sum_{l}F_{l}(\\tau)$ becomes\ntractable if we can properly estimate the constituents of the\nmassless and massive characters. \nLet us remind that the character of \nmassive representation ${\\cal M}_{(h,l)}^{k}$ is\ngiven by eq.(\\ref{eq:charmass}).\nThe asymptotic behavior of\nthe character of $\\widehat{{\\rm SU}(2)}_{k}$ \nof the isospin $l$, eq.(\\ref{eq:charaffine}), is given by\\cite{Kac1,Kac2}\n\\begin{eqnarray}\n\\chi_{k}^{l}(\\tau, 0) \\rightarrow a(k,l) \\exp \\left( \n\\frac{\\pi i}{12 \\tau} c_{k} \\right)\n\\qquad {\\rm as} \\ \\tau \\downarrow 0,\n\\end{eqnarray}\nwhere\n\\begin{eqnarray}\na(k,l) &=& \\sqrt{\\frac{2}{k+2}}\\sin\\left(\\frac{(2 l + 1) \\pi}{k+2}\\right),\n\\nonumber \\\\\nc_{k} &=& \\frac{3 k}{k+2}.\n\\end{eqnarray}\nTherefore, combining those of $\\theta_{2}(\\tau,0)$ and $\\eta(\\tau)$,\nwe obtain\n\\begin{eqnarray}\n{\\rm ch}^{(R) k}(h=\\frac{k}{4},l;\\tau,0) \\rightarrow \na(k{\\rm -}1,l{\\rm -}\\frac{1}{2}) (-i \\tau)^{\\frac{1}{2}} \\exp \\left(\n\\frac{\\pi i}{12 \\tau} (3 + c_{k-1}) \\right) \\quad \n{\\rm as} \\ \\tau \\downarrow 0. \\label{eq:asymchar}\n\\end{eqnarray}\n \nAs for the character of massless\nrepresentation ${\\cal M}_{0 (l{\\rm =}0)}^{k}$,\nwe can obtain the upper bound of the asymptotic\nbehavior by means of eq.(\\ref{eq:relation}):\n\\begin{eqnarray}\n\\hspace{-1cm}{\\rm ch}_{0}^{(R) k}\n(h=\\frac{k}{4},l=0;\\tau,0)|_{\\tau \\downarrow 0}\n&\\leq& {\\rm ch}^{(R) k}(h=\\frac{k}{4},l=\\frac{1}{2};\\tau,0)|_{\\tau\n\\downarrow 0} \\nonumber \\\\\n\\hspace{-1cm}&=& a(k,l{\\rm =}0)(-i \\tau)^{\\frac{1}{2}}\\exp\n\\left( \\frac{\\pi i}{12 \\tau} (3 + c_{k-1}) \\right),\n\\end{eqnarray}\nwhere $f(\\tau)|_{\\tau \\downarrow 0}$ means the leading asymptotic of\n$f(\\tau)$ as $\\tau \\downarrow 0$.\nFrom this estimation, the dominant contribution of the \nasymptotic behavior of $Z[S^k K3](\\tau,1\/2)$ turns out to come\nfrom the part of the massive representations. \nWe can neglect the contribution of the massless representations \nin the asymptotics.\n\nNow we can obtain the asymptotic behavior of \n$\\sum_{l}F_{l}(\\tau)$.\nTaking the limit $\\tau \\downarrow 0$ in eq.(\\ref{eq:charexp}), \n\\begin{eqnarray}\nZ[S^k K3](\\tau,\\frac{1}{2}) \\rightarrow \n\\sum_{l=\\frac{1}{2}}^{\\frac{k}{2}} {\\tilde F}_{l}(\\tau)|_{\\tau\n\\downarrow 0} \\times (-i \\tau)^{\\frac{1}{2}}\\exp \\left( \\frac{\\pi i}{12 \n\\tau} (3+c_{k-1}) \\right) \\qquad {\\rm as} \\ \\tau\n\\downarrow 0, \\label{eq:asymf} \n\\end{eqnarray}\nwhere ${\\tilde F}_{l}(\\tau) = \\sum_{n=0}^{\\infty} {\\tilde\na}_{n}^{(l)}q^{n} = a(k{\\rm -1},l{\\rm -1\/2})F_{l}(\\tau)$.\nThe asymptotic behavior of \n$\\sum_{l}{\\tilde F}_{l}(\\tau)$\nas $\\tau \\downarrow 0$ can be read by comparing (\\ref{eq:asymf}) with\n(\\ref{eq:asymz}) \n\\begin{eqnarray}\n\\sum_{l=\\frac{1}{2}}^{\\frac{k}{2}} {\\tilde F}_{l}(\\tau) \\rightarrow\n\\ c(k)(-i \\tau)^{- \\frac{1}{2}} \\exp \\left( \\frac{\\pi i (6 k -\n(3+c_{k-1}))}{12 \\tau} \\right) \\quad {\\rm as} \\ \\tau \\downarrow 0.\n\\end{eqnarray}\n\n$\\sum_{l}{\\tilde F}_{l}(\\tau)$ has the\nFourier expansion of the form\\footnote{These coefficients also satisfy \n$b_{n} \\leq b_{n+1}$.} \n\\begin{eqnarray}\n\\sum_{l=\\frac{1}{2}}^{\\frac{k}{2}} {\\tilde F}_{l}(\\tau) = \n\\sum_{n=0}^{\\infty} b_{n}q^n.\n\\qquad \\left( b_{n} = \\sum_{l=\\frac{1}{2}}^{\\frac{k}{2}} {\\tilde\na}_{n}^{(l)} \\right) \\label{eq:expf}\n\\end{eqnarray} \nDue to the Tauberian theorem, the asymptotic form of\n$b_{n}$ becomes:\n\\begin{eqnarray} \nb_{n} = \\sum_{l=\\frac{1}{2}}^{\\frac{k}{2}} {\\tilde a}_{n}^{(l)} \\sim\n\\frac{c(k)}{\\sqrt{2}}n^{- \\frac{1}{2}} \\exp \\left( 2 \\pi\n\\sqrt{(k-\\frac{(3+c_{k-1})}{6}) n} \\right) \\qquad {\\rm as} \\ n \\rightarrow\n\\infty. \\label{eq:numpri}\n\\end{eqnarray}\nTherefore, we conclude that the degeneracy of the massive\nprimary state of the dimension $h=n+k\/4$ \nat $n\\rightarrow\\infty$ is given by eq.(\\ref{eq:numpri}).\n\nAccording to the argument in section 3.2, the degeneracy of the \nprimary state (\\ref{eq:prisigma}) corresponds to the degeneracy \nof the microscopic states of the extremal BTZ black hole \n${\\rm BTZ}_{(N,N\/l)}$. At the limit $N\\gg k \\gg 1$\\footnote{The \ndifference between $\\sum_{l}{\\tilde a}_{n}^{(l)}$ and\n$\\sum_{l}a_{n}^{(l)}$ is \nirrelevant in this semiclassical limit.}, that is, the semiclassical limit of\nthree-dimensional gravity, the degeneracy of the state \n(\\ref{eq:extprimary}) becomes \n\\begin{eqnarray} \n\\sum_{l=\\frac{1}{2}}^{\\frac{k}{2}} {\\tilde a}_{N}^{(l)} \\sim\n\\frac{c(k)}{\\sqrt{2}}N^{- \\frac{1}{2}} \\exp (2 \\pi\n\\sqrt{(k-1) N}) \\qquad {\\rm as} \\ N \\rightarrow \\infty, \\label{eq:microent}\n\\end{eqnarray}\nwhere $k=Q_{1}Q_{5}+1$.\nThe logarithm of eq.(\\ref{eq:microent}) can be regarded as\nthe microscopic entropy of the extremal BTZ black hole with $Ml=J=N$.\nIt becomes\n\\begin{eqnarray}\nS_{{\\rm micro}} = 2 \\pi \\sqrt{Q_{1}Q_{5}N} + {\\cal O}(\\log N, \\\n\\log c(k)).\n\\end{eqnarray} \nThis completely agrees with the entropy formula\neq.(\\ref{eq:extentropy}). \nThis provides a justification of the\nidentification of the extremal BTZ black hole states with\nthe primary states (\\ref{eq:extprimary}) of the N=(4,4) $\\sigma$-model.\n\n\\section{Discussion} \n\nUntil now, our study is limited to the case of the extremal BTZ black holes.\nThe non-extremal BTZ black holes can also appear as the near\nhorizon geometry of the non-BPS solitonic solutions in IIB supergravity.\nAccording to the duality, the non-extremal BTZ black\nholes may be also identified with the Virasoro primary states \n\\begin{eqnarray}\n |h,l=0\\rangle \\otimes |{\\tilde h},{\\tilde l}=0\\rangle \\ \\quad {\\rm with}\n \\quad h, {\\tilde h} > \\frac{k}{4}, \\label{eq:nonextpri}\n\\end{eqnarray}\nin the corresponding N=(4,4) $\\sigma$-model.\n\nThese states are in the tensor product of the massive representations both in\nthe left and right moving sectors.\nSo we must consider not the elliptic genus but the full\npartition function of the N=(4,4) $\\sigma$-model for the counting of\nthe degeneracy of the states.\nIt is known that the full partition function, which depends on the moduli\nof $S^k K3$, has the contributions from the massive primary states of\n$h={\\bf Q}_{>0}+k\/4$ (${\\bf Q}$ : rational numbers)\\cite{Eguchi4}. \nTherefore, the patition\nfunction and the counterparts of $\\sum_{l}F_{l}(\\tau)$ can not have the\nforms (\\ref{eq:expgenus}) and (\\ref{eq:expf}). So the Tauberian\ntheorem can not be applied to the counting of the primary state \n(\\ref{eq:nonextpri}). \nWe need the further investigations\nfor the well-defined counting of the microscopic states of \nthe non-extremal BTZ black holes. \n\nThrough this paper, we have discussed only the case of $M_{4}=K3$. \nThe similar arguments in section 2.2 hold for \nthe case of $M_{4}=T^4$. However the elliptic genus of the\ncorresponding N=(4,4) $\\sigma$-model vanishes identically, since\nthis $\\sigma$-model has the extra $U(1)^{4}$ symmetry other than\nN=4 superconformal symmetry. So one cannot count the degeneracy\nof the state (\\ref{eq:extprimary}) by means of the elliptic genus.\nIn \\cite{Maldacena3}, the counting of the 1\/8 BPS states has been\nargued by using another topological index called new supersymmetric\nindex. And they pointed out that the representations\nof large N=4 superconformal algebra must be considered.\nWe may expect that the similar argument in this paper can be carried out\nvia the relation between this new supersymmetric\nindex and the characters of large N=4 superconformal algebra.\n\n{\\bf Acknowledgements} \\\\\nThe authors thank H. Umetsu and D. Tomino \nfor their collaboration at the early stage of this work and \nalso for their useful discussions and comments. \nThe authors thank also T. Kawai for his useful comments.\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nSuppose $X$ is a smooth\ncompact manifold and $\\varphi_t:X\\to X$ is an Anosov flow\ngenerated by a smooth vector field $ V $, $ \\varphi_t := \\exp t V $.\nCorrelation functions for a flow are defined as\n\\begin{equation}\n\\label{eq:corr}\n\\rho_{f,g} ( t ) := \\int_X f ( \\varphi_{-t} ( x ) ) g ( x ) dx , \\ \\\nf , g \\in C^\\infty ( X ) , \\ \\ t > 0 ,\n\\end{equation}\nwhere $ dx $ is a Lebesgue density on $ X $.\nThe power spectrum is defined as the (inverse) Fourier-Laplace transform\nof $ \\rho_{f,g} $:\n\\begin{equation}\n\\label{eq:powersp}\n\\widehat \\rho_{f,g} ( \\lambda ) := \\int_0^\\infty \\rho_{f,g} ( t ) e^{ i \\lambda t } dt,\n\\ \\ \\ \\Im \\lambda > 0 .\n\\end{equation}\nFaure--Sj\\\"ostrand \\cite{FaSj} proved that\n\\[ ( P - \\lambda)^{-1} : C^\\infty ( X )\n\\to {\\mathcal D}' ( X ) , \\ \\ P := \\frac 1 i V , \\ \\ \\Im \\lambda \\gg 1 , \\]\ncontinues to a meromorphic family of operators on all of $ \\mathbb C $. Using the fact\nthat $ f ( \\varphi_{-t} ( x ) ) = [\\exp ( - i t P ) f ] ( x ) $ this easily shows\nthat $ \\widehat \\rho_{f,g} ( \\lambda ) $ has a meromorphic continuation.\nThe poles of this continuation depend only on $ P $ and their study\nwas initiated in the work of Ruelle \\cite{Rue} and Pollicott \\cite{Po}.\nThey are called {\\em Pollicott--Ruelle resonances} and their set is denoted\nby $ \\Res ( P )$. The finer properties of the correlations are then\nrelated to the distribution of these resonances. This is particularly\nclear in the work of Liverani \\cite{Liv} and Tsujii \\cite{Ts} on contact\nAnosov flows, see also Nonnenmacher--Zworski \\cite{NZ} for semiclassical generalizations.\n\nAn equivalent definition of Pollicott--Ruelle resonances was given\nby Dyatlov--Zworski \\cite{DZ2}: they are limits (with multiplicities)\nof the eigenvalues of $ P + i \\epsilon \\Delta_g $, $ \\epsilon \\to 0 + $,\nwhere $ - \\Delta_g \\geq 0 $ is a Laplacian for some { Riemannian metric $g$} on $ X $.\nBecause of a connection to Brownian motion this shows stochastic stability of these resonances.\n\nIn this note we address the basic question about the size of the set of\nresonances: is their number always infinite? Despite the long\ntradition of the subject this appeared to be unknown for arbitrary\nAnosov flows on compact manifolds.\nIn Theorem \\ref{thm2}, we show that\nin sufficiently large strips the counting function of resonances cannot\nbe bounded by $r^\\delta$, $ \\delta < 1 $.\n\n\\begin{figure}\n\\includegraphics[width=6in]{gaps}\n\\caption{The {\\em spectral gap} $ \\nu_0 $ is the supremum of $ \\nu $\nsuch that there are {\\em no} resonances with $ - \\nu < \\Im \\lambda $,\n$ \\lambda \\neq 0 $.\nFor contact Anosov flows it is known that $ \\nu_0 > 0 $ \\cite{Liv},\\cite{NZ},\\cite{Ts}.\nThe {\\em essential spectral gap}, $ \\nu_1 $, is the supremum of $ \\nu $\nsuch that there are only finitely many resonances with $ \\Im \\lambda > - \\nu $.\nOur result states that the essential spectral gap is finite for any\nAnosov flow on a compact manifold.}\n\\end{figure}\n\n{General upper bounds\non the number of resonances in strips were established by\nFaure--Sj\\\"ostrand \\cite{FaSj} (and with a sharper \nexponent in the case of contact flows by Datchev--Dyatlov--Zworski \\cite{DDZ}):\nfor any $A > 0 $ there exists $ C $ such that\n\\begin{equation}\n\\label{eq:counting}\n\\#(\\Res(P)\\cap\\{\\Im\\mu>-A, |\\Re\\mu - r |\\leq \\sqrt r\\})\n\\leq Cr^{n- \\frac12} .\n\\end{equation}}\nOn the other hand, for contact Anosov flows satisfying certain pinching conditions on Lyapunov exponents, Faure--Tsujii \\cite{FaTs} showed that\nthe resonances satisfy a precise counting law in strips, agreeing with\nthe upper bound of \\cite{DDZ}. That is a far reaching generalization of the results known in constant curvature: see Dyatlov--Faure--Guillarmou \\cite{DFG} for recent results in that case and references.\n\nThe new counting result is proved by\nestablishing a local trace formula relating resonances to periods of\nclosed trajectories and to the their Poincar\\'e maps. Hence we denote\nby $\\mathcal{G}$ periodic orbits $\\gamma$ of the flow, by $T_\\gamma$ the period of $\\gamma$ and by $T_\\gamma^\\#$ the primitive period. We let $\\mathcal{P}_\\gamma$ be the linearized Poincar\\'{e} map -- see \\S \\ref{pr}.\nWith this notation we can state our {\\em local trace formula}:\n\\begin{thm}\n\\label{thm1}\nFor any $A>0$ there exists\na distribution $F_A\\in\\mathcal {S}'(\\mathbb{R})$ supported in $[0,\\infty)$ such that\n\\begin{equation}\n\\label{localtrace}\n\\sum_{\\mu\\in\\Res(P), \\Im\\mu>-A}e^{-i\\mu t}+F_A(t)\n=\\sum_{\\gamma\\in\\mathcal{G}}\\frac{T_\\gamma^{\\#}\\delta(t-T_\\gamma)}\n{|\\det(I-\\mathcal{P}_\\gamma)|},\\;\\;\\;\\; t>0\n\\end{equation}\nin the sense of distribution on $(0,\\infty)$.\nMoreover, the Fourier transform of $ F_A $ has an analytic extension\nto $\\Im\\lambda < A$ which satisfies,\n\\begin{equation}\n\\label{error}\n|\\widehat{F}_A(\\lambda) |=\\mathcal{O}_{A,\\epsilon}( \\langle \\lambda \\rangle^{2n+1}), \\ \\\n\\Im\\lambda < A - \\epsilon, \\text{ for any $\\epsilon>0$.}\n\\end{equation}\n\\end{thm}\n\nThe trace formula \\eqref{localtrace} can be motivated as follows.\nFor the case of geodesic flows of compact Riemann surfaces,\n$X=S^*(\\Gamma\\backslash\\mathbb{H}^2)$, where $ \\Gamma $ is\nco-compact subgroup of $ {\\rm{SL}}_2 ( \\mathbb R) $,\nand $\\varphi_t$ is the geodesic flow, we have a global trace formula:\n\\begin{equation}\n\\label{globaltrace}\n\\sum_{\\mu\\in\\Res(P)}e^{-i\\mu t}=\\sum_{\\gamma\\in\\mathcal{G}}\\frac{T_\\gamma^\\#\\delta(t-T_\\gamma)}{|\\det(I-\\mathcal{P}_\\gamma)|},\\;\\;\\; t>0.\n\\end{equation}\nHere the set of resonances is given by\n$\\Res(P)=\\{\\mu_{j,k}=\\lambda_j-i(k+\\frac{1}{2}),j,k\\in\\mathbb{N}\\}$ (up to exceptional values on the imaginary axis),\nwhere $\\lambda_j$'s are the eigenvalues\nof the Laplacian on $ \\Gamma \\backslash \\mathbb H^2 $. This follows\nfrom the Atiyah--Bott--Guillemin trace formula and\nthe Selberg trace formula -- see \\cite{DFG} and references given there.\n{ The bound $ \\langle \\lambda \\rangle^{ 2 n +1 } $ in \n\\eqref{error} is probably not\noptimal and comes from very general estimates presented in \\S \\ref{estflt}.\nIt is possible that \\eqref{globaltrace} is valid for all Anosov flows.}\n\n\n\nMelrose's Poisson formula for\nresonances valid for Euclidean infinities \\cite{Me,SZ,Z1} and some\nhyperbolic infinities \\cite{GZ} suggests that \\eqref{globaltrace}\ncould be valid for general Anosov flows but that is unknown.\n\nIn general, the validity of \\eqref{globaltrace} follows from, but is not equivalent to, the finite order (as an entire function) of the analytic continuation of\n\\begin{equation}\n\\label{zeta}\n\\zeta_1(\\lambda) { :=} \\exp\\left(-\\sum_\\gamma\\frac{T_\\gamma^\\#e^{i\\lambda T_\\gamma}}{T_\\gamma|\\det(I-\\mathcal{P}_\\gamma)|}\\right).\n\\end{equation}\nThis finite order property is only known under certain analyticity assumptions on $X$ and $\\varphi_t$ -- see Rugh \\cite{Ru} and Fried \\cite{Fr}. { \nThe notation $ \\zeta_1 $ is motivated by the factorization of the Ruelle\nzeta function -- see \\cite[(2.5)]{DZ}.}\n\nAs a consequence of the local trace formula \\eqref{localtrace},\nwe have the following weak lower bound on the number of resonances in a sufficiently wide strip near the real axis. It is formulated using the Hardy-Littlewood notation: $f=\\Omega(g)$ if it is\nnot true that $|f|=o(|g|)$.\n\n\\begin{thm}\n\\label{thm2}\nFor every $\\delta\\in(0,1)$ there exists a constant $A_\\delta>0$ such that if $A>A_\\delta$, then\n\\begin{equation}\n\\label{lowerbound}\n\\#(\\Res(P)\\cap\\{\\mu \\in \\mathbb C \\, : \\, |\\mu|\\leq r, \\ \\Im\\mu>-A\\})=\\Omega(r^\\delta).\n\\end{equation}\nIn particular, there are infinitely many resonances in any strip $\\Im\\mu>-A$ for $A$ sufficiently large. \n\\end{thm}\n\n\\medskip\n\\noindent{\\bf Remarks.} 1. { An explicit bound for the constant $A_\\delta$ is given by \\eqref{adelta} in the proof. This also gives an explicit bound $A_0=\\inf\\{A_\\delta:0<\\delta<1\\}$ for the essential spectral gap.} In the case of analytic semiflows (see \\cite{Naud1}) Fr\\'ederic Naud \\cite{Naud} pointed out that a better estimate of the essential spectral gap is possible: there are infinitely many resonances in any strip\n$ \\Im \\lambda > - \\frac32 P ( 2 ) - \\epsilon $, where \n{ $ P (s ) := P ( s \\psi^u ) $ is the\ntopological pressure associated to the unstable Jacobian -- see \\eqref{eq:press} and \\eqref{eq:psiu}}.\nIn Appendix \\ref{weakmix}, Fr\\'ed\\'eric Naud shows how similar methods and Theorem \\ref{thm1} give\na narrower strip with infinitely many resonances for weakly mixing\nAnosov flows.\n\n\\noindent\n2. In the case of flows obtained by suspending Anosov maps the \ngrowth of the number of resonances in strips is linear -- see\nAppendix \\ref{suspe} by Fr\\'ederic Naud \nfor a detailed discussion of analytic perturbations of linear \nmaps. That means that the exponent $ \\delta $ close to one is\nclose to be optimal in general.\n\n\\medskip\n\n\nThe proof of Theorem \\ref{thm1} uses the microlocal approach to\nAnosov dynamics due to Faure--Sj\\\"ostrand \\cite{FaSj} and\nDyatlov--Zworski \\cite{DZ}. In particular we use the fact that\n\\begin{equation*}\n\\frac{d}{d\\lambda} \\log \\zeta_1 ( \\lambda ) = \\tr^\\flat e^{ i \\lambda t_0 } \\varphi_{-t_0}^* ( P - \\lambda)^{-1} ,\n\\end{equation*}\nand that the right hand side continues meromorphically with poles\nwith integral residues. Here the flat trace, $ \\tr^\\flat $, is defined using a formal integration over the diagonal, see \\S \\ref{flat}, with the justification provided by the crucial wave front set relation, see \\S \\ref{wavefront}. Some of the techniques are also related to the proof of Sj\\\"{o}strand's local trace formula for scattering resonances in the semiclassical limit \\cite{S}. It is possible that an alternative\nproof of Theorem \\ref{thm1} could be obtained using the methods of Giulietti--Liverani--Pollicott \\cite{GLP} employed in their proof of Smale's conjecture about zeta function (\\cite{DZ} provided a simple microlocal proof of that conjecture).\n\nThe proof of Theorem \\ref{thm2} is based on the proof of a similar\nresult in Guillop\\'{e}--Zworski \\cite{GZ} which in turn was inspired\nby the work of Ikawa \\cite{Ik} on existence of resonances in\nscattering by several convex bodies.\n\n\\def\\smallsection#1{\\smallskip\\noindent\\textbf{#1}.}\n\n\\smallsection{Acknowledgements}\nWe would like to thank Semyon Dyatlov for helpful discussions and in particular for suggesting the decomposition \\eqref{decomposition} which simplified the wave front arguments. We are also grateful to Fr\\'ed\\'eric Naud for sharing his\nunpublished work \\cite{Naud} with us and to the anonymous referee for\nuseful suggestions. This material is based\nupon work supported by\nthe National Science Foundation under the grant and DMS-1201417.\n\n\\smallsection{Notation} We use the following notational\nconventions: $ \\langle x \\rangle := ( 1 + |x|^2 )^{\\frac12} $,\n$ \\langle u , \\varphi \\rangle$, for the the distributional\npairing of $ u \\in \\mathcal D' ( X ) $ (distributions on a compact\nmanifold $ X $), and $ \\langle u , v \\rangle_{ H}$ for\nthe Hilbert space inner product on $ H $.\nWe write $ f = \\mathcal O_\\ell ( g)_B $ to mean that\n$ \\|f \\|_B \\leq C_\\ell g $ where the norm (or any seminorm) is in the\nspace $ B$, and the constant $ C_\\ell $ depends on $ \\ell $. When either $ \\ell $ or $ B $ are absent then the constant is universal or the estimate is scalar, respectively. When $ G = \\mathcal O_\\ell ( g )_{B_1\\to B_2 } $ then the operator $ B : H_1 \\to H_2 $ has its norm bounded by $ C_\\ell g $. By $ \\neigh_U ( \\rho ) $\nwe mean a (small) neighbourhood of $ \\rho$ in the space $ U$.\nWe refer to \\cite{DZ} and \\cite{Z2} for the notational conventions from microlocal\/semiclassical analysis as they appear in the text.\n\n\\section{Preliminaries}\n\\label{pr}\n\\subsection{Anosov flows}\nLet $X$ be a compact Riemannian manifold, $V\\in C^\\infty(X;TX)$ be\na smooth non vanishing vector field and\nand $\\varphi_t=\\exp tV:X\\to X$ the corresponding flow.\n\nThe flow is called an {\\em Anosov flow}\nif the tangent space to $X$ has a continuous decomposition\n$T_xX=E_0(x)\\oplus E_s(x)\\oplus E_u(x)$ which is invariant under the flow: $d\\varphi_t(x)E_\\bullet(X)=E_\\bullet(\\varphi_t(X))$,\n$\\bullet=s,u$, $E_0(x)=\\mathbb{R}V(x)$,\nand satisfies\n\\begin{equation*}\n\\begin{split}\n|d\\varphi_t(x)v|_{\\varphi_t(x)}\\leq Ce^{-\\theta|t|}|v|_x,&\\;\\;\\; v\\in E_u(x), \\ \\ t<0\\\\\n|d\\varphi_t(x)v|_{\\varphi_t(x)}\\leq Ce^{-\\theta|t|}|v|_x,&\\;\\;\\; v\\in E_s(x),\n\\ \\ t>0,\n\\end{split}\n\\end{equation*}\nfor some fixed $C$ and $\\theta>0$.\n\n\\subsection{Anisotropic Sobolev spaces}\nLet us put $P=-iV:C^\\infty(X)\\to C^\\infty(X)$;\nthen the principal symbol of $ P $, $p\\in S^1(T^*X)$ (see\n\\cite[\\S 18.1]{H3} or \\cite[\\S 14.2]{Z2} for this standard notation; an\noverview of semiclassical and microlocal preliminaries needed in this\npaper can be found in \\cite[\\S 2.3]{DZ}) is given by\n$p(x,\\xi)=\\xi(V(x))$ which is homogeneous of degree 1.\nThe Hamilton flow of $ p $ is\nthe symplectic lift of $ \\varphi_t $ to the cotangent bundle:\n$e^{tH_p}(x,\\xi)=(\\varphi_t(x),({}^Td\\varphi_t(x))^{-1}\\xi)$.\nWe can define the dual decomposition $T^\\ast_xX=E^\\ast_0(x)\\oplus E^\\ast_s(x)\\oplus E^\\ast_u(x)$ where $E^\\ast_0(x),E^\\ast_s(x),E^\\ast_u(x)$ are dual to $E_0(x),E_u(x),E_s(x)$, respectively. Then\n\\begin{equation*}\n\\begin{split}\n\\xi\\not\\in E^\\ast_0(x)\\oplus E^\\ast_s(x)\\Rightarrow d(\\kappa(e^{tH_p}(x,\\xi)),\\kappa(E^\\ast_u))\\to0 \\text{ as } t\\to+\\infty\\\\\n\\xi\\not\\in E^\\ast_0(x)\\oplus E^\\ast_u(x)\\Rightarrow d(\\kappa(e^{tH_p}(x,\\xi)),\\kappa(E^\\ast_s))\\to0 \\text{ as } t\\to-\\infty.\n\\end{split}\n\\end{equation*}\nHere $\\kappa:T^\\ast X\\setminus0\\to S^\\ast X := T^*X \/ \\mathbb R_+ $\nis the natural projection.\n\nA microlocal version of anisotropic Sobolev spaces of\nBlank--Keller--Liverani~\\cite{BKL}, Baladi--Tsujii~\\cite{BT}\nand other authors was provided by Faure-Sj\\\"{o}strand \\cite{FaSj}.\nHere we used a simplified version from Dyatlov-Zworski \\cite{DZ}. For that\nwe construct a function $m_G\\in C^\\infty(T^\\ast X\\setminus0;[-1,1])$ which is homogeneous of degree 0, is supported in a small neighbourhood of $E_s^\\ast\\cup E_u^\\ast$ and satisfies\n\\begin{equation*}\nm_G=1 \\text{ near } E_s^\\ast;\\;\\;\\ m_G=-1 \\text{ near } E_u^\\ast;\\;\\;\\ H_pm_G\\leq0 \\text{ everywhere. }\n\\end{equation*}\nNext, we choose a pseudodifferential operator\n$ G\\in\\Psi^{0+}(X)$, { $ \\sigma(G)=m_G(x,\\xi)\\log\\langle\\xi\\rangle $}.\nThen for $ s > 0 $, $\\exp(\\pm sG)\\in\\Psi^{s+}(X)$ -- see \\cite[\\S 8.2]{Z2}. The anisotropic Sobolev spaces are defined as\n\\begin{equation*}\nH_{sG}:=\\exp(-sG)L^2(X), \\ \\ \\ \\|u\\|_{H_{sG}}: =\\|\\exp(sG)u\\|_{L^2}.\n\\end{equation*}\nBy the construction of $G$, we have $H^s\\subset H_{sG}\\subset H^{-s}$.\n\n\\subsection{Properties of Resolvent}\nWe quote the following results about the resolvent of $P$, see \\cite[Propositions 3.1, 3.2]{DZ}:\n\\begin{lem}\nFix a constant $C_0>0$. Then for $s>0$ large enough depending on $C_0$, $P-\\lambda:D_{sG}\\to H_{sG}$ is a Fredholm operator of index 0 in the region $\\{\\Im\\lambda>-C_0\\}$. Here the domain $D_{sG}$ of $P$ is the set of $u\\in H_{sG}$ such that $Pu$ (in the distribution sense) is in $H_{sG}$ and it is a Hilbert space with norm $\\|u\\|_{D_{sG}}^2=\\|u\\|_{H_{sG}}^2+\\|Pu\\|_{H_{sG}}^2$.\n\\end{lem}\n\n\\begin{lem}\nLet $s>0$ be fixed as above. Then there exists a constant $C_1$ depending on $s$, such that for $\\Im\\lambda>C_1$, the operator $P-\\lambda:D_{sG}\\to H_{sG}$ is invertible and\n\\begin{equation}\n\\label{resolvent}\n(P-\\lambda)^{-1}=i\\int_0^\\infty e^{i\\lambda t}\\varphi_{-t}^\\ast dt,\n\\end{equation}\nwhere $\\varphi_{-t}^\\ast=e^{-itP}$ is the pull back operator by $\\varphi_t$. The integral converges in operator norm $H^s\\to H^s$ and $H^{-s}\\to H^{-s}$.\n\\end{lem}\n\nThe analytic Fredholm theory now shows that the resolvent $ \\lambda\n\\mapsto R(\\lambda)=(P-\\lambda)^{-1}:H_{sG}\\to H_{sG}$ forms a meromorphic family of operators with poles of finite rank. In the region $\\Im\\lambda>-C_0$, the Ruelle-Pollicott resonances are defined as the poles of $R(\\lambda)$. They can be described as the meromorphic continuation of the Schwartz kernel of the operator on the right-hand side, thus are independent of the choice of $s$ and the weight $G$.\nThe mapping properties of $ ( P - \\lambda)^{-1}$ and formula \\eqref{resolvent}\nshow that the power spectrum \\eqref{eq:powersp} has a meromorphic\ncontinuation with the same poles. We note here that our definition\n\\eqref{eq:powersp} is different from the definition in \\cite{Rue} but\nthe formula there can be expressed in terms of \\eqref{eq:powersp}.\n\n{We recall the following general upper bounds on the number of resonances from \nFaure--Sj\\\"ostrand \\cite{FaSj}:\n\\begin{prop}\nLet $\\Res(P)$ be the set of Ruelle-Pollicott resonances. Then for any $C_0>0$,\n\\begin{equation}\n\\label{hupperbound}\n\\#(h\\Res(P))\\cap D(1,C_0 h^{\\frac12} )=\\mathcal{O}(h^{-n+ \\frac12}),\n\\end{equation}\nwhich is equivalent to \\eqref{eq:counting}. In particular,\n\\begin{equation}\n\\label{upperbound}\n\\#\\Res(P)\\cap\\{\\mu:|\\Re\\mu|\\leq r,\\Im\\mu>-C_0\\}=\\mathcal{O}(r^n). \n\\end{equation}\n\\end{prop}}\n\n\\subsection{Complex absorbing potentials}\nIt is convenient to introduce a semiclassical parameter $h$ and to consider the operator $hP\\in\\Psi_h^1(X)$ (for the definitions of pseudodifferential\noperators and wave front sets we\nrefer to \\cite[\\S 14.2]{Z2} and \\cite[\\S 2.3, Appendix C]{DZ})\n with semiclassical principal symbol $p=\\sigma_h(hP)(x,\\xi)=\\xi(V_x)$.\nThen we introduce a semiclassical adaption $G(h)\\in\\Psi_h^{0+}(X)$ of the operator $G$ with\n\\begin{equation*}\n\\sigma_h(G(h))=(1-\\chi(x,\\xi))m_G(x,\\xi)\\log|\\xi|,\n\\end{equation*}\nwhere $\\chi\\in C_0^\\infty(T^\\ast X)$ is equal to 1 near the zero section. In this way, $H_{sG(h)}=H_{sG}$ but with a new norm depending on $ h $. We also\ndefine an $h$-dependent norm on the domain of $hP$, $D_{sG(h)} = D_{s G} $:\n\\begin{equation*}\n\\|u\\|_{D_{sG(h)}} :=\\|u\\|_{H_{sG(h)}}+\\|hPu\\|_{H_{sG(h)}}.\n\\end{equation*}\n\nNow we modify $hP$ by adding a semiclassical pseudodifferential complex absorbing potential $-iQ_\\delta\\in\\Psi_h^0(X)$ which is localized to a neighbourhood of the zero section:\n\\begin{equation}\n\\label{eq:WFQd}\n\\WF_h(Q_\\delta)\\subset\\{|\\xi|<\\delta\\}, \\ \\ \\sigma_h(Q_\\delta)>0 \\text{ on } \\{|\\xi|\\leq\\delta\/2\\}, \\ \\ \\sigma_h(Q_\\delta)\\geqslant0 \\text{ everywhere}.\n\\end{equation}\n{ (For the definition of $ \\WF_h ( A ) \\subset \\overline T^* X $ and\nof the compactified cotangent bundle $ \\overline T^* X $, \nsee \\cite[\\S C.2]{DZ}.)}\nInstead of $P_h(z)=hP-z$, we consider the operator $P^\\delta_h(z)=hP-iQ_\\delta-z$ acting on $H_{sG(h)}$ which is equivalent to the conjugated operator\n\\begin{equation}\n\\label{eq:conj}\nP^{\\delta,s}_h(z)=e^{sG(h)}P_\\delta(z)e^{-sG(h)}=P^\\delta_h (z)+s[G(h),hP]+\\mathcal{O}(h^2)_{\\Psi_h^{-1+}}\n\\end{equation}\nacting on $L^2$. We recall the crucial \\cite[Proposition 3.4]{DZ}:\n\\begin{lem}\n\\label{l:3.4}\nFix a constant $C_0>0$ and $\\delta >0$. Then for $s>0$ large enough depending on $C_0$ and $h$ small enough, the operator\n\\begin{equation*}\nP^\\delta_h(z):D_{sG(h)}\\to H_{sG(h)}, \\ \\ \\ -C_0h\\leq\\Im z\\leq1, \\ \\\n |\\Re z|\\leq 2h^{1\/2},\n\\end{equation*}\nis invertible, and the inverse $R^\\delta_h(z)$, satisfies\n$\\|R^\\delta_h(z)\\|_{H_{sG(h)}\\to H_{sG(h)}}\\leq Ch^{-1}$.\n\\end{lem}\n\n\\subsection{Finite rank approximation}\n\\label{fra}\nFor our application we need to make $ Q_\\delta $ a finite rank\noperator.\nIt is also convenient to make a further assumption on the\nsymbol of $ Q_\\delta$.\nAs long as \\eqref{eq:WFQd} holds,\nLemma \\ref{l:3.4} still applies.\n\nFrom now on we fix $ \\delta > 0 $ and put\n\\[ Q = Q_\\delta = f ( - h^2 \\Delta_g ), \\ \\ f \\in {C^\\infty_{\\rm{c}}} ( ( -2 \\delta, 2 \\delta ),\n[ 0 , 1 ]) , \\ \\\nf ( s ) = 1, \\ \\ |s |\\leq \\delta . \\]\nThen (see for instance \\cite[Theorem 14.9]{Z2})\n\\begin{equation}\n\\label{finiterank}\n\\rank Q=\\mathcal O (h^{-n}), \\ \\ Q \\geq 0 , \\ \\ \\sigma_h ( Q ) = f ( |\\xi|_g^2 ).\n\\end{equation}\nFor technical convenience only (so that we can\ncite easily available results in the proof of Proposition \\ref{flattracees}\nin the appendix)\nwe make an additional assumption on $ f$: for some $ 0 < \\alpha < \\frac12$,\n\\begin{equation}\n\\label{eq:condfk}\n| f^{(k)} ( x ) | \\leq C_k f ( x ) ^{1- \\alpha} .\n\\end{equation}\nThis can be achieved by building $ f$ from functions of the form equal to $\ne^{-1\/x} $ for $ x > 0 $ and $ 0 $ for $ x \\leq 0 $. (In that case\n\\eqref{eq:condfk} holds for all $ \\alpha > 0 $.)\n\n\nLemma \\ref{l:3.4} shows that\nfor $-C_0h\\leq\\Im z\\leq 1\n$, $|\\Re z|\\leq 2h^{1\/2}$, \n\\begin{equation}\n\\label{eq:wideP} \n\\widetilde{P}_h(z): =hP-iQ-z, \n\\end{equation} \nis also invertible and its inverse $\\widetilde{R}_h(z)$ satisfies\n\\begin{equation}\n\\label{modifiedresolvent}\n\\|\\widetilde{R}_h(z)\\|_{H_{sG(h)}\\to H_{sG(h)}}\\leq Ch^{-1}.\n\\end{equation}\nIn the upper half plane we have the following estimate on the original resolvent:\n\\begin{equation}\n\\label{eq:orres}\n \\|R_h(z)\\|_{H_{sG(h)}\\to H_{sG(h)}}\\leq Ch^{-1}, \\ \\ \\\nC_1h\\leq\\Im z\\leq1, \\ \\ |\\Re z|\\leq 2h^{1\/2},\n\\end{equation}\nprovided that $ C_1 $ is large enough. This follows from the\nFredholm property and the estimate $ \\Im \\langle e^{ s G ( h ) } P_h ( z )\ne^{ -s G ( h ) } u , u \\rangle_{L^2 }\n\\geq h \\| u\\|_{L^2} $, $ \\Im z > C_1 h $ -- see \\eqref{eq:conj}.\n\n\\subsection{Wavefront set condition}\n\\label{wavefront}\nWe need to study the wavefront set and semiclassical wavefront set of $R_h(z)$ and $\\widetilde{R}_h(z)$. For the definitions and notations of the wavefront sets and the semiclassical wavefront sets, we refer to \\cite[Chapter VIII]{H}, \\cite[Section 8.4]{Z2} and \\cite[Appendix C]{DZ} and \\cite{A}.\n\nWe recall the following wavefront set condition and semiclassical wavefront set conditions for the resolvent $R(\\lambda)$ and $\\widetilde{R}_h(z)$ from \\cite[Proposition 3.3]{DZ}. { (For the definition of the standard wave front set $ \\WF $\nsee \\cite[\\S C.1]{DZ} and for the definition of the twisted \nwave front set $ \\WF' $, \\cite[(C.2)]{DZ} -- the reason for the twist\nis to have $ \\WF ( I ) $ equal to the diagonal in $ T^*X \\times T^* X $.)}\n\\begin{prop}\n\\label{p:3.3}\nLet $C_0$ and $s$ be as above and assume $\\lambda$ is not a resonance with $\\Im\\lambda>-C_0$, then\n\\begin{equation}\n\\label{wavefrontset}\n\\WF'(R(\\lambda))\\subset\\Delta(T^\\ast X)\\cup\\Omega_+\\cup(E_u^\\ast\\times E_s^\\ast),\n\\end{equation}\nwhere $\\Delta(T^\\ast X)$ is the diagonal in $T^\\ast X$ and $\\Omega_+$ is the positive flow-out of $e^{tH_p}$ on $\\{p=0\\}$:\n\\begin{equation}\n\\label{eq:Omegapl}\n\\Omega_+=\\{(e^{tH_p}(x,\\xi),x,\\xi) \\, : \\, t\\geqslant0, \\ p(x,\\xi)=0\\}.\n\\end{equation}\nAlso, if $R_h(z)=h^{-1}R(z\/h)$, then\n\\begin{equation}\n\\label{semiwf1}\n\\WF_h'(R_h(z))\\cap T^\\ast(X\\times X)\\subset\\Delta(T^\\ast X)\\cup\\Omega_+\\cup(E_u^\\ast\\times E_s^\\ast),\n\\end{equation}\nand\n\\begin{equation}\n\\WF_h'(R_h(z))\\cap S^\\ast(X\\times X)\\subset\\kappa(\\Delta(T^\\ast X)\\cup\\Omega_+\\cup(E_u^\\ast\\times E_s^\\ast)\\setminus\\{0\\}).\n\\end{equation}\n\\end{prop}\n\nNow we determine the wavefront set and the semiclassical wavefront set of $\\widetilde{R}_h(z)$. First, \n{ by inserting the resolvent formula\n$$\\widetilde{R}_h(z)=R_h(z)+iR_h(z)Q\\tilde{R}_h(z)$$\ninto another resolvent formula\n$$\\widetilde{R}_h(z)=R_h(z)+i\\tilde{R}_h(z)QR_h(z),$$}\nwe write\n$$\\widetilde{R}_h(z) = R_h ( z ) + i R_h ( z ) Q R_h ( z ) -\nR_h ( z ) Q \\widetilde{R}_h ( z ) Q R_h ( z ).$$\nThen since $Q$ is a smoothing operator, $\\WF(Q)=\\emptyset$, we have\n$$\\WF'(R_h(z)QR_h(z))\\subset E_u^\\ast\\times E_s^\\ast.$$\nSimilarly, since $Q\\widetilde{R}_h(z)Q$ is also a smoothing operator,\n$$\\WF'(R_h(z)Q\\widetilde{R}_h(z)QR_h(z))\\subset E_u^\\ast\\times E_s^\\ast.$$\nTherefore we get the same wavefront set condition as $R_h(z)$:\n\\begin{equation}\n\\WF'(\\widetilde{R}_h(z))\\subset\\Delta(T^\\ast X)\\cup\\Omega_+\\cup(E_u^\\ast\\times E_s^\\ast).\n\\end{equation}\n\nFor the semiclassical wavefront set, we already know from \\cite[Proposition 3.4]{DZ} that\n\\begin{equation}\n\\label{eq:WFRh} \\WF_h'(\\widetilde{R}_h(z))\\cap T^\\ast(X\\times X)\\subset\\Delta(T^\\ast X)\\cup\\Omega_+.\n\\end{equation}\nMoreover, since $\\WF_h'(Q)\\cap S^\\ast(X\\times X)=\\emptyset$, we have\n$$\\WF_h'(R_h(z)QR_h(z))\\subset E_u^\\ast\\times E_s^\\ast,$$\nand similarly, $\\WF_h'(Q\\widetilde{R}_h(z)Q)\\cap S^\\ast(X\\times X)=\\emptyset$. Therefore\n\\begin{equation}\n\\label{eq:WFhR}\n\\WF_h'(\\widetilde{R}_h(z))\\cap S^\\ast(X\\times X)\\subset\n\\kappa(\\Delta(T^\\ast X)\\cup\\Omega_+\\cup(E_u^\\ast\\times E_s^\\ast)\\setminus\\{0\\}).\n\\end{equation}\n\n\\subsection{Flat trace}\n\\label{flat}\nConsider an operator $B:C^\\infty(X)\\to\\mathcal{D}'(X)$ with\n\\begin{equation}\\label{ftc}\n\\WF'(B)\\cap\\Delta(T^\\ast X)=\\emptyset.\n\\end{equation}\nThen we can define the flat trace of $B$ as\n\\begin{equation}\n\\label{eq:flat}\n\\tr^\\flat B=\\int_X(\\iota^\\ast K_B)(x)dx:=\\langle\\iota^\\ast K_B,1\\rangle\n\\end{equation}\nwhere $\\iota:x\\mapsto (x,x)$ is the diagonal map, $K_B$ is the Schwartz kernel of $X$ with respect to the density $dx$ on $X$. The pull back $\\iota^\\ast K_B\\in\\mathcal{D}'(X)$ is well-defined under the condition \\eqref{ftc} (see \\cite[Section 8.2]{H}).\n\n\\subsection{Dynamical zeta function and Guillemin's trace formula}\nThe zeta function $ \\zeta_1 $ defined in \\eqref{zeta}\nis closely related to the Ruelle zeta function -- see \\cite{GLP},\\cite{DZ}\nand references given there.\nThe right hand side in \\eqref{zeta} converges for $\\Im\\lambda>C_1$ and\nit continues analytically to the entire plane.\n The Pollicott-Ruelle resonances are exactly the zeros of $ \\zeta_1 $.\n We recall the (Atiyah--Bott--)Guillemin's trace formula \\cite{Gu} (see \\cite[Appendix B]{DZ} for a proof):\n\\begin{equation}\n\\label{Guillemin}\n\\tr^\\flat e^{-itP}=\\sum_{\\gamma\\in\\mathcal{G}}\\frac{T_\\gamma^\\#\\delta(t-T_\\gamma)}{|\\det(I-\\mathcal{P}_\\gamma)|}, \\ \\ \\ t>0.\n\\end{equation}\nTherefore we have\n\\begin{equation*}\n\\frac{d}{d\\lambda}\\log\\zeta_1(\\lambda)=\\frac{1}{i}\\sum_\\gamma\\frac{T_\\gamma^\\# e^{i\\lambda T_\\gamma}}{|\\det(I-\\mathcal{P}_\\gamma)|}\n=\\frac{1}{i}\\int_0^\\infty e^{it\\lambda}\\tr^\\flat e^{-itP}dt.\n\\end{equation*}\nFrom \\eqref{Guillemin}, $\\tr^\\flat e^{-itP}=0$ on $(0,t_0)$ if $t_0<\\inf\\{T_\\gamma:\\gamma\\in\\mathcal{G}\\}$. Formally, we can write (see \\cite[\\S 4]{DZ} for the\njustification)\n\\begin{equation*}\n\\frac{d}{d\\lambda}\\log\\zeta_1(\\lambda)\n=\\frac{1}{i}\\int_{t_0}^\\infty e^{it\\lambda}\\tr^\\flat e^{-itP}dt\n=\\tr^\\flat\\left(\\frac{1}{i}e^{-it_0(P-\\lambda)}\n\\int_0^\\infty e^{it\\lambda}e^{-itP}dt\\right).\n\\end{equation*}\nTherefore by \\eqref{resolvent} we have\n\\begin{equation}\n\\label{zetaresolvent}\n\\frac{d}{d\\lambda}\\log\\zeta_1(\\lambda)=\\tr^\\flat(e^{-it_0(P-\\lambda)}(P-\\lambda)^{-1}).\n\\end{equation}\nThe wavefront set condition \\eqref{wavefrontset} shows that\n\\begin{equation*}\n\\begin{split}\n& \\WF'(e^{-it_0(P-\\lambda)}(P-\\lambda)^{-1})\\subset\\\\\n& \\ \\ \\ \\ \\ \\ \\{((x,\\xi),(y,\\eta)) \\; :\\;\n(e^{-t_0H_p}(x,\\xi),(y,\\eta))\\in\\Delta(T^\\ast X)\\cup\\Omega_+\\cup(E_u^\\ast\\times E_s^\\ast)\\}\n\\end{split}\n\\end{equation*}\nwhich does not intersect $\\Delta(T^\\ast X)$. This justifies taking the\nflat trace \\eqref{eq:flat}.\n\nTherefore $\\frac{d}{d\\lambda} \\log\\zeta_1$ has a meromorphic continuation to all of $\\mathbb{C}$ with simple poles and\npositive integral residues. That is equivalent to having a holomorphic\ncontinuation of $ \\zeta_1 $. This strategy for proving Smale's conjecture\non the meromorphy of Ruelle zeta functions is the starting point of\nour proof of the local trace formula.\n\n\\section{Estimates on flat traces}\n\\label{estflt}\n\nThe key step in the proof of the trace formula is the following estimate on the flat trace of the propagated resolvent.\n\n\\begin{prop}\n\\label{flattracees}\nLet $\\widetilde{P}_h(z)$ and $\\widetilde{R}_h(z)$ { be given by \n\\eqref{eq:wideP} and \\eqref{modifiedresolvent},} and let $t_0\\in(0,\\inf T_\\gamma)$. Then\n\\begin{equation}\n\\label{eq:Tofz} T(z ) := \\tr^\\flat(e^{-it_0h^{-1}\\widetilde{P}_h(z)}\\widetilde{R}_h(z)),\n\\end{equation}\nis well defined and holomorphic in $ z $ when $ - C_0 h \\leq \\Im z \\leq 1, $\n$ |\\Re z | \\leq C_1 h^{\\frac12} $. Moreover, in that range of $ z $,\n\\begin{equation}\n\\label{eq:flattr1}\n T ( z ) = \\mathcal O_{C_0, C_1 } (h^{-2n-1}).\n\\end{equation}\n\\end{prop}\n\nThe proof is based on a quantitative study of the proof of \\cite[Theorem 8.2.4]{H} and on the wave front properties established\nin \\cite[\\S 3.3,3.4]{DZ}. The general idea is the following: the wave\nfront set condition shows that the trace is well defined. The analysis\nbased on the properties of the semiclassical wave front set shows more:\nthe contribution from a microlocal neighbourhood of fiber infinity is $ \\mathcal O ( h^\\infty ) $. The contribution away from fiber infinity can be controlled\nusing the norm estimate on $ \\widetilde R_h ( z ) $. Since the weights\ndefining the $ H_{sG} $ spaces are supported near infinity, the norm\nestimates are effectively $ L^2 $ estimates.\n\nFor the proof of \\eqref{eq:flattr1} we first review\nthe construction of the flat trace under the wave front set\ncondition. Suppose that $ u \\in \\mathcal D' ( X \\times X ) $\nsatisfies the (classical) wave front condition\n\\begin{equation}\n\\label{eq:wfcond} \\WF ( u ) \\cap N^* \\Delta ( X ) = \\emptyset, \\ \\ \\Delta ( X )\n= \\{ ( x, x ) : x\\in X \\} \\subset X \\times X.\n\\end{equation}\nIf $ u $ is a Schwartz kernel of an operator $ T $ then\n$ \\tr^\\flat T := \\langle \\iota^* u , 1 \\rangle $, where $ \\iota :\n\\Delta ( X ) \\hookrightarrow X \\times X $. We will recall why \\eqref{eq:wfcond}\nallows the definition $ \\iota^* u $.\nFor any $x_0\\in X$, we can choose a neighbourhood $U$ of $x_0$ in $X$ equipped with a local coordinate patch. For simplicity, we abuse the notation and assume $x_0\\in U\\subset \\mathbb{R}^n$. Then $\\iota(x_0)=(x_0,x_0)\\in U\\times U\\subset \\mathbb{R}^n\\times\\mathbb{R}^n$. The conormal bundle to the diagonal is locally given by\n$$N_\\iota=\n\\{(x,x,\\xi,-\\xi)\\in ( U\\times U) \\times\n( \\mathbb{R}^n\\times \\mathbb{R}^n) \\}.$$\nPut $ \\Gamma := \\WF ( u ) $ and\n$ {\\Gamma}_{(x,y)}=\\{(\\xi,\\eta) : (x, y , \\xi, \\eta )\\in\\Gamma\\}$. Then\n\\[ {\\Gamma}_{(x_0,x_0)}\\cap\\{(\\xi,-\\xi):\\xi\\in\\mathbb{R}^n,\\xi\\neq0\\}=\\emptyset.\n\\]\nSince ${\\Gamma}_{(x_0,x_0)}$ is closed, we can find a conic neighbourhood, $V$, of $ {\\Gamma}_{(x_0,x_0)}$ in $\\mathbb{R}^n\\times\\mathbb{R}^n\\setminus0$ such that\n$$V\\cap\\{(\\xi,-\\xi):\\xi\\in\\mathbb{R}^n,\\xi\\neq0\\}=\\emptyset.$$\nWe can also find a compact neighbourhood $Y_0$ of $(x_0,x_0)$ such that $V$ is a neighbourhood of ${\\Gamma}_{(x,y)}$ for every $(x,y)\\in Y_0$.\nNext we choose a\nneighbourhood $X_0$ of $x_0$ such that $X_0\\times X_0\\Subset Y_0$. Then we have for every $x\\in X_0, (\\xi,\\eta)\\in V$,\n\\begin{equation}\n\\label{eq:nonstat} {}^t\\iota'(x)\\cdot(\\xi,\\eta)=\\xi+\\eta\\neq0.\n\\end{equation}\n\nMoreover, we can choose $ V$ so that its complement, $\\complement V $, is\na small conic neighbourhood of $\\{(\\xi,-\\xi):\\xi\\in\\mathbb{R}^n,\\xi\\neq0\\}$. In particular\nthere exists a constant $C>0$ such that in ${\\complement V}$, $C^{-1}|\\eta|\\leq|\\xi|\\leq C|\\eta|$. We can also assume that\n\\begin{equation}\n\\label{eq:CV}\n\\complement V = - \\complement V .\n\\end{equation}\n\nFinally we choose $\\psi(x)\\in C^\\infty(U)$ equal to 1 on $X_0$ such that $\\varphi(x,y)=\\psi(x)\\psi(y)\\in C_0^\\infty(Y_0)$, then for any $\\chi\\in C_0^\\infty(X_0)$, $u\\in C^\\infty(X\\times X)$, we have\n\\begin{equation}\n\\label{eq:extend} \\langle\\iota^\\ast u,\\chi\\rangle =\\langle\\iota^\\ast(\\varphi u),\\chi\\rangle\n=(2\\pi)^{-2n}\\int\\widehat{\\varphi u}(\\xi,\\eta)I_\\chi(\\xi,\\eta)d\\xi d\\eta,\n\\end{equation}\nwhere\n$$I_\\chi(\\xi,\\eta)=\\int \\chi(x)e^{i\\langle\\iota(x),(\\xi,\\eta)\\rangle}dx\n=\\int\\chi(x)e^{ix\\cdot(\\xi+\\eta)}dx.$$\nWe claim that as long as \\eqref{eq:wfcond} holds the right hand\nside of \\eqref{eq:extend} is well defined and hence the pull back\n$ \\iota^* u $ is a well defined distribution.\n\nTo see this, we first notice that if $(\\xi,\\eta)\\in V$, then\n\\eqref{eq:nonstat} shows that the phase is not stationary and hence,\n$|I_\\chi(\\xi,\\eta)|\\leq C_{N,\\chi}(1+|\\xi|+|\\eta|)^{-N} $, for all $N$.\nOn the other hand, we have\n\\begin{equation}\n\\label{eq:widehat} |\\widehat{\\varphi u}(\\xi,\\eta)|=\\left|\\int\\psi(x)\\psi(y)u(x,y)e^{-i(x\\cdot\\xi+y\\cdot\\eta)}dxdy\\right|.\\end{equation}\nThe construction of $ V $ and \\eqref{eq:wfcond} imply that if $(\\xi,\\eta)\\not\\in V$, then\n$|\\widehat{\\varphi u}(\\xi,\\eta)|\\leq C_N(1+|\\xi|+|\\eta|)^{-N}$, for all $ N$.\nWhen $ ( \\xi, \\eta ) \\in V $ then, there exists $M>0$ such that\n$|\\widehat{\\varphi u}(\\xi,\\eta)|\\leq C_N(1+|\\xi|+|\\eta|)^M$.\nTherefore $\\langle\\iota^\\ast u,\\chi\\rangle$ is well defined. Now to define $\\langle\\iota^\\ast u,1\\rangle$, we first choose a finite partition of unity $1=\\sum\\chi_j$ where $\\chi_j$ is constructed as above for some $x_j\\in X$ (playing the role of $x_0$)\nand then choose the corresponding $\\psi_j$'s (playing the role of $ \\psi $).\nThis concludes our review of the proof\nthat\n$ \\tr^\\flat T = \\langle \\iota^* u , 1\\rangle $ is well defined when \\eqref{eq:wfcond}\nholds.\n\nAll of this can be applied to $ u = K$, the\nSchwartz kernel of $e^{-it_0h^{-1}\\widetilde{P}_h(z)}\\widetilde{R}_h(z)$,\nwith quantitative bounds in terms of $ h $.\nWe first estimate the wave front set of\n$e^{-it_0h^{-1}\\widetilde{P}_h(z)}\\widetilde{R}_h(z)$. For that we\nneed the following\n\\begin{lem}\n\\label{l:prop}\nFor $ t \\geq 0 $,\n\\begin{equation*}\n\\begin{split}\n \\WF_h' ( e^{ - i t h^{-1} \\widetilde P_h ( z ) } )\n\\cap T^\\ast(X\\times X)&\n\\subset \\{ ( e^{ t_0 H_p } ( x, \\xi ) , ( x, \\xi ) ) : ( x ,\\xi ) \\in T^\\ast X \\},\\\\\n\\WF_h' ( e^{ - i t h^{-1} \\widetilde P_h ( z ) } )\n\\cap S^\\ast(X\\times X)&\n\\subset \\kappa(\\{ ( e^{ t_0 H_p } ( x, \\xi ) , ( x, \\xi ) ) : ( x ,\\xi ) \\in T^\\ast X\\setminus\\{0\\} \\}).\n\\end{split}\n\\end{equation*}\n\\end{lem}\n\\begin{proof}\nWe first note that the inclusion is obviously true for the\n$ \\WF_h' ( e^{ - i t P } ) $ since the operator is the pull back by\n$ \\varphi_{-t}^* $. Hence the statement above will follow from showing\nthat $ V ( t ) := e^{ i t P} e^{ - i t h^{-1} \\widetilde P_h ( z ) } $ is a pseudodifferential\noperator. If $ B \\in \\Psi^0_h $ satisfies\n\\[ \\WF_h ( B ) \\cap\n\\cup_{ 0 \\leq |t'| \\leq t }\\, e^{t'H_p} (\\WF_h ( Q ) ) = \\emptyset , \\]\nthen\n$ B e^{ i t P} e^{ - i t h^{-1} ( h P - i Q ) } = B + {\\mathcal O} ( h^\\infty )_{\n\\mathcal D' \\to {C^\\infty} } $.\n\nIn fact, we can use Egorov's theorem (a trivial case since $ e^{ it P } =\n\\varphi_t ^* $) to see that\n\\begin{equation}\n\\begin{split}\n h D_t \\left( B e^{ i t P} e^{ - i t h^{-1} ( h P - i Q ) } \\right) & =\ni B e^{ it P } Q e^{ - i t h^{-1} ( h P - i Q ) } \\\\\n& = i e^{ i t P } B ( t ) Q e^{ - i t h^{-1} ( h P - iQ ) } =\n\\mathcal O ( h^\\infty )_{ \\mathcal D' \\to {C^\\infty} } ,\n\\end{split}\n\\end{equation}\nwhere $ B ( t ) := e^{- i t P } B e^{ i t P } $ satisfies\n$ \\WF_h ( B ( t ) ) \\cap \\WF_h ( Q ) = \\emptyset $.\nBy switching the sign of $ P $ and taking adjoints we see\nthat the we also have\n$$ e^{ i t P} e^{ - i t h^{-1} ( h P - i Q ) } B = B + {\\mathcal O} ( h^\\infty )_{\n\\mathcal D' \\to {C^\\infty} } .$$\nHence it is enough to prove that, for $ \\alpha $ in \\eqref{eq:condfk},\n$ e^{ i tP } e^{ - i h^{-1} t ( P - i Q ) } A \\in \\Psi_\\alpha ( X ) $,\n$ \\alpha < {\\frac12} $, for $ A \\in \\Psi^{\\rm{comp}}( X ) $. But that\nis included in \\cite[Proposition { A}.3]{NZ}.\n\\end{proof}\n\n\\medskip\n\\noindent\n{\\bf Remark.} { \nThe assumption \\eqref{eq:condfk} in the construction of $ \\widetilde P_h ( z ) $ and used in the proof of Lemma \\ref{l:prop}\nis made for convenience only as we can then cite\n\\cite[Proposition { A}.3]{NZ}. }\n\n\\medskip\n\nInclusions \\eqref{eq:WFRh} and \\eqref{eq:WFhR} and\nLemma \\ref{l:prop} show that\n\\begin{equation*}\n\\begin{split}\n& \\WF_h'(e^{-it_0h^{-1}\\widetilde{P}_h(z)}\\widetilde{R}_h(z))\n\\cap T^\\ast(X\\times X)\\subset\\\\\n& \\ \\ \\ \\ \\ \\ \\{((x,\\xi),(y,\\eta)) \\; :\\;\n(e^{-t_0H_p}(x,\\xi),(y,\\eta))\\in\\Delta(T^\\ast X)\\cup\\Omega_+\\}\n\\end{split}\n\\end{equation*}\nand\n\\begin{equation*}\n\\begin{split}\n& \\WF_h'(e^{-it_0h^{-1}\\widetilde{P}_h(z)}\\widetilde{R}_h(z))\n\\cap S^\\ast(X\\times X)\\subset\\\\\n& \\ \\ \\ \\ \\ \\ \\kappa\\{((x,\\xi),(y,\\eta)) \\; :\\;\n(e^{-t_0H_p}(x,\\xi),(y,\\eta))\\in\\Delta(T^\\ast X)\\cup\\Omega_+\\cup(E_u^\\ast\\times E_s^\\ast)\\setminus\\{0\\}\\}.\n\\end{split}\n\\end{equation*}\nIn particular, for all $0 < h < 1 $,\n\\begin{equation*}\n\\begin{split}\n& \\WF'(e^{-it_0h^{-1}\\widetilde{P}_h(z)}\\widetilde{R}_h(z))\\subset\\\\\n& \\ \\ \\ \\ \\ \\ \\{((x,\\xi),(y,\\eta)) \\; :\\;\n(e^{-t_0H_p}(x,\\xi),(y,\\eta))\\in\\Delta(T^\\ast X)\\cup\\Omega_+\\cup(E_u^\\ast\\times E_s^\\ast)\\},\n\\end{split}\n\\end{equation*}\n{ satisfying \\eqref{ftc},} that is, does not intersect with $\\Delta(T^\\ast X)$ and hence $\\tr^\\flat(e^{-it_0h^{-1}\\widetilde{P}_h(z)}\\widetilde{R}_h(z))$ is well-defined.\n\n\nUsing a microlocal partition of unity,\n$ I=\\sum_{j=1}^J B_j + \\mathcal O ( h^\\infty )_{ \\mathcal D'\n\\to {C^\\infty} } $, $ B_j\\in\\Psi^0_h(X)$ { (see for instance \\cite[Proposition E.34]{res})}\nwe only need to prove that\n\\[\n\\begin{split}\n& {\\rm(i)} \\WF_h(B) \\subset \\neigh_{ T^* X } ( x_0, \\xi_0 ) , \\\n(x_0,\\xi_0)\\in T^\\ast X \\Rightarrow\n\\tr^\\flat e^{-it_0h^{-1}\\widetilde{P}_h(z)}\\widetilde{R}_h(z) B\n= \\mathcal O ( h^{-{ 2 } n-1} ) , \\\\\n& {\\rm(ii)} \\WF_h(B) \\subset \\neigh_{ \\overline T^* X } ( x_0, \\xi_0 ) , \\\n(x_0,\\xi_0)\\in S^\\ast X \\Rightarrow\n\\tr^\\flat e^{-it_0h^{-1}\\widetilde{P}_h(z)}\\widetilde{R}_h(z) B = \\mathcal O ( h^{\\infty} ) ,\n\\end{split} \\]\nIn case (ii), $ W :=\\neigh_{ \\overline T^* X } ( x_0, \\xi_0 ) $ is the image of\nthe closure of a conic neighbourhood of $(x_0,\\xi_0)$ in $T^\\ast X$, under\nthe map $ T ^* X \\to \\overline T^* X $.\n{ In fact, given (i) and (ii), we can use a microlocal partition of unity to write\n$$ e^{-it_0h^{-1}\\widetilde{P}_h(z)}\\widetilde{R}_h(z)=\\sum_{j=1}^J\n e^{-it_0h^{-1}\\widetilde{P}_h(z)}\\widetilde{R}_h(z) B_j+O(h^\\infty)_{\\mathcal{D}'\\to {C^\\infty}}$$\nwhere each $B_j$ satisfies either (i) or (ii) and this proves \n\\eqref{eq:flattr1}.}\n\nFor each case, we repeat the construction with the Fourier transform replaced by the semiclassical Fourier transform. Let\n$u=K_h$ be the Schwartz kernel\nof $e^{-it_0h^{-1}\\widetilde{P}_h(z)}\\widetilde{R}_h(z)B$.\nThen, in the notation of \\eqref{eq:extend},\n\\begin{equation}\n\\label{eq:iota}\n\\langle\\iota^\\ast u,\\chi\\rangle =\\langle\\iota^\\ast(\\varphi u),\\chi\\rangle\n=(2\\pi h)^{-2n}\\int\\mathcal{F}_h(\\varphi u)(\\xi,\\eta)I_{\\chi,h}(\\xi,\\eta)d\\xi d\\eta,\n\\end{equation}\nwhere now\n\\begin{equation}\n\\label{eq:Ichih} I_{\\chi,h}(\\xi,\\eta)=\\int\\chi(x)e^{i\\langle\\iota(x),(\\xi,\\eta)\\rangle\/h}dx\n=\\int\\chi(x)e^{ix\\cdot(\\xi+\\eta)\/h}dx. \n\\end{equation}\n\n If $\\WF_h(B)$ is contained in a small compact neighbourhood $W$ of $(x_0,\\xi_0)$, we can assume in the partition of unity $1=\\sum\\chi_j$ (see the\nargument following \\eqref{eq:widehat}), $\\pi(W)\\subset X_0$ for some coordinate patch $X_0$ and $\\pi(W)\\cap\\supp\\chi_j=\\emptyset$ except for the one in this coordinate patch, say $\\psi=\\psi_0$. For $j\\neq0$,\nsince\n\\begin{equation*}\n\\WF_h'(\\varphi_j u)\\subset\\WF_h' ( u ) \\cap[(\\overline{T}^\\ast X)\\times\\WF_h(B)]\n\\cap[(\\overline{T}^\\ast\\supp\\psi_j )\\times(\\overline{T}^\\ast\\supp\\psi_j)]=\\emptyset,\n\\end{equation*}\nwe have\n\\begin{equation*}\n \\mathcal{F}_h(\\varphi_j u)(\\xi,\\eta)=\\mathcal{O}(h^\\infty(1+|\\xi|+|\\eta|)^{-\\infty}),\n\\end{equation*}\nand thus\n$ \\langle\\iota^\\ast u,\\chi_j \\rangle=\\mathcal{O}(h^\\infty)$.\nTherefore we only need to consider the coordinate patch $X_0$ centered at $x_0$ and the corresponding $\\chi,\\psi$ constructed as before.\nWe note that $ I_{\\chi, h } (\\xi, \\eta ) = \\mathcal O (h^\\infty(1+|\\xi|+|\\eta|)^{-\\infty})$ uniformly for $(\\xi,\\eta)\\in V$ ($ I_{\\chi, h } $ is defined in \\eqref{eq:Ichih}\nand again we use the notation introduced before \\eqref{eq:extend}).\nHence we only need to to estimate\n\\begin{equation*}\n\\left|\\int_{{\\complement V}}\\mathcal{F}_h(\\varphi u)(\\xi,\\eta)I_{\\chi,h}(\\xi,\\eta)d\\xi d\\eta\\right|\\leq\\int_{{\\complement V}}|\\mathcal{F}_h(\\varphi u)(\\xi,\\eta)|d\\xi d\\eta.\n\\end{equation*}\nHere\n\\begin{equation}\n\\begin{split}\n\\mathcal{F}_h(\\varphi u)(\\xi,\\eta)\n=&\\int \\psi(x)\\psi(y)u(x,y)e^{-i(x\\cdot\\xi+y\\cdot\\eta)\/h}dxdy\\\\\n=&\\langle e^{-it_0h^{-1}\\widetilde{P}_h(z)}\\widetilde{R}_h(z)B(\\psi(y)e^{-iy\\cdot\\eta\/h}), \\psi(x)e^{- ix\\cdot\\xi\/h}\\rangle,\n\\end{split}\n\\end{equation}\nwhere $ \\langle \\bullet, \\bullet \\rangle $ denotes distributional pairing.\nWe also note that \n\\[ \\WF_h(\\psi(x)e^{ix\\cdot\\xi\/h})=\\supp\\psi\\times\\{\\xi\\}, \\ \\ \n\\WF_h(\\psi(y)e^{-iy\\cdot\\eta\/h})=\\supp\\psi\\times\\{-\\eta\\}, \\ \\ \n ( \\xi , \\eta ) \\in \\complement V .\\]\n\nIn case (i), we assume \n\\[ \\WF_h(B)\\subset W=W_1 \\times W_2 \\text{ where $W_1=\\pi(W)\\subset X_0$\nand $W_2 \\subset\\mathbb{R}^n$ are compact.} \\]\n We make the following observation:\nif $ \\widetilde W_2 = \\{ \\xi' : \\exists \\, \\eta' \\in W_2 \\ ( \\xi', \\eta' )\n\\in \\complement V \\} $, then either $ - \\eta \\notin W_2 $ or $ - \\xi \\in \\widetilde W_2 $.\n(Here we used the symmetry \\eqref{eq:CV}.)\nHence if $A\\in\\Psi_h^{{\\rm{comp}}} (X)$, $ \\WF_h ( I - A ) \\cap\n\\widetilde{W}_1\\times\\widetilde{W}_2 = \\emptyset$,\nwhere $\\widetilde{W}_1$ is a small neighbourhood of $\\supp\\psi$, then\n\\begin{equation*}\n\\mathcal{F}_h(\\varphi u)(\\xi,\\eta)=\n\\langle e^{-it_0h^{-1}\\widetilde{P}_h(z)}\\widetilde{R}_h(z)B(\\psi(y)e^{-iy\\cdot\\eta\/h}), A(\\psi(x)e^{- ix\\cdot\\xi\/h})\\rangle+\\mathcal{O}(h^\\infty).\n\\end{equation*}\nTherefore\n\\begin{equation*}\n\\begin{split}\n|\\mathcal{F}_h(\\varphi u)(\\xi,\\eta)|\n=&\\;|\\langle e^{-it_0h^{-1}\\widetilde{P}_h(z)}\\widetilde{R}_h(z)B(\\psi(y)e^{-iy\\cdot\\eta\/h}), A(\\psi(x)e^{- ix\\cdot\\xi\/h})\\rangle|+\\mathcal{O}(h^\\infty)\\\\\n\\leq &\\; C\\|Ae^{-it_0h^{-1}\\widetilde{P}_h(z)}\\widetilde{R}_h(z)B\\|_{L^2\\to L^2}+\\mathcal{O}(h^\\infty)\\leq Ch^{-1}\\\\\n\\end{split}\n\\end{equation*}\nwhere we use the estimate \\eqref{modifiedresolvent} and the fact that microlocally on $\\WF_h(A)\\times\\WF_h(B)$ which is a compact set in $T^\\ast(X\\times X)$, $H_{sG(h)}$ is equivalent to $L^2$ uniformly.\nCombined with \\eqref{eq:iota} this finishes the proof for case (i).\n\nIn case (ii), we again assume that \n$ \\WF_h(B)\\subset W=W_1\\times W_2$ where $W_1=\\pi(W)\\subset X_0$ is a small compact neighbourhood of $x_0$ but now $W_2\\subset\\bar{\\mathbb{R}}^n=\\mathbb{R}^n\\cup\\partial \\, \\bar {\\mathbb{R}}^n$ is a small conic neighbourhood of $\\xi_0\\in\n\\partial \\,\\bar{ \\mathbb{R}}^n$ intersecting with $\\{|\\xi|\\geqslant C\\}$. As in case (i),\nwe put\n$ \\widetilde W =\\widetilde{W}_1\\times\\widetilde{W}_2$ such that $\\widetilde{W}_1$ is a small neighbourhood of $\\supp\\psi$ and $\\widetilde{W}_2$ is a small neighbourhood of ${\\complement V}(W_2)$, which is again a small conic neighbourhood of $\\xi_0$.\n\nWe then choose $A\\in\\Psi_h^0(X)$ such that $ \\WF_h ( I - A) \\cap \\widetilde{W}\n= \\emptyset $, and $\\WF_h(A)$ is contained in a small neighbourhood of $\\widetilde{W}$.\nWe have\n\\[ (\\xi,\\eta)\\in {\\complement V} \\ \\Longrightarrow \\ \\text{ (a) $-\\eta\\notin W_2$\nor (b) $- \\xi\\in\\widetilde{W}_2$.}\n\\]\nIn the case (a) we have\n\\begin{equation*}\n|\\mathcal{F}_h(\\varphi u)(\\xi,\\eta)|=\\mathcal{O}(h^\\infty(1+|\\xi|+|\\eta|)^{-\\infty}).\n\\end{equation*}\nIn the case\n (b) we need a uniform estimate for $\\langle\\xi\\rangle^N|\\mathcal{F}_h(\\varphi u)(\\xi,\\eta)|$ where $N $ is large.\nTo do this, we use the notation from the proof of Lemma \\ref{l:prop} and write\n\\begin{equation*}\n\\begin{split}\n\\langle\\xi\\rangle^N\\mathcal{F}_h(\\varphi u)(\\xi,\\eta)\n=&\\;\\langle e^{-it_0h^{-1}\\widetilde{P}_h(z)}\\widetilde{R}_h(z)B(\\psi(y)e^{-iy\\cdot\\eta\/h}),\\langle\\xi\\rangle^N\\psi(x)e^{-ix\\cdot\\xi\/h}\\rangle\\\\\n=&\\;\\langle \\varphi_{-t_0}^\\ast V (t_0) \\widetilde{R}_h(z)B(\\psi(y)e^{-iy\\cdot\\eta\/h}),A(\\langle\\xi\\rangle^N\\psi(x)e^{-ix\\cdot\\xi\/h})\\rangle+\\mathcal{O}(h^\\infty)\\\\\n=&\\;\\langle V(t_0)\\widetilde{R}_h(z)B(\\psi(y)e^{-iy\\cdot\\eta\/h}),\\varphi_{t_0}^\\ast A(\\langle\\xi\\rangle^N\\psi(x)e^{-ix\\cdot\\xi\/h})\\rangle+\\mathcal{O}(h^\\infty).\n\\end{split}\n\\end{equation*}\nWe notice that $\\WF_h(\\langle\\xi\\rangle^N\\psi(x)e^{-ix\\cdot\\xi\/h})=\\supp\\psi\\times\\{-\\xi\\}$, and\n\\begin{equation*}\n\\|\\langle\\xi\\rangle^N\\psi(x)e^{-ix\\cdot\\xi\/h}\\|_{H_h^{-N}}=\\mathcal{O}(1)\n\\end{equation*}\nuniformly in $\\xi$. Since $ t_0 $ is small we can choose $W$ and $\\widetilde{W}$ small\nenough, so that $e^{-t_0 H_p}\\widetilde{W}\\cap\\widetilde{W}=\\emptyset$.\nThen we choose a microlocal partition of unity, $A_1^2+A_2^2=I +\n\\mathcal O ( h^\\infty )_{ \\mathcal D' \\to {C^\\infty}} $,\nsuch that $e^{-tH_p}\\widetilde{W}\\subset\\el_h(A_1)$,\n$\\WF_h(A_1)$ is a small neighbourhood of $e^{-tH_p} \\widetilde W $ and\n $\\WF_h(A_2)\\cap e^{-t_0H_p}(\\WF_h(A))=\\emptyset$. We have\n\\begin{equation}\n\\label{eqa}\n\\begin{split}\n& \\langle\\xi\\rangle^N\\mathcal{F}_h(\\varphi u)(\\xi,\\eta)\n= \\langle A_1 V(t_0) \\widetilde{R}_h(z)B(\\psi(y)e^{-iy\\cdot\\eta\/h}),A_1\\varphi_{t_0}^\\ast A(\\langle\\xi\\rangle^N\\psi(x)e^{-ix\\cdot\\xi\/h})\\rangle\\\\\n& \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ + \\langle A_2 V(t_0) \\widetilde{R}_h(z)B(\\psi(y)e^{-iy\\cdot\\eta\/h}),A_2\\varphi_{t_0}^\\ast A(\\langle\\xi\\rangle^N\\psi(x)e^{- ix\\cdot\\xi\/h})\\rangle+\n\\mathcal \\mathcal{O}(h^\\infty).\n\\end{split}\n\\end{equation}\n\nWe recall the following propagation estimate \\cite[Propositon 2.5]{DZ} which\nis essentially the clasical result of Duistermaat--H\\\"ormander:\n\\begin{prop}\n\\label{ppg}\nAssume that $P_0\\in\\Psi_h^1(X)$ with semiclassical principal symbol $p-iq\\in S^1_h(X)\/hS_h^0(X)$ where $p\\in S^1(X;\\mathbb{R})$ is independent of $h$ and $q\\geqslant0$ everywhere. Assume also that $p$ is homogeneous of degree 1 in $\\xi$ for $|\\xi|$ large enough. Let $e^{tH_p}$ be the Hamiltonian flow of $p$ on $\\overline{T}^\\ast X$ and $u(h)\\in\\mathcal{D}'(X)$, then if $A_0,B_0,B_1\\in\\Psi_h^0(X)$ and for each $(x,\\xi)\\in\\WF_h(A_0)$, there exists $T\\geqslant0$ with $e^{-TH_p}(x,\\xi)\\in\\el_h(B_0)$ and $e^{tH_p}(x,\\xi)\\in\\el_h(B_1)$ for $t\\in[-T,0]$. Then for each $m$,\n\\begin{equation}\n\\|A_0u\\|_{H^m_h(X)}\\leq C\\|B_0u\\|_{H^m_h(X)}+Ch^{-1}\\|B_1P_0u\\|_{H_h^m(X)}+\\mathcal{O}(h^\\infty).\n\\end{equation}\n\\end{prop}\n\nWe apply the proposition to $u=\\widetilde{R}_h(z)B(\\psi(y)e^{-iy\\cdot\\eta\/h})$, $P_0=\\widetilde{P}_h(z)$, $A_0=A_1V(t_0)$ with $\\el_h(B_0)$ containing $e^{-TH_p}(\\WF_h(A_0))$ for $T>0$ small enough and $e^{tH_p}(x,\\xi)\\in\\el_h(B_1)$ for $t\\in [-T,0]$. Furthermore, we can choose $B_1$ so that $\\WF_h(B_1)\\cap\\WF_h(B)=\\emptyset$.\n\\[ \\begin{split} \\|A_0u\\|_{H_h^N} & \\leq C\\|B_0u\\|_{H_h^N}+Ch^{-1}\\|B_1B(\\psi(y)e^{-iy\\cdot\\eta})\\|_{H_h^N}+\\mathcal{O}(h^\\infty)\\\\\n& =C\\|B_0u\\|_{H_h^N}+\\mathcal{O}(h^\\infty). \\end{split} \\]\nHowever, the semiclassical wavefront set condition of $\\widetilde{R}_h(z)$ shows that $\\WF_h(B_0)\\cap\\WF_h(u)=\\emptyset$, thus\n$\\|A_0u\\|_{H_h^N}=\\mathcal{O}(h^\\infty)$\nand\nwe know the term corresponding to $A_1$ in the sum of \\eqref{eqa} is $\\mathcal{O}(h^\\infty)$. For the other term involving $ A_2 $, we use\n$$\\|A_2\\varphi_{t_0} ^\\ast A(\\langle\\xi\\rangle^N\\psi(x)e^{ix\\cdot\\xi\/h})\\|_{H^{P}_h }\n\\leq \\mathcal{O}(h^\\infty)\\|\\varphi_{t_0} ^\\ast A(\\langle\\xi\\rangle^N\\psi(x)e^{ix\\cdot\\xi\/h})\\|_{H_h^{-N}}=\\mathcal{O}(h^\\infty),$$\nfor any $ P $.\nThis is paired with the term estimated by\n$$\\|A_2 V ( t_0 ) \\widetilde{R}_h(z)B(\\psi(y)e^{-iy\\cdot\\eta\/h})\\|_{H_h^{-P }}\\leq\nC\\|\\psi(y)e^{-iy\\cdot\\eta\/h}\\|_{H_h^P } \\leq C \\langle \\eta \\rangle^P , $$\nfor some $ P$. Hence\n\\[\n\\langle A_2 V(t_0) \\widetilde{R}_h(z)B(\\psi(y)e^{-iy\\cdot\\eta\/h}),A_2\\varphi_{t_0}^\\ast A(\\langle\\xi\\rangle^N\\psi(x)e^{- ix\\cdot\\xi\/h}) \\rangle = \\mathcal O ( h^\\infty \\langle\n\\eta \\rangle^P ). \\]\nReturning to \\eqref{eqa} we see that\n\\[ \\langle \\xi \\rangle^N \\mathcal F_h ( \\varphi u ) (\\xi, \\eta ) =\n\\mathcal O ( h^\\infty \\langle\n\\eta \\rangle^P ). \\]\nSince $ |\\xi | $ is comparable $ |\\eta | $ in $ \\complement V $, we have\n$$ \\mathcal F_h ( \\varphi u ) (\\xi, \\eta ) = \\mathcal O ( h^\\infty\n\\langle ( \\xi , \\eta ) \\rangle^{ -N + P } ) $$ \nand that concludes the\nproof of \\eqref{eq:flattr1}.\n\n\n\n\n\n\n\\section{Proof of the trace formula}\n\\subsection{Sketch of the proof}\nWe first indicate basic ideas of the proof before we go into the details -- the\nprinciple is quite simple but the implementation involves the use of\nthe results of \\cite{DZ} and of some ideas from \\cite{S}.\n\nIn general, a trace formula such as \\eqref{localtrace} follows from the finite order of the analytic continuation of $\\zeta_1(\\lambda)$ in the strip $\\Im\\lambda\\geqslant-A$, that is,\nfrom having the following estimate valid away from small neighbourhoods of resonances:\n\\begin{equation}\n\\label{finiteorder}\n\\left|\\frac{d}{d\\lambda} \\log\\zeta_1(\\lambda)\\right|=\\mathcal{O}(\\langle\\lambda\\rangle^{2n+1}).\n\\end{equation}\nTo obtain the distributional identity \\eqref{localtrace}\nwe take $\\psi\\in C_0^\\infty(0,\\infty)$ and compute the following integral in two different ways\n\\begin{equation*}\n\\int_{\\mathbb{R}}\\widehat{\\psi}(\\lambda)\\frac{d}{d\\lambda} \\log\\zeta_1(\\lambda)d\\lambda.\n\\end{equation*}\nOn one hand, we pass the integral contour to $\\mathbb{R}+iB$, where $B>C_1$ so that \\eqref{zeta} converges. Since there are no resonances in the upper half plane, we have\n\\begin{equation*}\n\\begin{split}\n\\int_{\\mathbb{R}+iB}\\widehat{\\psi}(\\lambda)\\left(\\frac{1}{i}\\int_0^\\infty e^{it\\lambda}\\tr^\\flat e^{-itP}dt\\right)d\\lambda\n=&\\frac{1}{i}\\int_0^\\infty\\left(\\int_{\\mathbb{R}+iB}\\widehat{\\psi}(\\lambda)e^{it\\lambda}d\\lambda\\right)\\tr^{\\flat}e^{-itP}dt\\\\\n=&\\int_0^\\infty\\psi(t)\\tr^{\\flat}e^{-itP}dt.\n\\end{split}\n\\end{equation*}\nGuillemin's trace formula \\eqref{Guillemin} gives\n\\begin{equation}\n\\int_{\\mathbb{R}}\\widehat{\\psi}(\\lambda)\\frac{d}{d\\lambda}\\log\\zeta_1(\\lambda)d\\lambda=\\left\\langle \\sum_\\gamma\\frac{T_\\gamma^\\#\\delta(t-T_\\gamma)}{|\\det(I-\\mathcal{P}_\\gamma)|}, \\psi \\right\\rangle.\n\\end{equation}\nOn the other hand, we pass the integral contour to $\\mathbb{R}-iA$ and we get the contribution from the poles of $\\frac{d}{d\\lambda}\\log\\zeta_1(\\lambda)$ which are exactly the Pollicott-Ruelle resonances,\n\\begin{equation}\n\\sum_{\\mu\\in\\Res(P),\\Im\\mu>-A}\\widehat{\\psi}(\\mu)=\\left\\langle\n\\sum_{\\mu\\in\\Res(P),\\Im\\mu>-A}e^{-i\\mu t}, \\psi \\right\\rangle.\n\\end{equation}\nThe remainder is exactly\n\\begin{equation}\n\\langle F_A, \\psi\\rangle: =\\int_{\\mathbb{R}-iA}\\widehat{\\psi}(\\lambda)\\frac{d}{d\\lambda}\\log\\zeta_1(\\lambda)d\\lambda,\n\\end{equation}\nand we want to show that $ F_A $ can be extended to a tempered distribution\nsupported on $ [ 0, \\infty ) $ and that it satisfies \\eqref{error}. The\nestimate \\eqref{finiteorder} is crucial here.\n\nTo see \\eqref{finiteorder}, we decompose\n\\begin{equation}\n\\label{decomposition}\n\\begin{split}\ne^{-it_0(P-\\lambda)}(P-\\lambda)^{-1} & =\ne^{-it_0(P-i \\widetilde Q-\\lambda)}(P-i \\widetilde Q-\\lambda)^{-1}\n+[(P-\\lambda)^{-1}-(P-i \\widetilde Q-\\lambda)^{-1}]\\\\\n& \\ \\ \\ \\ - i\\int_0^{t_0}[e^{-it(P-\\lambda)}-e^{-it(P-i \\widetilde Q-\\lambda)}]dt,\n\\end{split}\n\\end{equation}\nwhere $ \\widetilde Q = h^{-1} Q $ for a suitably chosen $ h$ depending on the range of $ \\lambda $'s. This is valid from $ \\Im \\lambda \\gg 0 $ and then continues analytically to $ \\mathbb C $ on the level of distributional Schwartz kernels.\n\nThe first term is holomorphic in $\\lambda$ and can be estimated by Proposition \\ref{flattracees} in the semiclassical setting.\n\nThe second term on the right hand side of \\eqref{decomposition} is of trace class if $\\lambda$ is not a resonance. To see this, we use the following formula\n\\begin{equation}\n\\label{res1}\n\\begin{split}\n(P-\\lambda)^{-1}-(P-i\\widetilde Q-\\lambda)^{-1} & =\n[(P-\\lambda)^{-1}(P-i \\widetilde Q-\\lambda)-I](P-i\\widetilde Q-\\lambda)^{-1}\\\\\n& =-(P-\\lambda)^{-1}i \\widetilde Q(P-i \\widetilde Q-\\lambda)^{-1}\\\\\n\\end{split}\n\\end{equation}\nto get\n\\begin{equation}\n\\label{res2}\n(P-\\lambda)^{-1}=(P-i \\widetilde Q-\\lambda)^{-1}[I+i\\widetilde Q(P-i\\widetilde Q-\\lambda)^{-1}]^{-1}.\n\\end{equation}\nBy using \\eqref{res2} in \\eqref{res1} we obtain\n\\begin{equation}\n(P-\\lambda)^{-1}-(P-i\\widetilde Q-\\lambda)^{-1}=\n-(P-i\\widetilde Q-\\lambda)^{-1}[I+i \\widetilde Q(P-i\\widetilde Q-\\lambda)^{-1}]^{-1}\ni\\widetilde Q(P-i \\widetilde\nQ-\\lambda)^{-1}.\n\\end{equation}\nIf we denote { $F(\\lambda)=I+i\\widetilde{Q}(P-i\\widetilde Q-\\lambda)^{-1}$}, then\n\\begin{equation*}\nF'(\\lambda)=\\frac{d}{d\\lambda}F(\\lambda)=i\\widetilde Q(P-i\\widetilde Q-\\lambda)^{-2}.\n\\end{equation*}\nMoreover, $F(\\lambda) - I $ and $ F' ( \\lambda ) $ are operators\nof finite rank. By the cyclicity of the trace, we have\n\\begin{equation*}\n\\begin{split}\n\\tr[(P-\\lambda)^{-1}-(P-i\\widetilde Q-\\lambda)^{-1}]=&\n-\\tr[I+i\\widetilde Q(P-i\\widetilde Q-\\lambda)^{-1}]^{-1}i\\widetilde Q(P-i\\widetilde Q-\\lambda)^{-2}\\\\\n=&-\\tr F'(\\lambda)F(\\lambda)^{-1}=-\\frac{d}{d\\lambda}\\log\\det F(\\lambda).\n\\end{split}\n\\end{equation*}\nTherefore it can be controlled by the rank of $\\widetilde Q$ and the norm of $F(\\lambda)$.\n\nThe third term in \\eqref{decomposition} can be handled by Duhamel's principle: if $u(t): =e^{-it(P-i\\widetilde Q-\\lambda)}f$, then\n\\begin{equation*}\n\\partial_t u(t)=-i(P-i\\widetilde Q-\\lambda)u(t), \\ \\ \\ u(0)=f.\n\\end{equation*}\nRewriting the equation as\n$\\partial_t u(t)+i(P-\\lambda)u(t)=-\\widetilde Qu(t)$ ,\nwe get\n\\begin{equation*}\nu(t)=e^{-it(P-\\lambda)}f-\\int_0^t e^{-i(t-s)(P-\\lambda)}\\widetilde Qu(s)ds.\n\\end{equation*}\nTherefore\n\\begin{equation*}\ne^{-it(P-\\lambda)}-e^{-it(P-i\\widetilde Q-\\lambda)}=\\int_0^te^{-i(t-s)(P-\\lambda)}\\widetilde Qe^{-is(P-i\\widetilde Q-\\lambda)}ds.\n\\end{equation*}\nThis shows that the left hand side is also of trace class and its trace class norm is controlled by the trace class norm of $\\widetilde Q$.\n\n{ To carry out the strategy above} we need to choose correct contours and to obtain\na local version of \\eqref{finiteorder} using $ \\det F ( \\lambda ) $.\nFor that we break the infinite contour into a family of finite contours and use the semiclassical reduction to treat the zeta function on each contour separately.\nThat involves choices of $ h $ so that $ z = h \\lambda $ is in an\nappropriate range.\n\n\\subsection{The contours for integration}\nIn this section, we choose contours for integration. First, we decompose the region $\\Omega=\\{\\lambda\\in\\mathbb{C}:-A\\leq \\Im\\lambda\\leq B\\}$ into dyadic pieces: fix $ E > 0 $ and put $\\Omega=\\bigcup_{k\\in\\mathbb{Z}}\\Omega_k$, where $\\Omega_0=\\Omega\\cap\\{-E\\leq\\Re\\lambda\\leq E\\}$ and\n\\begin{gather*}\n\\Omega_k:=\\Omega\\cap\\{2^{k-1}E\\leq\\Re\\lambda\\leq 2^kE\\}, \\ \\ k>0\\\\\n\\Omega_{-k}:=\\Omega\\cap\\{-2^kE\\leq\\Re\\lambda\\leq - 2^{k-1}E\\}, \\ \\ k>0.\n\\end{gather*}\nFor each $k$, we write $\\gamma_k=\\partial\\Omega_k=\\bigcup_{j=1}^4\\gamma^j_k$ with counterclockwise orientation.\n\n\\begin{figure}[ht]\n\\includegraphics[width=6.5in]{contours}\n\\caption{Integration contours}\n\\end{figure}\n\nNext, we shall modify $\\gamma^2_k,\\gamma^3_k$ and $\\gamma^4_k$ to avoid the resonances. For simplicity, we only work for $k>0$ as the case for $k<0$ can be handled by symmetry. We choose $\\widetilde{\\gamma}^2_k,\\widetilde{\\gamma}^3_k$ and $\\widetilde{\\gamma}^4_k$ lying in\n\\begin{equation*}\n([2^{k-1}E-1,2^kE+1]+i[-A-1,B])\\setminus([2^{k-1}E+1,\n2^kE-1]+i[-A,B])\n\\end{equation*}\nso that $\\widetilde{\\gamma}^2_k\\subset[2^{k-1}E-1,2^{k-1}E+1]\n+i[-A,B]$ connects $2^{k-1}E+iB$ with a point $w_k$ which lies\non $[2^{k-1}E-1,2^{k-1}E+1]-iA$, $\\widetilde{\\gamma}^4_k=-\\widetilde{\\gamma}^2_{k+1}$; $\\widetilde{\\gamma}^3_k\\subset[2^{k-1}E-1,2^kE+1]+i[-A-1,-A]$ connects $w_k$ with $w_{k+1}$. The region bounded by $\\widetilde{\\gamma}_k :=\\bigcup_{j=1}^4\\widetilde{\\gamma}^j_k$\n is denoted as $\\widetilde{\\Omega}_k$, (we write $\\widetilde{\\gamma}^1_k=\\gamma^1_k$). Then we have { \n\\begin{equation*}\n\\Omega\\subset\\widetilde{\\Omega}=\\bigcup_{k\\in\\mathbb{Z}}\\widetilde{\\Omega}_k\n\\subset\\{\\lambda\\in\\mathbb{C},-A-1\\leq\\Im\\lambda\\leq B\\}\n\\end{equation*}}\nand all $\\widetilde{\\Omega}_k$ have disjoint interiors.\n\nFor convenience, we turn into the semiclassical setting.\nLet $W_h=h\\widetilde{\\Omega}_k$ where $h^{-1\/2}=2^kE$, then\n\\begin{equation*}\n\\textstyle [\\frac{1}{2}h^{1\/2}+h,h^{1\/2}-h]+i[-Ah,Bh]\\subset W_h\\subset\n[\\frac{1}{2}h^{1\/2}-h,h^{1\/2}+h]+i[(-A-1)h,Bh].\n\\end{equation*}\nMoreover, $\\rho_h:=\\partial W_h=\\bigcup_{j=1}^4\\rho_h^j$ where $\\rho_h^1$ is the horizontal segment $[\\frac{1}{2}h^{1\/2},h^{1\/2}]+iBh$ with negative orientation; $\\rho_h^2\\subset[\\frac{1}{2}h^{1\/2}-h,\\frac{1}{2}h^{1\/2}+h]+i[-Ah,Bh]$ connects $\\frac{1}{2}h^{1\/2}+iBh$ with a point $z_h\\in[\\frac{1}{2}h^{1\/2}-h,\\frac{1}{2}h^{1\/2}+h]-iAh$; $\\rho_h^4\\subset[h^{1\/2}-h,h^{1\/2}+h]+i[-Ah,Bh]$ connects a point $z'_h\\in[h^{1\/2}-h,h^{1\/2}+h]-iAh$ with $h^{1\/2}+iBh$;\nand $\\rho_h^3\\subset[\\frac{1}{2}h^{1\/2}-h,h^{1\/2}+h]$ connects $z_h$ with $z'_h$.\n\nWe have the following contour integration\n\\begin{equation}\n\\label{contour}\n\\oint_{\\rho_h}\\widehat{\\psi}_h(z)\\frac{d}{dz}\\log\\zeta_h(z)dz=\n\\sum_{z_j\\in \\Res_h(P)\\cap W_h}\\psi_h(z_j).\n\\end{equation}\nHere we write $\\widehat{\\psi}_h(z)=\\widehat{\\psi}(z\/h)$, $\\zeta_h(z)=\\zeta_1(z\/h)$, $\\Res_h(P)=h\\Res(P)$.\n\nWe rewrite the decomposition \\eqref{decomposition} in this scaling:\n\\begin{equation}\n\\label{hdecom}\n\\begin{split}\n\\frac{d}{dz}\\log\\zeta_h(z) & = h \\tr^\\flat(e^{-it_0h^{-1}P_h(z)}R_h(z))\\\\\n& = \\tr^\\flat(e^{-it_0h^{-1}\\widetilde{P}_h(z)}\\widetilde{R}_h(z))\n+ \\tr(R_h(z)-\\widetilde{R}_h(z))\\\\\n& -\\frac{i}{h}\\tr\\int_0^{t_0}[e^{-ith^{-1}P_h(z)}-e^{-ith^{-1}\\widetilde{P}_h(z)}]dt.\n\\end{split}\n\\end{equation}\nThen as in the discussion after \\eqref{decomposition}, in the region $-C_0h\\leq \\Im z\\leq 1, |\\Re z|\\leq 2h^{1\/2}$, we can apply Proposition\n\\ref{flattracees} to obtain\n\\begin{equation}\n\\label{hes1}\n\\left|\\tr^\\flat(e^{-it_0h^{-1}\\widetilde{P}_h(z)}\\widetilde{R}_h(z))\\right|=\\mathcal{O}(h^{-2n-1}).\n\\end{equation}\nAlso we have\n\\begin{equation}\n\\label{hes2}\n\\left \\|\\int_0^{t_0}[e^{-ith^{-1}P_h(z)}-e^{-ith^{-1}\\widetilde{P}_h(z)}]dt \\right\\|_{\\rm{tr}} =\\mathcal{O}(h^{-n-1}).\n\\end{equation}\nFor the second term, we have\n\\begin{equation*}\n\\tr(R_h(z)-\\widetilde{R}_h(z))=-\\frac{d}{dz}\\log\\det F(z),\n\\end{equation*}\nwhere\n$F(z)=I+iQ\\widetilde{R}_h(z)$ is a Fredholm operator and the poles for $F(z)^{-1}$ coincides with the resonances. Moreover, by \\eqref{finiterank}, \\eqref{modifiedresolvent} and Weyl's inequality, we have\n\\begin{equation}\n\\label{detes1}\n|\\det F(z)|\\leq (Ch^{-1})^{Ch^{-n}}\\leq Ce^{Ch^{-n-1}}.\n\\end{equation}\n\nMoreover, when $\\Im z\\geqslant C_1h$, we have $F(z)=I+iQ\\widetilde{R}_h(z)=P_h(z)\\widetilde{R}_h(z)$, so $F(z)$ is invertible and $F(z)^{-1}=\\widetilde{P}_h(z)R_h(z)$. Therefore\n\\begin{equation*}\n\\begin{split}\n\\|F(z)^{-1}\\|_{H_{sG(h)}\\to H_{sG(h)}}\n\\leq\\|\\widetilde{P}_h(z)\\|_{D_{sG(h)}\\to H_{sG(h)}}&\\|R_h(z)\\|_{H_{sG(h)}\\to D_{sG(h)}}\\\\\n\\leq\\|\\widetilde{P}_h(z)\\|_{D_{sG(h)}\\to H_{sG(h)}}&(\\|R_h(z)\\|_{H_{sG(h)}\\to H_{sG(h)}}\\\\\n&+\\|hPR_h(z)\\|_{H_{sG(h)}\\to H_{sG(h)}})\n\\leq Ch^{-1}.\n\\end{split}\n\\end{equation*}\nWe can also write $F(z)^{-1}=I-iQR_h(z)$ which gives the estimate\n\\begin{equation}\n\\label{detes2}\n|\\det F(z)^{-1}|\\leq (Ch^{-1})^{Ch^{-n}}\\leq Ce^{Ch^{-n-1}}.\n\\end{equation}\n\nWe recall a lower modulus theorem due to H. Cartan { (see \\cite[\\S 11.3, Theorem 4]{Le})} : Suppose that $g$ is holomorphic in $D(z_0,2eR)$ and $g(z_0)=1$. Then for any $\\eta>0$,\n\\begin{equation}\n\\label{lowermod}\n\\log|g(z)|\\geqslant-\\log(15e^3\/\\eta)\\log\\max_{|z-z_0|<2eR}|g(z)|,\\;\\;\nz\\in D(z_0,R)\\setminus\\mathcal{D},\n\\end{equation}\nwhere $\\mathcal{D}$ is a union of discs with the sum of radii less than $\\eta R$. With the help of this lower modulus theorem, we can make a suitable choice of integration contour.\n\n\\begin{lem}\nWe can choose $\\widetilde{\\gamma}_k$ suitably such that in addition to the assumptions above, we have\n\\begin{equation}\n\\label{detbound}\n|\\log\\det F(z)|=\\mathcal{O}(h^{-n-1})\n\\end{equation}\nwhen $z\\in\\rho_h$.\n\\end{lem}\n\\begin{proof}\nWe shall apply the lower modulus theorem with $z_0=\\frac{1}{2}h^{1\/2}+iBh\/2$ and $R=C_0'h$ where $C_0'$ is large enough, so that\n\\begin{equation*}\n\\begin{split}\n[\\textstyle{\\frac{1}{2}h^{1\/2}-h,\\frac{1}{2}h^{1\/2}+h}]+i[(-A-1)h,Bh] & \\subset D(z_0,R) \\subset D(z_0,2eR) \\\\\n & \\subset[-2h^{1\/2},2h^{1\/2}]+i[-C_0h,1].\n\\end{split}\n\\end{equation*}\nIn addition, we let $\\eta$ be small enough, so that $\\eta R-A}\\widehat{\\psi}(\\mu)\n=\\sum_{\\gamma}\\frac{T_\\gamma^{\\#}\\delta(t-T_\\gamma)}\n{|\\det(I-\\mathcal{P}_\\gamma)|}+\\langle\\psi,F_A\\rangle\n\\end{equation*}\nwhere\n\\begin{equation}\n\\label{errorform}\n\\langle\\psi, F_A\\rangle=-\\sum_{\\mu_j\\in\\Res(P)\\cap\\widetilde{\\Omega},\\Im\\mu_j\\leq-A}\\widehat{\\psi}(\\mu_j)+\\int_{\\Gamma}\\widehat{\\psi}(\\lambda)\n\\frac{d}{d\\lambda}\\log\\zeta_1(\\lambda)d\\lambda.\n\\end{equation}\nThis proves \\eqref{localtrace}.\n\nSo far, the distribution $F_A$ is only defined in $\\mathcal{D}'(0,\\infty)$. However, the right-hand side in \\eqref{localtrace} has an obvious extension to $\\mathbb{R}$ by zero on the negative half line as it is supported away from 0. By the polynomial upper bounds \\eqref{upperbound} on the number of resonances in the strip $\\Im\\mu>-A$, the sum\n\\begin{equation*}\nu_A(t)=\\sum_{\\mu\\in\\Res(P),\\Im\\mu>-A}e^{-i\\mu t}\n\\end{equation*}\nalso has an extension to $\\mathbb{R}$ which has support in $[0,\\infty)$. We only need to show that $u_A$ is of finite order: For any $\\varphi\\in C_0^\\infty(0,\\infty)$, $k\\geqslant0$, we have\n$\\widehat{\\varphi^{(k)}}(\\lambda)=(i\\lambda)^k\\widehat{\\varphi}(\\lambda)$. Therefore we can write\n\\begin{equation*}\n\\langle u_A,\\varphi\\rangle=\\sum_{\\mu\\in\\Res(P),\\Im\\mu>-A}\\widehat{\\varphi}(\\mu)=\\sum_{\\mu\\in\\Res(P),\\Im\\mu>-A}(i\\mu)^{-k}\\widehat{\\varphi^{(k)}}(\\mu)\n\\end{equation*}\nWhen $k$ is large, the sum\n\\begin{equation*}\n\\sum_{\\mu\\in\\Res(P),\\Im\\mu>-A}|\\mu|^{-k}\n\\end{equation*}\nconverges absolutely. Therefore we have the finite order property of $u_A$. Moreover, any two such extensions of $u_A$ are only differed by a distribution $v$ supported at $\\{0\\}$, that is, a linear combination of delta function and its derivatives.\n\nNow we can certainly extend $F_A$ to a distribution on $\\mathbb{R}$ with support in $[0,\\infty)$. Since $\\check{v}$ is a polynomial in the whole complex plane. Therefore choice of the extension of $u_A$ does not affect the estimate on $\\widehat{F}_A$.\n\nFinally, we give the { desired} estimate on $\\widehat{F}_A$. This follows from the fact $e^{\\eta t}F_A\\in\\mathcal{S}'$ for any $\\eta-A\\})\n\\end{equation*}\nand assume that\n\\begin{equation}\n\\label{contra}\nN_A(r)\\leq P(\\delta,A)r^\\delta.\n\\end{equation}\n\nWe fix a test function $\\varphi\\in C_0^\\infty(\\mathbb{R})$ with the following properties:\n\\begin{equation*}\n\\varphi\\geqslant0, \\ \\ \\varphi(0)>0, \\ \\ \\supp\\varphi\\subset[-1,1].\n\\end{equation*}\nNext we set $\\varphi_{l,d}(t)=\\varphi(l^{-1}(t-d))$ where $d>1$ and $l<1$, so that $\\varphi_{l,d}\\in C_0^\\infty(0,\\infty)$. Therefore we can apply the local trace formula to get\n\\begin{equation}\n\\label{tr1}\n\\sum_{\\mu\\in\\Res(P),\\Im\\mu>-A}\\widehat{\\varphi}_{l,d}(\\mu)\n+\\langle F_A,\\varphi_{l,d}\\rangle=\\sum_{\\gamma}\\frac{T_\\gamma^\\#\\varphi_{l,d}(T_\\gamma)}{|\\det(I-\\mathcal{P}_\\gamma)|}.\n\\end{equation}\n\nFirst, we note that by Paley-Wiener theorem,\n\\begin{equation}\n\\label{PW}\n|\\widehat{\\varphi}_{l,d}(\\zeta)|=|l\\widehat{\\varphi}(l\\zeta)e^{-id\\zeta}|\\leq\nC_N l e^{(d-l)\\Im\\zeta}(1+|l\\zeta|)^{-N},\n\\end{equation}\nfor $\\Im\\zeta\\leq0$ and any $ N \\geq 0 $.\n\nBy the assumption, we have the following estimate on the sum on the left-hand side of \\eqref{tr1},\n\\begin{equation}\n\\label{es1}\n\\begin{split}\n\\left|\\sum_{\\mu\\in\\Res(P),\\Im\\mu>-A}\\widehat{\\varphi}_{l,d}(\\mu)\\right|\\leq&\\; Cl\\int_0^\\infty(1+l r)^{-N}dN_A(r)\\\\\n\\leq&\\; Cl\\int_0^\\infty\\frac{d}{dr}[(1+l r)^{-N}]N_A(r)dr\\\\\n\\leq&\\; CP(\\delta,A)l\\int_0^\\infty\\frac{d}{dr}[(1+l r)^{-N}]r^\\delta dr\\leq Cl^{1-\\delta}.\n\\end{split}\n\\end{equation}\n\nThe remainder term $\\langle F_A,\\varphi_{l,d}\\rangle$ on the left-hand side of \\eqref{tr1} can be rewritten as\n\\begin{equation*}\n\\langle\\check{F}_A,\\widehat{\\varphi}_{l,d}\\rangle\n=\\int_{\\mathbb{R}}\\widehat{F}_A(-\\zeta)\\widehat{\\varphi}_{l,d}(\\zeta)d\\zeta.\n\\end{equation*}\nBy \\eqref{error}, we can pass the contour to $\\mathbb{R}+i(\\epsilon-A)$ to get\n\\begin{equation}\n\\label{es2}\n\\begin{split}\n|\\langle F_A,\\varphi_{l,d}\\rangle|\n\\leq &\n\\int_{\\mathbb{R}+i(\\epsilon-A)}|\\widehat{F}_A(-\\zeta)|\n|\\widehat{\\varphi}_{l,d}(\\zeta)|d\\zeta\\\\\n\\leq &\\;\nCl e^{(d-l)(\\epsilon-A)}\n\\int_{\\mathbb{R}+i(\\epsilon-A)}\\langle\\zeta\\rangle^{2n+1}(1+l|\\zeta|)^{-2n-3}d\\zeta\\\\\n\\leq &\\; Cl^{-2n-1}e^{(d-l)(\\epsilon-A)}\n\\end{split}\n\\end{equation}\nwhere we use \\eqref{PW} with $ N =2n+3$.\n\n{ \nOn the other hand, to get a lower bound of the right-hand side of \\eqref{tr1}, we fix one primitive periodic orbit $\\gamma_0$ and let $d=kT_{\\gamma_0}$, $k\\in\\mathbb{N}$. Since every term there is nonnegative, we ignore all but the term corresponding to $\\gamma_d$ which is the $k$-times iterate of $\\gamma_0$ and get\n\\begin{equation*}\n\\sum_{\\gamma}\\frac{T_\\gamma^\\#\\varphi_{l,d}(T_\\gamma)}{|\\det(I-\\mathcal{P}_\\gamma)|}\n\\geq\\frac{T_{\\gamma_d}^\\#\\varphi(0)}{|\\det(I-\\mathcal{P}_{\\gamma_d})|}=\\frac{T_{\\gamma_0}\\varphi(0)}{|\\det(I-\\mathcal{P}_{\\gamma_0}^k)|}.\n\\end{equation*}\nLet $\\lambda_1,\\ldots,\\lambda_{n-1}$ be the eigenvalues of $\\mathcal{P}_{\\gamma_0}$, then for some $\\alpha$ depending only on $\\lambda_j$'s,\n\\begin{equation*}\n|\\det(I-\\mathcal{P}_{\\gamma_0}^k)|=|(1-\\lambda_1^k)\\cdots(1-\\lambda_{n-1}^k)|\\leq Ce^{k\\alpha}=Ce^{\\theta_0d},\n\\end{equation*}\nif $\\theta_0=\\alpha\/T_{\\gamma_0}$.} \nThis gives the lower bound\n\\begin{equation}\n\\label{es3}\n\\sum_{\\gamma}\\frac{T_\\gamma^\\#\\varphi_{l,d}(T_\\gamma)}{|\\det(I-\\mathcal{P}_\\gamma)|}\\geq Ce^{-\\theta_0d}.\n\\end{equation}\n\n\n\nCombining \\eqref{es1},\\eqref{es2},\\eqref{es3}, we have the following inequality\n\\begin{equation*}\nCl^{1-\\delta}+Cl^{-2n-1}e^{(d-l)(\\epsilon-A)}\\geq Ce^{-\\theta d}.\n\\end{equation*}\nWe first choose $l=e^{-\\beta d}$, then we have\n\\begin{equation*}\nCe^{-\\beta d(1-\\delta)}+Ce^{(d-l)(\\epsilon-A)+(2n+1)\\beta d}\\geq Ce^{-\\theta_0d}.\n\\end{equation*}\nNotice that the constants $C$'s may depend on $A$, but not on $d$. If we choose $\\beta$ and $A$ large while $\\epsilon$ small so that $\\beta(1-\\delta)>\\theta_0$ and $A-\\epsilon-(2n+1)\\beta>\\theta_0$, then we get a contradiction as $d\\to\\infty$. This can be achieved when $A>A_\\delta$ where\n\\begin{equation}\n\\label{adelta}\nA_\\delta=\\theta_0(1+(2n+1)(1-\\delta)^{-1}).\n\\end{equation}\nThis finishes the proof of Theorem \\ref{thm2}.\n\n\\medskip\n\n\\noindent\n{\\bf Remark.} { \nFrom the proof, we see that the essential gap is bounded by $A_0=\\theta_0(2n+2)$, where $\\theta_0$ given above only depends on the Poincar\\'{e} map associated to a primitive periodic orbit $\\gamma_0$. More explicitly, \n$$\\theta_0=\\frac{1}{T_{\\gamma_0}}\\sum_{\\lambda\\in\\sigma(\\mathcal{P}_{\\gamma_0}):|\\lambda|>1}\\log|\\lambda|.$$\nA weaker bound not depending on the specific orbit is given by\n$\\theta_0\\leq\\theta d_u$ where $d_u=\\dim E_u$ is the dimension of the unstable fiber and $\\theta$ is the Lyapunov constant of the flow given in \\S 2.1.}\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzbzsa b/data_all_eng_slimpj/shuffled/split2/finalzzbzsa new file mode 100644 index 0000000000000000000000000000000000000000..6bbf4bc06149e4132e3ccd01085f74d2cd7b4ee2 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzbzsa @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\nMany complex fluids display shear banding, in which a state of\ninitially homogeneous shear flow gives way to the formation of\ncoexisting bands of differing shear rate, with layer normals in the\nflow-gradient direction. For recent reviews,\nsee~\\cite{Olmsted2008,Manneville2008a,Fielding2014,Divoux2015}.\nFollowing its early observation in wormlike micellar surfactant\nsolutions~\\cite{Britton1997}, over the past two decades shear banding\nhas been seen in virtually all the major classes of complex fluids and\nsoft solids. Examples include microgels~\\cite{Divoux2010},\nclays~\\cite{Martin2012}, emulsions~\\cite{Coussot2002}\nfoams~\\cite{Rodts2005}, lamellar surfactant\nphases~\\cite{Salmonetal2003a}, triblock\ncopolymers~\\cite{Berretetal2001a,Mannevilleetal2007a}, star\npolymers~\\cite{Rogers2008}, and -- subject to ongoing\ncontroversy~\\cite{Wangetal2003a,Wangetal2006c,Wangetal2008a,Li2013,Wang2014,Li2015,Wang2011,Wangetal2008a} -- \nlinear polymers.\n\nPrior to about 2010, the majority of studies of shear banding focused\non conditions of a steadily applied shear flow. The criterion for the\npresence of steady state banding in this case is well known: that the\nunderlying homogeneous constitutive curve of shear stress as a\nfunction of shear rate has a regime of negative slope. (In some cases\nof strong concentration coupling shear banding can arise even for a\nmonotonic constitutive curve~\\cite{Fielding2003a}, but we\ndo not consider that case here.) Such a regime is predicted by the\noriginal tube theory of Doi and Edwards for non-breakable\npolymers~\\cite{DoiEdwards}, and by the reptation-reaction model of\nwormlike micellar surfactants~\\cite{Cates1990}. It is\nstraightforward to show that a state of initially homogeneous shear\nflow is linearly unstable, in this regime of negative constitutive\nslope, to the formation of shear bands~\\cite{Yerushalmi1970}. The\ncomposite steady state flow curve of shear stress as a function of\nshear rate then displays a characteristically flat plateau regime, in\nwhich shear bands are observed.\n\nFrom an experimental viewpoint, the evidence for steady state shear\nbanding under a steadily applied shear flow is now overwhelming in the case\nof wormlike micelles. For reviews,\nsee~\\cite{Cates2007,Berret2005}. For linear unbreakable\npolymers the issue remains controversial, as recently reviewed in\nRef.~\\cite{Snijkers2015}. In particular the original\nDoi-Edwards model did not account for a process known as convective\nconstraint release\n(CCR)~\\cite{Marrucci1996,Ianniruberto2014a,Ianniruberto2014}.\nSince CCR (which we describe below) was proposed, there has been an\nongoing debate about its efficacy in potentially eliminating the\nregime of negative constitutive slope and restoring a monotonic\nconstitutive curve, thereby eliminating steady state banding.\nHowever, a non-monotonic constitutive curve and associated steady\nstate shear banding has been seen in molecular dynamics simulations of\npolymers~\\cite{Likhtmanetal2012}, for long enough chain lengths. It\nis important to note, though, that the polydispersity that is often\npresent in practice in unbreakable polymers also tends to restore\nmonotonicity.\n\nBesides the conditions of steady state flow just described, many flows\nof practical importance involve a strong time dependence. In view of\nthis, a natural question to ask is whether shear banding might also\narise in these time-dependent flows and, if so, under what conditions.\nOver the past decade, a body of experimental data has accumulated to\nindicate that it does indeed occur: in shear\nstartup~\\cite{Divoux2010, Divoux2011a, Wangetal2009a,\n Huetal2007a, Wangetal2008a, Martin2012}, following a step\nstrain (in practice a rapid strain ramp)~\\cite{Wangetal2010a,\n Boukany2009a, Wangetal2006a, Fang2011, Wangetal2007a,\n Archer1995, Wangetal2008c}, and following a step\nstress~\\cite{Gibaud2010,Divoux2011,\n Wangetal2009a,Huetal2007a, Wangetal2003a, Hu2008,\n Wangetal2008c, Hu2005, Hu2010}.\n\n\nConsistent with this growing body of experimental evidence,\ntheoretical\nconsiderations~\\cite{Moorcroft2013,Moorcroft2014,Moorcroft2011,Adamsetal2011,Manningetal2009a}\nalso suggest that shear banding might arise rather generically in\nflows with a sufficiently strong time-dependence, even in fluids that\nhave a monotonically increasing constitutive curve and so do not\ndisplay steady state banding under conditions of a continuously\napplied shear. Indeed, the calculations to date suggest that the set\nof fluids that show banding in steady state is only a subset of those\nthat exhibit banding in time-dependent flows. In view of this,\nalthough the question concerning the existence or otherwise of steady\nstate shear banding in polymers remains an important one, the\nresolution of that controversy is likely to be of less practical\nimportance to the broader issue of whether shear banding arises more\ngenerally in time-dependent flows.\n\nIn the last five years progress has been made in establishing\ntheoretically, separately for each of the time-dependent flow\nprotocols listed above (shear startup, step strain and step stress), a\nfluid-universal criterion~\\cite{Moorcroft2013} for the onset of\nshear banding, based on the shape of the time-dependent rheological\nresponse function for the particular protocol in question. We now\nbriefly review these criteria as backdrop to understanding the results\nthat follow below for shear banding in large amplitude oscillatory\nshear (LAOS).\n\nIn shear startup (the switch-on at some time $t=0$ of a constant shear\nrate $\\dot{\\gamma}$), the onset of banding is closely associated with the\npresence of an\novershoot~\\cite{Moorcroft2013,Moorcroft2014,Moorcroft2011,Adamsetal2011,Manningetal2009a}\nin the startup signal of stress as a function of time (or equivalently\nas a function of strain), as it evolves towards its eventual steady\nstate on the material's flow curve. This concept builds on the early\ninsight of Ref.~\\cite{Marrucci1983}. The resulting bands may, or may\nnot, then persist to steady state, according to whether or not the\nunderlying constitutive curve of stress as a function of strain rate\nis non-monotonic. This tendency of a startup overshoot to trigger\nbanding was predicted on the basis of fluid-universal analytical\ncalculations in Ref.~\\cite{Moorcroft2013}, and has been\nconfirmed in numerical simulations of polymeric fluids (polymer solutions, polymer melts and\nwormlike micelles)~\\cite{Moorcroft2014,Adamsetal2011},\npolymer glasses~\\cite{Fielding2013} and soft glassy materials\n(dense emulsions, microgels, foams, {\\it\n etc.})~\\cite{Moorcroft2011,Manningetal2009a,Jagla2010}.\nIt is consistent with experimental observations in wormlike micellar\nsurfactants~\\cite{Hu2008, Wangetal2008c},\npolymers~\\cite{Wangetal2008a, Wangetal2008b, Wangetal2009a,\n Wangetal2009b, Boukany2009a, Wangetal2003a, Huetal2007a,\n Wangetal2009d}, carbopol\ngels~\\cite{Divoux2011a,Divoux2010} and Laponite\nclay suspensions~\\cite{Martin2012}.\n\nFollowing the imposition of a step stress in a previously undeformed\nsample, the onset of shear banding is closely associated with the\nexistence of a regime of simultaneous upward slope and upward\ncurvature in the time-differentiated creep response curve of shear\nrate as a function of\ntime~\\cite{Moorcroft2013,Fielding2014}. This criterion\nwas also predicted on the basis of fluid-universal analytical\ncalculations in Ref.~\\cite{Moorcroft2013}, and has been\nconfirmed in numerical simulations of polymeric\nfluids~\\cite{Moorcroft2014} and soft glassy\nmaterials~\\cite{Fielding2014}. It is consistent with\nexperimental observations in polymers~\\cite{Wangetal2009a,Huetal2007a,Wangetal2003a, Hu2008,\n Wangetal2008c, Hu2005, Hu2010, Wangetal2009d}, carbopol\nmicrogels~\\cite{Divoux2011} and carbon black\nsuspensions~\\cite{Gibaud2010}.\n\nIn the shear startup and step stress experiments just described, the\ntime-dependence is inherently transient in nature: after (typically)\nseveral strain units, the system evolves to its eventual steady state\non the material's flow curve. In any such protocol, for a fluid with\na monotonic constitutive curve that precludes steady state banding,\nany observation of banding is predicted to be limited to this regime\nof time-dependence following the inception of the flow. That poses an\nobvious technical challenge to experimentalists: of imaging the flow\nwith sufficient time-resolution to detect these transient bands. This\nis particularly true for a polymeric fluid with a relatively fast\nrelaxation spectrum. For soft glassy materials, in contrast, the\ndynamics are typically much slower and any bands associated with the\nonset of flow, though technically transient, may persist for a\nsufficiently long time to be mistaken for the material's ultimate\nsteady state response for any practical\npurpose~\\cite{Moorcroft2011,Fielding2014}.\n\nIn the past decade, the rheological community has devoted considerable\nattention to the of study large amplitude oscillatory shear (LAOS).\nFor a recent review, see Ref.~\\cite{Hyun2011}. In this\nprotocol, the applied flow has the form of a sustained oscillation and\nis therefore perpetually time-dependent, in contrast to the transient\ntime-dependence of the shear startup and step stress protocols just\ndescribed. But by analogy with the predictions of transient shear\nbanding in shear startup and step stress, a sustained oscillatory flow\nmight (in certain regimes that we shall discuss) be expected to\nrepeatedly show banding at certain phases of the cycle, or even to\nshow sustained banding round the whole cycle. Importantly, again by\nanalogy with our knowledge of shear startup and step stress, this\neffect need not be limited to fluids with a non-monotonic constitutive\ncurve that show steady state banding in a continuously applied shear\nflow, but might instead arise as a natural consequence of the\ntime-dependence inherent to the oscillation.\n\nIndeed, a particularly attractive feature of LAOS is that the severity\nof the flow's time-dependence, relative to the fluid's intrinsic\ncharacteristic relaxation timescale $\\tau$, can be tuned by varying\nthe frequency $\\omega$ of the applied oscillation. A series of LAOS\nexperiments can thereby explore the full range between steady state\nbehaviour in the limit $\\omega\\to 0$, where the oscillation\neffectively corresponds to a repeated series of quasi-static sweeps up\nand down the flow curve, and strongly time-dependent behaviour for\n$\\omega > 1\/\\tau$. A fluid with a non-monotonic underlying\nconstitutive curve that admits steady state banding is then clearly\nexpected to exhibit banding in the limit of $\\omega\\to 0$, as the\nshear rate quasi-statically transits the plateau in the steady state\nflow curve. In contrast, a monotonic constitutive curve precludes\nbanding for $\\omega\\to 0$. Crucially, though, as noted above, the\nabsence of banding in steady state conditions does not rule out the\npossibility of banding in flows with a strong enough time-dependence,\n$\\omega \\gtrapprox O(1\/\\tau)$.\n\nIndeed, intuitively, a square-wave caricature of a large amplitude\noscillatory shear strain (LAOStrain) experiment points to a perpetual\nswitching between a shear startup like process in the forward\ndirection, followed by `reverse startup' in the opposite direction.\nAny regime in which these startup-like events are associated with an\novershoot in the associated curve of stress as a function of strain\nthen strongly suggests the possibility of shear banding during those\nquasi-startup parts of the cycle, by analogy with the criterion for\nbanding in a true shear startup from rest. In the same spirit, a\nsquare-wave caricature of a large amplitude oscillatory shear stress\n(LAOStress) experiment indicates a perpetually repeated series of step\nstress events, jumping between positive and negative stress values,\nand so admitting the possibility of shear banding if the criterion for\nbanding following a step stress is met.\n\nIn practice, of course, LAOS is more complicated than the caricatures\njust described and the criteria for banding in shear startup and step\nstress might only be expected to apply in certain limiting regimes.\nNonetheless, in what follows we shall show that many of our results\nfor banding in LAOStrain and LAOStress can, to a large extent, be\nunderstood within the framework of these existing criteria for the\nsimpler time-dependent protocols.\n\nExperimentally, shear banding has indeed been observed in LAOS: in\npolymer solutions~\\cite{Wangetal2006c}, dense\ncolloids~\\cite{Cohen2006}, and also in wormlike micellar\nsurfactants that are known to shear band in steady\nstate~\\cite{Dimitriou2012,KateGurnon2012,Gurnon2014a}.\n\nFrom a theoretical viewpoint, several approaches to the interpretation\nof LAOS data have been put forward in the\nliterature~\\cite{Hyun2011}. These include Fourier transform\nrheology~\\cite{Wilhelm2002}; measures for quantifying\nLissajous-Bowditch curves (defined below) in their elastic\nrepresentation of stress versus strain, or viscous representation of\nstress versus strain rate~\\cite{Tee1975}; a decomposition\ninto characteristic sine, square and triangular wave prototypical\nresponse functions~\\cite{Klein2007,Klein2008};\ndecomposition into elastic and viscous stress contributions using\nsymmetry arguments~\\cite{Cho2005}; Chebyshev series\nexpansions of these elastic and viscous\ncontributions~\\cite{Ewoldt2008a}; and interpretations of the\nLAOS cycle in terms of a sequence of physical\nprocesses~\\cite{Rogers2012,Rogers2011}.\n\nHowever, many of these existing theoretical studies assume either\nexplicitly or implicitly that the flow remains homogeneous, and\nthereby fail to take account of the possibility of shear banding. An\nearly exception can be found in\nRefs.~\\cite{Zhou2010,Zhou2008}, which studied a\nmodel of wormlike micellar surfactants with a non-monotonic\nconstitutive curve in LAOStrain. Another exception is in the paper of\nAdams and Olmsted~\\cite{Adams2009}, which recognised that\nshear banding can arise even in the absence of any non-monotonicity in\nthe underlying constitutive curve.\n\nThe work that follows here builds on the remarkable insight of these\nearly papers, in carrying out a detailed numerical study of shear\nbanding in LAOStrain and LAOStress within the Rolie-poly\nmodel~\\cite{Grahametal2003a} of polymers and wormlike\nmicellar surfactant solutions. Consistent with the above discussion,\nin LAOStrain we observe banding at low frequencies $\\omega\\to 0$ and\nsufficiently high strain rate amplitudes $\\dot{\\gamma}\\gtrapprox 1\/\\tau$ in\nfluids for which the underlying constitutive curve of shear stress as\na function of shear rate is non-monotonic. At higher frequencies\n$\\omega=O(1\/\\tau)$ and for sufficiently high strain amplitudes\n$\\gamma\\gtrapprox 1$ we instead see `elastic' shear banding associated\nwith an overshoot in the elastic curve of stress as a function of\nstrain, in close analogy with the elastic banding predicted in a fast\nshear startup\nexperiment~\\cite{Moorcroft2013,Moorcroft2014,Adamsetal2011,Adams2009}.\nImportantly, we show that this elastic banding arises robustly even in\na wide range of model parameter space for which the underlying\nconstitutive curve is monotonic, precluding steady state banding.\n\nIn LAOStress we observe banding in fluids that shear thin sufficiently\nstrongly to have either a negatively, or weakly positively, sloping\nregion in the underlying constitutive curve. We emphasise again that\nfluids in the latter category do not display steady state banding, and\ntherefore that, for such fluids, the banding predicted in LAOStress is\na direct result of the time-dependence of the applied flow. In this\ncase the banding is triggered in each half cycle as the stress magnitude\ntransits in an upward direction the region of weak slope and the\nstrain rate magnitude increases dramatically such that the material effectively\nyields. This is strongly reminiscent of the transient banding\ndiscussed previously in step\nstress~\\cite{Moorcroft2013,Moorcroft2014}.\n\nWhile it would be interesting to interpret our findings within one (or\nmore) of the various mathematical methodologies for analysing LAOS\ndiscussed above (and in particular to consider the implications of\nbanding for the presence of higher harmonics in the output rheological\ntime series), in the present manuscript we focus instead on the\nphysical understanding that can be gained by considering the shapes of\nthe signals of stress versus strain or strain rate (in LAOStrain) and\nstrain rate versus time (in LAOStress). In that sense, this work is\nclosest in spirit to the sequence of physical processes (SPP) approach\nof Refs.~\\cite{Rogers2012,Rogers2011} (which did\nnot, however, explicitly consider heterogeneous response). In\nparticular, we seek to interpret the emergence of shear banding in\nLAOS on the basis of the existing criteria for the onset of banding in\nthe simpler time-dependent protocols of shear startup and step\nstress~\\cite{Moorcroft2013}.\n\nThe paper is structured as follows. In Sec.~\\ref{sec:models} we\nintroduce the model, flow geometry and protocols to be considered.\nSec.~\\ref{sec:methods} outlines the calculational methods that we\nshall use. Sec.~\\ref{sec:recap} contains a summary of previously\nderived linear instability criteria for shear banding in steady shear,\nfast shear startup and step shear stress protocols, with the aim of providing a\nbackdrop to understanding shear banding in oscillatory protocols. In\nSecs.~\\ref{sec:LAOStrain} and~\\ref{sec:LAOStress} we present our\nresults for LAOStrain and LAOStress respectively, and discuss their\npotential experimental verification. Finally\nSec.~\\ref{sec:conclusions} contains our conclusions and an outlook for\nfuture work.\n\n\n\\section{Model, flow geometry and protocols}\n\\label{sec:models}\n\nWe write the stress $\\tens{\\Sigma}(\\tens{r},t)$ at any time $t$ in a\nfluid element at position $\\tens{r}$ as the sum of a viscoelastic\ncontribution $\\tens{\\sigma}(\\tens{r},t)$ from the polymer chains or\nwormlike micelles, a Newtonian contribution characterised by a\nviscosity $\\eta$, and an isotropic contribution with pressure\n$p(\\tens{r},t)$:\n\\begin{equation}\n\\tens{\\Sigma} = \\tens{\\sigma} + 2 \\eta \\tens{D} - p\\tens{I}.\n\\label{eqn: total_stress_tensor}\n\\end{equation}\nThe Newtonian stress $2 \\eta \\tens{D}(\\tens{r},t)$ may arise from the\npresence of a true solvent, and from any polymeric\ndegrees of freedom considered fast enough not to be ascribed their own\nviscoelastic dynamics. The symmetric strain rate tensor $\\tens{D} =\n\\frac{1}{2}(\\tens{K} + \\tens{K}^T)$ where $K_{\\alpha\\beta} =\n\\partial_{\\beta}v_{\\alpha}$ and $\\tens{v}(\\tens{r},t)$ is the fluid\nvelocity field. \n\nWe consider the zero Reynolds number limit of creeping flow, in which\nthe condition of local force balance requires the stress field\n$\\tens{\\Sigma}(\\tens{r},t)$ to be divergence free:\n\\begin{equation}\n\\vecv{\\nabla}\\cdot\\,\\tens{\\Sigma} = 0.\n\\label{eqn: force_balance}\n\\end{equation}\nThe pressure field $p(\\tens{r},t)$ is determined by enforcing that the\nflow remains incompressible:\n\\begin{equation}\n\\label{eqn: incomp}\n\\vecv{\\nabla}\\cdot\\vecv{v} = 0.\n\\end{equation}\n\nThe viscoelastic stress is then written in terms of a constant elastic\nmodulus $G$ and a tensor $\\visc(\\tens{r},t)$ characterising the\nconformation of the polymer chains or wormlike micelles,\n$\\tens{\\sigma} = G\\, (\\visc - \\tens{I})$. We take the dynamics of\n$\\visc$ to be governed by the Rolie-poly (RP)\nmodel~\\cite{Grahametal2003a} with\n\\begin{widetext}\n\\begin{eqnarray}\n\\partial_t{\\visc}+\\tens{v}\\cdot\\nabla\\tens{\\visc} &=& \\tens{K} \\cdot \\visc + \\visc \\cdot \\tens{K}^T - \\frac{1}{\\tau_d}\\left(\\visc - \\tens{I}\\right) \n- \\frac{2(1-A)}{\\tau_R}\\left[\\, \\visc + \\beta A^{-2\\delta}\\left(\\visc - \\tens{I}\\right) \\right] + D\\nabla^2\\visc,\n\\label{eqn: rolie-poly_tensor}\n\\end{eqnarray}\n\\end{widetext}\nin which $A = \\sqrt{3\/T\\,}$ with trace $T = \\text{tr}\\,\\tens{\\visc}$.\nThis RP model is a single mode simplification of the GLAMM model\n\\cite{GLAMM}, which provides a microscopically derived stochastic\nequation for the dynamics of a test chain (or micelle) in its mean\nfield tube of entanglements with other chains. The timescale $\\tau_d$\nsets the characteristic time on which a chain escapes its tube by\nmeans of 1D curvilinear diffusion along the tube's contour, known as\nreptation, allowing the molecular orientation to refresh itself. The\nRouse timescale $\\tau_R$ sets the shorter time on which chain stretch,\nas characterised by $T = \\text{tr}\\,\\tens{\\visc}$, relaxes. The ratio\n$\\tau_d\/\\tau_R=3Z$, where $Z$ is the number of entanglements per chain.\nThe parameters $\\beta$ and $\\delta$ govern a phenomenon known as\nconvective constraint\nrelease~\\cite{Marrucci1996,Ianniruberto2014a,Ianniruberto2014}\n(CCR), in which the relaxation of the stretch of a test chain has the\neffect of also relaxing entanglement points, thereby facilitating the\nrelaxation of tube orientation. The diffusive term $D\\nabla^2\\visc$\nadded to the right hand side of Eqn.~\\ref{eqn: rolie-poly_tensor} is\nrequired to account for the slightly diffuse nature of the interface\nbetween shear bands~\\cite{Luetal2000a}: without it the shear\nrate would be discontinuous across the interface, which is unphysical.\n\nUsing this model we will consider shear flow between infinite flat\nparallel plates at $y = \\{0,L\\}$, with the top plate moving in the\n$\\vecv{\\hat{x}}$ direction at speed $\\overline{\\dot{\\gamma}}(t) L$. We assume\ntranslational invariance in the flow direction $\\vecv{\\hat{x}}$ and\nvorticity direction $\\vecv{\\hat{z}}$ such that the fluid velocity can\nbe written as $\\vecv{v} = v(y,t)\\vecv{\\hat{x}}$. The local shear rate\nat any position $y$ is then given by\n\\begin{equation}\n\\dot{\\gamma}(y,t) = \\partial_{y}v(y,t),\n\\end{equation}\nand the spatially averaged shear rate\n\\begin{equation}\n\\overline{\\dot{\\gamma}}(t) = \\frac{1}{L}\\int_{0}^{L} \\dot{\\gamma}(y,t)dy.\n\\end{equation}\nSuch a flow automatically satisfies the constraint of\nincompressibility, Eqn.~\\ref{eqn: incomp}. The force balance\ncondition, Eqn.~\\ref{eqn: force_balance}, further demands that the\ntotal shear stress is uniform across the cell, in the planar flow\nsituation considered here, giving $\\partial_{y}\\Sigma_{xy} =0$. The\nviscoelastic and Newtonian contributions may, however, each depend on\nspace provided their sum remains uniform:\n\\begin{equation}\n\\Sigma_{xy}(t) = GW_{xy}(y,t) + \\eta \\dot{\\gamma}(y,t).\n\\label{eqn: shear_stress}\n\\end{equation}\n\nFor such a flow, the RP model can be written componentwise as\n\\begin{widetext}\n\\begin{eqnarray}\n\\dot{W}_{xy} &=& \\dot{\\gamma} W_{yy} - \\frac{W_{xy}}{\\tau_d} - \\frac{2(1-A)}{\\tau_R}(1+ \\beta A)W_{xy} + D\\partial_y^2 W_{xy}, \\nonumber\\\\\n\\dot{W}_{yy} &=& - \\frac{W_{yy}-1}{\\tau_d} - \\frac{2(1-A)}{\\tau_R}\\left[W_{yy}+ \\beta A(W_{yy}-1)\\right]+ D\\partial_y^2W_{yy},\\nonumber\\\\\n\\dot{T} &=& 2\\dot{\\gamma}W_{xy} - \\frac{T-3}{\\tau_d} - \\frac{2(1-A)}{\\tau_R}\\left[T + \\beta A(T - 3)\\right]+ D\\partial_y^2 T.\\quad \\quad\n\\label{eqn: sRP_components}\n\\end{eqnarray}\n(The other components of $\\tens{W}$ decouple to form a separate\nequation set, with trivial dynamics.) In the limit of fast chain\nstretch relaxation $\\tau_R \\to 0$ we obtain the simpler\n`non-stretching' RP model in which the trace $T=3$ and\n\\begin{eqnarray}\n\\dot{W}_{xy} &=& \\dot{\\gamma} \\left[W_{yy} - \\frac{2}{3} (1+\\beta)W_{xy}^2\\right]\\;\\;\\;\\;\\;\\;\\; -\\frac{1}{\\tau_d}W_{xy},+ D\\partial_y^2 W_{xy}\\nonumber\\\\\n\\dot{W}_{yy} &=& \\frac{2}{3}\\dot{\\gamma}\\left[\\betaW_{xy}-(1+\\beta)W_{xy}W_{yy} \\right] - \\frac{1}{\\tau_d}(W_{yy}-1)+ D\\partial_y^2W_{yy}. \\quad \\quad\n\\label{eqn: nRP_components}\n\\end{eqnarray}\n\\end{widetext}\nFor convenient shorthand we shall refer to this simpler non-stretching\nform as the nRP model. We refer to the full `stretching' model of\nEqns.~\\ref{eqn: sRP_components} as the sRP model.\n\nFor boundary conditions at the walls of the flow cell we assume no\nslip and no permeation for the fluid velocity, and zero-gradient\n$\\partial_y W_{\\alpha\\beta}=0$ for every component $\\alpha\\beta$ of\nthe polymeric conformation tensor.\n\nIn what follows we consider the behaviour of the Rolie-poly model in the following two flow protocols:\n\n\\begin{itemize}\n\n\\item LAOStrain, with an imposed strain \n\\begin{equation}\n\\gamma(t)=\\gamma_0\\sin(\\omega t),\n\\end{equation}\nto which corresponds the strain rate\n\\begin{equation}\n\\dot{\\gamma}(t)=\\gamma_0\\omega\\cos(\\omega t)=\\dot{\\gamma}_0\\cos(\\omega t).\n\\end{equation}\n\n\\item LAOStress, with an imposed stress\n\\begin{equation}\n\\Sigma(t)=\\Sigma_0\\sin(\\omega t).\n\\end{equation}\n\n\\end{itemize}\n\nThe model, flow geometry and protocol just described are characterised\nby the following parameters: the polymer modulus $G$, the reptation\ntimescale $\\tau_d$, the stretch relaxation timescale $\\tau_R$, the CCR\nparameters $\\beta$ and $\\delta$, the stress diffusivity $D$, the\nsolvent viscosity $\\eta$, the gap size $L$, the frequency $\\omega$ and\nthe amplitude $\\gamma_0$ (for LAOStrain) or $\\Sigma_0$ (for\nLAOStress). We are free to choose units of mass, length and time,\nthereby reducing the list by three: we work in units of length in\nwhich the gap size $L = 1$, of time in which the reptation time $\\tau_d\n= 1$ and of mass (or actually stress) in which the polymer modulus\n$G=1$. We then set the value of the diffusion constant $D$ such that\nthe interface between the bands has a typical width $\\ell =\n\\sqrt{D\\tau_d}=2\\times 10^{-2}L$, much smaller than the gap size. This\nis the physically relevant regime for the macroscopic flow cells of\ninterest here, and we expect the results we report to be robust to\nreducing $l$ further. Following Ref.~\\cite{Grahametal2003a} we set\n$\\delta = -\\frac{1}{2}$.\n\nAdimensional quantities remaining to be explored are then the model\nparameters $\\eta$, $\\beta$ and (for the sRP model only) $\\tau_R$; and\nthe protocol parameters $\\omega$ and $\\gamma_0$ or $\\Sigma_0$. For\neach set of model parameters we explore the whole plane of feasibly\naccessible values of protocol parameters $\\omega$ and $\\gamma_0$ or\n$\\Sigma_0$.\n\nAmong the model parameters the CCR parameter has the range $0 \\leq\n\\beta \\leq 1$. Within this there is no current consensus as to its\nprecise value, and we shall therefore explore widely the full range\n$0\\to 1$. For the fluids of interest here the Newtonian viscosity is\ntypically much smaller than the zero shear viscosity of the\nviscoelastic component, giving $\\eta\\ll 1$ in our units. Based on a\nsurvey of the experimental data, a range of $10^{-7}$ to $10^{-3}$ was\nsuggested by Graham et al. in Ref.~\\cite{Graham2013}\nConsistentwith comments made in Ref.~\\cite{Agimelen2013} we find values\nless than $10^{-5}$ unfeasible to explore numerically, due to a\nresulting large separation of timescales between $\\tau_d$ and $\\eta\/G$.\nTherefore we adopt typical values $\\eta=10^{-4}$ and $10^{-5}$. Given\nthat that the susceptibility to shear banding increases with\ndecreasing $\\eta$, we note that the levels of banding reported in what\nfollows are likely, if anything, to be an underestimate of what might\nbe observed experimentally. We return in our concluding remarks to\ndiscuss this issue further.\n\nWe explore a wide range of values of the stretch relaxation time\n$\\tau_R$, or equivalently of the degree of entanglement\n$Z=\\tau_d\/3\\tau_R$: we consider $Z=1$ to $350$ for the sRP model (and\nnote that the nRP model has $Z\\to\\infty$ by definition).\nExperimentally, values of $Z$ in the range of $50$ appear commonplace\nand $100$ towards the upper end of what might currently be used\nexperimentally in nonlinear rheological studies. One of the\nobjectives of this work is to provide a roadmap of values of $Z$ and\n$\\beta$ in which shear banding is expected to be observed, for typical\nsmall values of $\\eta$, in a sequence of LAOS protocols that scan\namplitude and frequency space.\n\n\\section{Calculation methods} \n\\label{sec:methods}\n\nIn this section we outline the theoretical methods to be used\nthroughout the paper. In order to develop a generalised framework\nencompassing both the nRP and sRP models, we combine all the relevant\ndynamical variables (for any given model) into a state vector\n$\\vecv{s}$, with $\\vecv{s}=(W_{xy},W_{yy})^T$ for the nRP model and\n$\\vecv{s}=(W_{xy},W_{yy},T)^T$ for the sRP model. Alongside this we\ndefine a projection vector $\\vecv{p}$ of corresponding dimensionality\n$d$, with $\\vecv{p}=(1,0)$ for the nRP model and $\\vecv{p}=(1,0,0)$\nfor sRP.\n\nThe total shear stress $\\Sigma_{xy}=\\Sigma$, from which we drop the\n$xy$ subscript for notational brevity, is then given by\n\\begin{equation}\n\\label{eqn: governing_eqn_force}\n\\Sigma(t) = G\\vecv{p} \\cdot \\vecv{s}(y,t) + \\eta \\dot{\\gamma}(y,t),\n\\end{equation}\nand the viscoelastic constitutive equation has the generalised form\n\\begin{equation}\n\\partial_{t\\,}\\vecv{s}(y,t) = \\vecv{Q}(\\vecv{s},\\dot{\\gamma}) + D\\partial_y^2\\vecv{s}.\n\\label{eqn: governing_eqn_diffusive}\n\\end{equation}\nThe dimensionality and functional form of $\\vecv{Q}$ then specify the\nparticular constitutive model. In this way our generalised notation in\nfact encompasses not only the nRP model (for which $d=2$) and sRP\nmodel (for which $d=3$) but many more besides, including the Johnson\nSegalman, Giesekus and Oldroyd B models~\\cite{Larson1988}.\n\n\\subsection{Homogeneous base state}\n\\label{sec:base}\n\nFor any given applied flow our approach will be first to calculate the\nfluid's response within the simplifying assumption that the\ndeformation must remain homogeneous across the cell. While this is an\nartificial (and indeed incorrect) constraint in any regime where shear\nbanding is expected, it nonetheless forms an important starting point\nfor understanding the mechanism by which shear banding sets in. (We\nalso note that most papers in the literature make this assumption\nthroughout, thereby disallowing any possibility of shear banding\naltogether.)\n\nWithin this assumption of homogeneous flow, the response of the system\nfollows as the solution to the set of ordinary differential equations\n\\begin{equation}\n\\label{eqn: governing_eqn_force_local}\n\\base{\\Sigma}(t) = G\\vecv{p} \\cdot \\base{\\vecv{s}}(t) + \\eta \\base{\\dot{\\gamma}}(t),\n\\end{equation}\nand\n\\begin{equation} \n\\dot{\\base{\\vecv{s}}}(t) = \\vecv{Q}(\\base{\\vecv{s}},\\base{\\dot{\\gamma}}).\n\\label{eqn: governing_eqn_diffusive_local}\n\\end{equation}\nIn these either $\\base{\\dot{\\gamma}}(t)$ or $\\base{\\Sigma}(t)$ is imposed, in\nLAOStrain and LAOStress respectively, and the other dynamical\nquantities are calculated numerically using an explicit Euler\nalgorithm~\\cite{NumRecipes}. We use the `hat' notation to denote\nthat the state being considered is homogeneous.\n\n\\subsection{Linear stability analysis}\n\\label{sec:lsa}\n\nHaving calculated the behaviour of the fluid within the assumption\nthat the flow remains homogeneous, we now proceed to consider whether\nthis homogeneous `base state' flow will, at any point during an applied\noscillatory protocol, be unstable to the formation of shear bands. To\ndo so we add to the base state, for which we continue to use the hat\nnotation, heterogeneous perturbations of (initially) small amplitude:\n\\begin{eqnarray}\n\\Sigma(t) &=& \\base{\\Sigma}(t),\\nonumber\\\\\n\\dot{\\gamma}(y,t)&=& \\base{\\dot{\\gamma}}(t) + \\sum_{n=1}^\\infty \\delta \\dot{\\gamma}_n(t) \\cos(n\\pi y\/L),\\nonumber\\\\\n\\vecv{s}(y,t) &=& \\vecv{\\base{s}}(t) + \\sum_{n=1}^\\infty \\delta\\vecv{s}_n(t) \\cos(n\\pi y\/L).\n\\label{eqn: LSA}\n\\end{eqnarray}\nNote that the total stress $\\Sigma$ is not subject to heterogeneous\nperturbations because the constraint of force balance decrees that it\nmust remain uniform across the gap, at least in a planar shear cell.\nSubstituting Eqns.~\\ref{eqn: LSA} into Eqns.~\\ref{eqn:\n governing_eqn_force} and~\\ref{eqn: governing_eqn_diffusive}, and\nexpanding in successive powers of the magnitude of the small\nperturbations $\\delta{\\dot{\\gamma}_n},\\vecv{\\delta s_n}$, we recover at\nzeroth order Eqns.~\\ref{eqn: governing_eqn_force_local} and~\\ref{eqn:\n governing_eqn_diffusive_local} for the dynamics of the base state. At first order the heterogeneous perturbations obey\n\\begin{eqnarray}\n\\label{eqn: perturbation}\n0&=&G\\tens{p}\\cdot \\delta\\vecv{s}_n(t)+\\eta\\delta\\dot{\\gamma}_n(t),\\nonumber\\\\\n\\dot{\\delta\\vecv{s}}_n &=& \\tens{M}(t) \\cdot \\delta\\vecv{s}_n + \\tens{q}\\delta{\\dot{\\gamma}}_n,\n\\end{eqnarray}\nin which $\\tens{M} =\n\\partial_{\\vecv{s}\\,}\\vecv{Q}|_{\\vecv{\\base{s}},\\base{\\dot{\\gamma}}}-\\tens{\\delta}D(n\\pi\/L)^2$\nand $\\vecv{q}\n= \\partial_{\\dot{\\gamma}}\\vecv{Q}|_{\\vecv{\\base{s}},\\base{\\dot{\\gamma}}}$. Combining\nthese gives\n\\begin{equation}\n\\label{eqn: one}\n\\dot{\\delta\\vecv{s}}_n = \\tens{P}(t) \\cdot \\delta\\vecv{s}_n,\n\\end{equation}\nwith\n\\begin{equation}\n\\tens{P}(t) = \\tens{M}(t) - \\frac{G}{\\eta}\\vecv{q}(t)\\, \\vecv{p}.\n\\label{eqn: two}\n\\end{equation}\nIn any regime where the heterogeneity remains small, terms of second\norder and above can be neglected.\n\nTo determine whether at any time $t$ during an imposed oscillatory\nflow the heterogeneous perturbations\n$\\delta{\\dot{\\gamma}}_n,\\delta\\vecv{s}_n(t)$ have positive rate of growth,\nindicating linear instability of the underlying homogeneous base state\nto the onset of shear banding, we consider first of all the\ninstantaneous sign of the eigenvalue $\\lambda(t)$ of $\\tens{P}(t)$ that\nhas the largest real part. A positive value of $\\lambda(t)$ is clearly\nsuggestive that heterogeneous perturbations will be instantaneously\ngrowing at that time $t$. We note, however, that the concept of a\ntime-dependent eigenvalue must be treated with caution. In view of\nthis we cross check predictions made on the basis of the eigenvalue by\nalso directly numerically integrating the linearised Eqns.~\\ref{eqn:\n one} using an explicit Euler algorithm. This allows us to determine\nunambiguously whether the heterogeneous perturbations will be at any\ninstant growing (taking the system towards a banded state) or decaying\n(restoring a homogeneous state), at the level of this linear\ncalculation.\n\nIn these linear stability calculations we neglect the diffusive term\nin the viscoelastic constitutive equation, setting $D=0$. Reinstating\nit would simply transform any eigenvalue $\\lambda \\to \\lambda_n=\\lambda -\nD n^2\\pi^2\/L^2$ and provide a mechanism whereby any heterogeneity with\na wavelength of order the microscopic lengthscale $l$, or below,\ndiffusively decays. Accordingly the results of this linear calculation\nonly properly capture the dynamics of any heterogeneous perturbations\nthat have macroscopically large wavelengths, which are the ones of\ninterest in determining the initial formation of shear bands starting\nfrom a homogeneous base state.\n\nAs a measure of the degree of flow heterogeneity at any time $t$ in\nthis linear calculation, we shall report in our results sections below\n$\\delta\\dot{\\gamma} (t)$ normalised by the amplitude of the imposed\noscillation $\\dot{\\gamma}_0$ in LAOStrain, or by $1+|\\dot{\\gamma}(t)|$ in LAOStress,\nwhere $\\dot{\\gamma}(t)$ is the instantaneous value of the shear rate. (We\nfind numerically that bands tend to form in LAOStress when\n$|\\dot{\\gamma}(t)|\\gg 1$. The additional 1 in the normalisation is used\nsimply to prevent the divergence of this measure when $\\dot{\\gamma}(t)$ passes\nthrough 0 in each half cycle.) Note that we no longer need to specify\nthe mode number $n$ for $\\delta\\dot{\\gamma}$, because within the assumption\n$D=0$ just described, we are confining our attention to the limit of\nlong wavelength modes only and noting them all to have the same\ndynamics, to within small corrections set by $D$.\n\n\\subsection{Full nonlinear simulation}\n\\label{sec:nonlinear}\n\nWhile the linear analysis just described provides a calculationally\nconvenient method for determining whether shear banding will arise in\nany given oscillatory measurement, enabling us to quickly build up an\noverall roadmap of parameter space, it cannot predict the detailed\ndynamics of the shear bands once the amplitude of heterogeneity has\ngrown sufficiently large that nonlinear effects are no longer\nnegligible. Therefore in what follows we shall also perform full\nnonlinear simulations of the model's spatio-temporal dynamics by\ndirectly integrating the full model Eqns.~\\ref{eqn:\n governing_eqn_force} and~\\ref{eqn: governing_eqn_diffusive} using a\nCrank-Nicolson algorithm \\cite{NumRecipes}, with the system's\nstate discretised on a grid of $J$ values of the spatial coordinate\n$y$, checked in all cases for convergence with respect to increasing\nthe number of grid points.\n\nAs a measure of the degree of shear banding at any time $t$ in this\nnonlinear calculation we report the difference between the maximum and\nminimum values of the shear rate across the cell:\n\\begin{equation}\n\\Delta_{\\dot{\\gamma}}(t) = \\frac{1}{N}\\Big[|\\dot{\\gamma}_{\\rm max}(t) - \\dot{\\gamma}_{\\rm min}(t)|\\Big],\n\\label{eqn: dob}\n\\end{equation}\nagain normalised depending upon the employed protocol, by $N$, where $N$ is the amplitude of the imposed oscillation $\\dot{\\gamma}_0$ in LAOStrain, and $1+|\\dot{\\gamma}(t)|$ in LAOStress.\n\n\\subsection{Seeding the heterogeneity}\n\\label{sec:seed}\n\nWhen integrating the model equations to determine the time evolution\nof any flow heterogeneity, whether linearised or in their full\nnonlinear form, we must also specify the way in which whatever\nheterogeneous perturbations that are the precursor to the formation of\nshear bands are seeded initially. Candidates include any residual\nheterogeneity left in the fluid by the initial procedure of sample\npreparation; imperfections in the alignment of the rheometer plates;\ntrue thermal noise with an amplitude set by $k_{\\rm B}T$; and\nrheometer curvature in cone-and-plate or cylindrical Couette devices.\nWe consider in particular the last of these because it is likely to be\nthe dominant source of heterogeneity in commonly used flow cells,\nwhich typically have a curvature of about $10\\%$.\n\nWhile modelling the full effects of curvature is a complicated task,\nits dominant consequence can be captured simply by including a slight\nheterogeneity in the total stress field. (The assumption made above of\na uniform stress across the gap only holds in an idealised planar\ndevice.) Accordingly we set $\\Sigma(t)\\to\\Sigma(t)\\left[1+q\n h(y)\\right]$ where $q$ sets the amplitude of the curvature and\n$h(y)$ is a function with an amplitude of $O(1)$ that prescribes its\nspatial dependence. The detailed form of $h(y)$ will differ from\ndevice to device: for example in a cylindrical Couette it is known to\nhave a $1\/r^2$ dependence, where $r$ is the radial coordinate.\nHowever, the aim here is not to model any particular device geometry\nin detail, but simply to capture the dominant effect of curvature in\nseeding the flow heterogeneity. Accordingly we set\n$h(y)=\\cos(\\pi\/L)$ which is the lowest Fourier mode to fit into the\nsimulation cell while still obeying the boundary conditions at the\nwalls.\n\n\\section{Shear banding in other time dependent protocols}\n\\label{sec:recap}\n\nAs a preamble to presenting our results for shear banding in\noscillatory flow protocols in the next two sections below, we first\nbriefly collect together criteria derived in previous work for linear\ninstability to the formation of shear bands in simpler time-dependent\nprotocols: slow shear rate sweep, fast shear startup, and step stress.\n\n\\subsection{Slow shear rate sweep}\n\\label{sec:recapSweep}\n\nA common experimental protocol consists of slowly sweeping the shear\nrate $\\dot{\\gamma}$ upwards (or downwards) in order to measure a fluid's\n(quasi) steady state flow curve. In this protocol the criterion for\nlinear instability to the onset of shear banding, given a base state\nof initially homogeneous shear flow, has long been known to\nbe~\\cite{Yerushalmi1970}\n\\begin{equation}\n\\label{eqn:criterionSteady}\n\\frac{\\partial\\Sigma}{\\partial\\dot{\\gamma}} < 0.\n\\end{equation}\n\n\\subsection{Fast shear startup}\n\\label{sec:recapStartup}\n\nAnother common experimental protocol consists of taking a sample of\nfluid that is initially at rest and with any residual stresses well\nrelaxed, then suddenly jumping the strain rate from zero to some\nconstant value such that $\\dot{\\gamma}(t)=\\dot{\\gamma}_0\\Theta(t)$, where\n$\\Theta(t)$ is the Heaviside function. Commonly measured in response\nto this applied flow is the time-dependent stress signal $\\Sigma(t)$\nas it evolves towards its eventual steady state value, for that\nparticular applied shear rate, on the fluid's flow curve. This\nevolution typically has the form of an initial elastic regime with\n$\\Sigma\\approx G\\gamma$ while the strain $\\gamma$ remains small,\nfollowed by an overshoot in the stress at a strain of $O(1)$, then a\ndecline to the final steady state stress on the flow curve. In\nRef.~\\cite{Moorcroft2013,Moorcroft2014,Adamsetal2011}\nwe gave evidence that the presence of an overshoot in this stress\nstartup signal is generically indicative of a strong tendency to form\nshear bands, at least transiently. These bands may, or may not, then\npersist for as long as the shear remains applied, according to whether\nor not the underlying constitutive curve of stress as a function of\nstrain rate is non-monotonic.\n\nSuch behaviour is to be expected intuitively. Consider a shear startup\nrun performed at a high enough strain rate that the material's\nresponse is initially elastic, with the stress startup signal\ndepending only on the accumulated strain $\\gamma=\\dot{\\gamma} t$ and not\nseparately on the strain rate $\\dot{\\gamma}$. The decline in stress following\nan overshoot in the stress startup signal corresponds to a negative\nderivative\n\\begin{equation}\n\\label{eqn:criterionSimpleElastic}\n\\frac{\\partial \\Sigma}{\\partial\\gamma}<0.\n\\end{equation}\nThis clearly has the same form as~(\\ref{eqn:criterionSteady}) above,\nwith the strain rate now replaced by the strain. As such it is the\ncriterion that we might intuitively expect for the onset of strain\nbands in a nonlinear elastic solid, following the early intuition of\nRef.~\\cite{Marrucci1983}\n\nIn close analogy to this intuitive expectation, for a complex fluid\nsubject to a fast, elastically dominated startup the criterion for the\nonset of banding was shown in Ref.~\\cite{Moorcroft2013} to be\nthat the stress signal $\\Sigma(\\gamma=\\dot{\\gamma} t)$ of the initially\nhomogeneous startup flow obeys\n\\begin{equation} \n\\label{eqn:criterionStartup}\n-\\textrm{tr}\\tens{M} \\frac{\\partial \\Sigma}{\\partial\\gamma} +\n\\dot{\\gamma}\\frac{\\partial^2\\Sigma}{\\partial\\gamma^2} < 0,\n\\end{equation}\nwhere $\\textrm{tr}\\tens{M}<0$ in this startup protocol. This result\nholds exactly for any model whose equations are of the generalised\nform in Sec.~\\ref{sec:methods} above, and have only two relevant\ndynamical variables, $d=2$. (Recall that for the nRP model these two\nvariables are the shear stress $W_{xy}$ and one component of normal\nstress $W_{yy}$, in units in which the polymer modulus $G=1$.) The\ncriterion~(\\ref{eqn:criterionStartup}) closely resembles the simpler\nform~(\\ref{eqn:criterionSimpleElastic}) motivated intuitively above,\nwith an additional term informed by the curvature in the signal of\nstress as a function of strain. The effect of this additional term is\nto trigger the onset of banding just {\\em before} overshoot, as the\nstress startup signal starts to curve downwards from its initial\nregime of linear elastic response.\n\nWhat this criterion tells us is that the presence of an overshoot in the\nstress signal of an underlying base state of initially homogeneous shear startup acts as a causative trigger for the formation of shear bands. A common misconception is that instead it is the onset of shear\nbanding that causes the stress drop. While it is true that the onset\nof banding may reduce the stress further compared to that expected on\nthe basis of a homogeneous calculation, we emphasise that the\ndirection of mechanistic causality here is that the stress drop\nfollowing overshoot causes shear banding and not (primarily) vice\nversa.\n\nWith criterion (\\ref{eqn:criterionStartup}) in mind, theorists should\nbe alert that any model predicting startup stress overshoot in a\ncalculation in which the flow is artificially constrained to remain\nhomogeneous is likely to further predict the formation of shear bands\nin a full heterogeneous calculation that allows bands to form. Likewise\nexperimentalists should be alert that any observations of stress\novershoot in shear startup is strongly suggestive of the presence of\nbanding in the material's flow profile.\n\nIn Ref.~\\cite{Moorcroft2014} the analytically derived\ncriterion~(\\ref{eqn:criterionStartup}) was confirmed numerically for\nfast shear startup in the nRP model, where it should indeed apply\nexactly due to the presence of just $d=2$ relevant dynamical variables\n$W_{xy}$ and $W_{yy}$ in that model. It was also shown to apply to\ngood approximation in the sRP model, for which $d=3$, for strain rates\nlower than the inverse stretch relaxation time (where the dynamics of\nthe sRP model indeed well approximate those of the nRP model).\n\nBanding associated with startup stress overshoot has also been\ndemonstrated in several numerical studies of soft glassy materials\n(SGMs)~\\cite{Moorcroft2011,Fielding2014,Manningetal2009a}.\n(The term SGM is used to describe a broad class of materials including\nfoams, emulsions, colloids, surfactant onion phases and microgels, all\nof which show structural disorder, metastability, a yield stress, and\noften also rheological ageing below the yield stress.) In these soft\nglasses, however, it should be noted that the decrease in stress\nfollowing the startup overshoot arises from increasing plasticity\nrather than falling elasticity. This makes it more difficult to derive\nan analytical criterion analogous to~(\\ref{eqn:criterionStartup}).\nAccordingly the theoretical evidence for shear banding following\nstartup overshoot in these soft glasses, while very convincing,\nremains primarily numerical to date.\n\nConsistent with these theoretical predictions, experimental\nobservations of banding associated with startup stress overshoot are\nwidespread: in wormlike micellar surfactants~\\cite{Hu2008,\n Wangetal2008c}, polymers~\\cite{Wangetal2008a, Wangetal2008b,\n Wangetal2009a, Wangetal2009b, Boukany2009a, Wangetal2003a,\n Huetal2007a, Wangetal2009d}, carbopol\ngels~\\cite{Divoux2011a,Divoux2010} and Laponite clay\nsuspensions~\\cite{Martin2012}. Nonetheless, we also note other\n studies of polymer solutions~\\cite{Li2015} where stress overshoot is\n seen without observable banding. It would be particularly\n interesting to see further experimental work on polymeric fluids to\n delineate more fully the regimes, for example of entanglement number\n and degree of polydispersity, in which banding arises with\n sufficient amplitude to be observed experimentally.\n\n\n\n\\subsection{Step stress}\n\\label{sec:recapCreep}\n\nBesides the strain-controlled protocols just described, a fluid's\nrheological behaviour can also be probed under conditions of imposed\nstress. In a step stress experiment, an initially well relaxed fluid\nis suddenly subject to the switch-on of a shear stress $\\Sigma_0$ that\nis held constant thereafter, such that $\\Sigma(t)=\\Theta(t)\\Sigma_0$.\nCommonly measured in response to this applied stress is the material's\ncreep curve, $\\gamma(t)$, or the temporal derivative of this,\n$\\dot{\\gamma}(t)$. In Ref.~\\cite{Moorcroft2013} the criterion for\nlinear instability to the formation of shear bands, starting from a\nstate of initially homogeneous creep shear response, was shown to be\nthat\n\\begin{equation}\n\\frac{\\partial^2\\dot{\\gamma}}{\\partial t^2}\/\\frac{\\partial\\dot{\\gamma}}{\\partial t}>0.\n\\end{equation}\nThis tells us that shear banding should be expected in any step stress\nexperiment in which the differentiated creep response curve\nsimultaneously curves upwards and slopes upwards. (Indeed it should\nalso be expected in any experiment where that response function\nsimultaneously curves downwards and slopes downwards, though we do not\nknow of any instances of such behaviour.) This prediction has been\nconfirmed numerically in the Rolie-poly model of polymers and wormlike\nmicelles~\\cite{Moorcroft2014}, as well as in the soft glassy\nrheology model of foams, dense emulsions, microgels, {\\it\n etc}~\\cite{Fielding2014}.\n\n\\begin{figure}[tbp]\n\\includegraphics[width=9.0cm]{figure1.eps}\n\\caption{LAOStrain: sketch of regions of shear rate amplitude and\n frequency space in which we expect limiting low frequency `viscous'\n and high frequency `elastic' behaviours, and regimes of linear and\n nonlinear response. LAOStrain runs at the locations marked $X_L$ and\n $X_H$ are explored in Figs.~\\ref{fig:nonmon} and~\\ref{fig:mon} for\n the nRP model with non-monotonic and monotonic underlying\n constitutive curve respectively.}\n\\label{fig:sketch}\n\\end{figure}\n\nExperimentally, shear banding associated with a simultaneously\nupwardly curving and upwardly sloping differentiated creep response\ncurve has indeed been seen in in entangled\npolymers~\\cite{Wangetal2009a,Huetal2007a,Wangetal2003a, Hu2008,\n Wangetal2008c, Hu2005, Hu2010, Wangetal2009d}, carbopol\nmicrogels~\\cite{Divoux2011} and carbon black\nsuspensions~\\cite{Gibaud2010}.\n\n\\section{Large amplitude oscillatory strain}\n\\label{sec:LAOStrain}\n\n\nWe now consider shear banding in the time-dependent strain-imposed\noscillatory protocol of LAOStrain. Here a sample of fluid, initially\nwell relaxed at time $t=0$, is subject for times $t>0$ to a strain of\nthe form\n\\begin{equation}\n\\gamma(t)=\\gamma_0\\sin(\\omega t),\n\\end{equation}\nto which corresponds the strain rate\n\\begin{equation}\n\\dot{\\gamma}(t)=\\gamma_0\\omega\\cos(\\omega t)=\\dot{\\gamma}_0\\cos(\\omega t).\n\\end{equation}\nAfter an initial transient, once many cycles have been executed, the\nresponse of the system is expected to attain a state that is\ntime-translationally invariant from cycle to cycle, $t\\to\nt+2\\pi\/\\omega$. All the results presented below are in this long-time\nregime, usually for the $N=20$th cycle after the flow\ncommenced. The dependence of the stress on the cycle number was carefully studied in wormlike micelles in Ref.~\\cite{Fujii2015}.\n\nTo characterise any given applied LAOStrain we must clearly specify\ntwo quantities: the strain amplitude and the frequency\n$(\\gamma_0,\\omega)$, or alternatively the strain rate amplitude and\nthe frequency $(\\dot{\\gamma}_0,\\omega)$, where $\\dot{\\gamma}_0=\\gamma_0\\omega$. In\nwhat follows we usually choose the latter pairing $(\\dot{\\gamma}_0,\\omega)$.\nAny given LAOStrain experiment is then represented by its location in\nthat plane of $\\dot{\\gamma}_0$ and $\\omega$. See Fig.~\\ref{fig:sketch}.\n\nIn any experiment where the applied strain rate remains small,\n$\\dot{\\gamma}_0 \\ll 1$, a regime of linear response is expected. (Recall\nthat in dimensional form this condition corresponds to $\\dot{\\gamma}_0\\tau_d\n\\ll 1$.) But even in an experiment where the strain rate does not\nremain small, linear response can nonetheless still be expected if the\noverall applied strain remains small, $\\gamma_0 \\ll 1$. Accordingly,\nlinear response should obtain in the region below the long-dashed line\nmarked in Fig.~\\ref{fig:sketch}. Because shear banding is an\ninherently nonlinear phenomenon, we expect the interesting region of\nthis $(\\dot{\\gamma}_0,\\omega)$ plane from our viewpoint to be in the\nnonlinear regime, above the long-dashed line, and we focus our\nattention mostly on this in what follows.\n\n\n\nBesides considering whether any given applied LAOStrain will result in\nlinear or nonlinear response, also relevant is the characteristic\ntimescale $1\/\\omega$ of the oscillation compared to the fluid's\nintrinsic terminal relaxation timescale $\\tau_d=1$. For low\nfrequencies $\\omega\\ll 1$, to the left of the leftmost dotted line in\nFig.~\\ref{fig:sketch}, we expect the material's reconfiguration\ndynamics to keep pace with the applied deformation. This will lead to\nquasi steady state response in which the stress slowly sweeps up\nand down the steady state flow curve as the shear rate varies through\na cycle. In contrast for high frequencies $\\omega\\gg 1$, to the right\nof the rightmost dotted line in Fig.~\\ref{fig:sketch}, the material's\nrelaxation dynamics cannot keep pace with the applied deformation and\nwe expect elastic-like response.\n\nWe illustrate these two limiting regimes by studying the response of\nthe nRP model to an imposed LAOStrain at each of the two locations\nmarked $X_L$ and $X_H$ in Fig.~\\ref{fig:sketch}. For simplicity, for\nthe moment, we artificially constrain the flow to remain homogeneous\nand confine ourselves to calculating the uniform `base state' as\noutlined in Sec.~\\ref{sec:base}. The results are shown in\nFig.~\\ref{fig:nonmon} for the nRP model with parameters for which the\nunderlying constitutive curve is non-monotonic, such that (in any\nheterogeneous calculation) the fluid would show shear banding under\nconditions of steady applied shear. Fig.~\\ref{fig:mon} shows results\nwith model parameters for which the constitutive curve is monotonic,\nsuch that no banding would be expected in steady shear flow.\n\n\nThe left panels of Figs.~\\ref{fig:nonmon} and~\\ref{fig:mon} contain\nresults for the low frequency oscillation marked $X_L$ in\nFig.~\\ref{fig:sketch}. Here we choose to plot the stress response\n$\\Sigma(t)$ in a Lissajous-Bowditch figure as a parametric function of\nthe time-varying imposed strain rate $\\dot{\\gamma}(t)$, consistent with the\nexpectation of fluid-like response in this low-frequency regime.\n(Throughout the paper we shall describe such a plot of stress versus strain rate as being in the\n`viscous' representation.) As can be seen, in each case the fluid\nindeed tracks up and down its (quasi) steady state homogeneous\nconstitutive curve $\\Sigma(\\dot{\\gamma})$ in the range $-\\dot{\\gamma}_0 < \\dot{\\gamma} <\n\\dot{\\gamma}_0$. For any set of model parameters, several of these LAOStrain\nresponse curves $\\Sigma(\\dot{\\gamma})$ collected together for different\n$\\dot{\\gamma}_0$ and low frequency $\\omega$ would all collapse onto this\nmaster constitutive curve.\n\nAlso shown by the colour scale in the left panels of\nFigs.~\\ref{fig:nonmon} and~\\ref{fig:mon} is the eigenvalue as\nintroduced in Sec.~\\ref{sec:lsa}. Recall that a positive eigenvalue at\nany point in the cycle strongly suggests that the homogeneous base\nstate is linearly unstable to the development of shear banding at that\npoint in the cycle. (In any region where this scale shows black the\neigenvalue is either negative, or so weakly positive as to cause only\nnegligible banding growth.) As expected, a regime of instability is\nindeed seen in Fig.~\\ref{fig:nonmon}, in the region\nwhere the constitutive curve has negative slope,\n\\begin{equation}\n\\frac{\\partial\\Sigma}{\\partial\\dot{\\gamma}} <0.\n\\end{equation}\nFor a fluid with a monotonic constitutive curve, no instability is\nobserved at this low frequency (Fig.~\\ref{fig:mon}, left).\n\n\\begin{figure}[tbp]\n\\includegraphics[width=8.0cm]{figure2.eps}\n\\caption{LAOStrain in the nRP model with a non-monotonic underlying\n constitutive curve. Model parameters $\\beta=0.4$, $\\eta=10^{-5}$.\n {\\bf Left:} Viscous Lissajous-Bowditch\n figure shows stress $\\Sigma$ versus strain rate $\\dot{\\gamma}$ for an\n imposed frequency and strain rate $(\\omega,\\dot{\\gamma}_0)=(0.001,50.0)$\n marked as $X_L$ in the low frequency regime of\n Fig.~\\ref{fig:sketch}. {\\bf Right:} Elastic Lissajous-Bowditch figure shows\n stress $\\Sigma$ versus strain $\\gamma$ for an imposed frequency and\n strain rate $(\\omega,\\dot{\\gamma}_0)=(31.6,200.0)$ marked as $X_H$ in the\n high frequency regime of Fig.~\\ref{fig:sketch}. Colourscale shows\n eigenvalue.}\n\\label{fig:nonmon}\n\\end{figure}\n\n\n\n\nThe corresponding results for the high frequency run marked $X_H$ in\nFig.~\\ref{fig:sketch} are shown in the right panels of\nFigs.~\\ref{fig:nonmon} and~\\ref{fig:mon}. Here we choose to plot the\nstress response $\\Sigma(t)$ in a Lissajous-Bowditch figure as a\nparametric function of the time-varying strain $\\gamma(t)$, in the\nso-called `elastic' representation. Indeed, just as in the low\nfrequency regime the material behaved as a viscous fluid with the\nstress response falling onto the steady state master constitutive\ncurve in the viscous representation $\\Sigma(\\dot{\\gamma})$, for a high\nfrequency cycle we might instead expect a regime of elastic response\nin which only the accumulated strain is important, and not\n(separately) the strain rate, giving a master response curve of stress\nversus strain, $\\Sigma(\\gamma)$.\n\n\\begin{figure}[tbp]\n\\includegraphics[width=8.0cm]{figure3.eps}\n\\caption{As in Fig.~\\ref{fig:nonmon}, but for a value of the CCR\n parameter $\\beta=1.0$, for which the underlying homogeneous\n constitutive curve is monotonic.}\n\\label{fig:mon}\n\\end{figure}\n\nWe might further have intuitively expected this curve to be the same\nas that obtained in a fast shear startup from rest, with (in the\npositive strain part of the cycle) elastic response $\\Sigma\\approx G\\gamma$\nat low strain $\\gamma\\ll 1$, followed by stress overshoot at a typical\nstrain $\\gamma=+O(1)$, then decline towards a constant stress at\nlarger strains (with the symmetric curve in the negative-strain part\nof the cycle, such that $\\Sigma\\to-\\Sigma$ for $\\gamma\\to-\\gamma$). In\nother words, in LAOStrain at high frequency we might have expected the\nsystem to continuously explore its elastic shear startup curve\n$\\Sigma(\\gamma)$ between $\\gamma=-\\gamma_0$ and $\\gamma=+\\gamma_0$.\n\nHowever, this intuition is not met in a straightforward way. In the\nright panels of Fig.~\\ref{fig:nonmon} and~\\ref{fig:mon} we observe\ninstead an open cycle that is explored in a clockwise sense as time\nproceeds through an oscillation: the stress transits the upper part of\nthe loop (from bottom left to top right) in the forward part of the\ncycle as the strain increases from left to right, and the\nsymmetry-related lower part of the loop in the backward part, where\nthe strain decreases from right to left.\n\nThis can be understood as follows. For any LAOStrain run at high\nfrequency $\\omega \\gg 1$ but in the linear regime with strain\namplitude $\\gamma_0\\ll 1$, we do indeed find the stress response to\nfall onto a closed master curve $\\Sigma(\\gamma)$, which also\ncorresponds to that obtained in a fast stress startup from rest, with\nlinear elastic response $\\Sigma\\approx G\\gamma$. (Data not shown.) In\ncontrast, for amplitudes $\\gamma_0 > 1$ the system only explores this\nstartup-from-rest curve in the first half of the {\\em first} cycle\nafter the inception of flow. (This has the usual form, with elastic\nresponse for small strains, stress overshoot at a strain\n$\\gamma=O(1)$, then decline to a constant stress.) In the second half\nof the cycle, when the strain rate reverses and the strain decreases,\nthe stress response departs from the startup-from-rest curve. With\nhindsight this is in fact obvious: as this backward shear part of the\ncycle commences the initial condition is not that of a well-relaxed\nfluid, but one that has just suffered a large forward strain.\n\n\n\n\nThe same is true for the next forward half cycle: its initial\ncondition is that of a fluid that has just suffered a large negative\nstrain, corresponding to the lower left point in the right panel of\nFigs.~\\ref{fig:nonmon} or~\\ref{fig:mon}. Starting from that initial\ncondition the stress evolution nonetheless thereafter resembles that\nof a fast startup, with an initial fast rise followed by an overshoot\nthen decline to constant stress, before doing the same in reverse\n(with a symmetry-related `negative overshoot') during the next half\ncycle, giving the open curves as described. Associated with this\novershoot in each half cycle is a positive eigenvalue indicating\ninstability to the onset of shear banding. Importantly, we note that\nthis arises even in the case of a monotonic underlying constitutive\ncurve (Fig.~\\ref{fig:mon}, right), and therefore even in a fluid that would\nnot display steady state banding under a steadily applied shear flow.\nIt is the counterpart for LAOStrain of the `elastic' banding triggered\nby stress overshoot in a fast shear startup from rest, as explored\npreviously in\nRef.~\\cite{Moorcroft2013,Moorcroft2014,Adamsetal2011}.\n\n\n\\begin{figure}[tbp]\n\\includegraphics[width=9.0cm]{figure4.eps}\n\\caption{Colour map of the normalised degree of shear banding for the\n nRP model with a non-monotonic constitutive curve. Each point in this\n $\\dot{\\gamma}_0,\\omega$ plane corresponds to a particular LAOStrain run\n with strain rate amplitude $\\dot{\\gamma}_0$ and frequency $\\omega$. For\n computational efficiency, these calculations are performed by\n integrating the linearised equations in Sec.~\\ref{sec:lsa}. Reported\n is the maximum degree of banding that occurs at any point in the\n cycle, after many cycles. Model parameters: $\\beta=0.4$,\n $\\eta=10^{-5}$. Cell curvature $q=10^{-4}$. Crosses indicate the\n grid of values of $\\dot{\\gamma}_{0}$ and $\\omega$ in Pipkin diagram of\n Fig.~\\ref{fig:PipkinNonMon}.}\n\\label{fig:pinPointNonMon}\n\\end{figure}\n\n\\begin{figure}[tbp]\n\\includegraphics[width=9.0cm]{figure5.eps}\n\\caption{As in Fig.~\\ref{fig:pinPointNonMon}, but with a CCR parameter\n $\\beta=1.0$, for which the fluid has a monotonic underlying\n constitutive curve. Crosses indicate the grid of values of\n $\\dot{\\gamma}_{0}$ and $\\omega$ used in the Pipkin diagram of\n Fig.~\\ref{fig:PipkinMon}.}\n\\label{fig:pinPointMon}\n\\end{figure}\n\n\\begin{figure}[tp!]\n\\includegraphics[width=9cm]{figure6.eps}\n\\caption{Lissajous-Bowditch curves in LAOStrain for the nRP model with\n a non-monotonic constitutive curve. Results are shown in the elastic\n representation in (a), and the viscous representation in\n (b). Columns of fixed frequency $\\omega$ and rows of fixed\n strain-rate amplitude $\\dot{\\gamma}_0$ are labeled at the top and\n right-hand side. Colourscale shows the time-dependent degree of\n shear banding. Model parameters: $\\beta=0.4, \\eta=10^{-5}$. Cell\n curvature: $q=10^{-4}$. Number of numerical grid points $J=512$. A detailed portrait of the run outlined by\n the thicker box is shown in Fig.~\\ref{fig:portraitNonMon}.}\n\\label{fig:PipkinNonMon}\n\\end{figure}\n\n\\begin{figure}[tp!]\n\\includegraphics[width=9cm]{figure7.eps}\n\\caption{As in Fig.~\\ref{fig:PipkinNonMon} but for a value of the CCR\n parameter $\\beta=1.0$, for which the fluid's underlying constitutive\n curve is monotonic. Number of numerical grid points $J=512$. A detailed portrait of the run outlined by the\n thicker box is shown in Fig.~\\ref{fig:portraitMon}.}\n\\label{fig:PipkinMon}\n\\end{figure}\n\n\\begin{figure}[tbp]\n\\includegraphics[width=8cm]{figure8.eps}\n\\caption{LAOStrain in the nRP model with a non-monotonic constitutive\n curve. Strain rate amplitude $\\dot{\\gamma}_{0}=10.0$ and frequency\n $\\omega=3.16$. Model parameters $\\beta=0.4, \\eta=10^{-5}$. Cell\n curvature $q=10^{-4}$. Number of numerical grid points $J=1024$.\n {\\bf Left:} stress response in the elastic representation. Solid\n black and red-dashed line: calculation in which the flow is\n constrained to be homogeneous. Red-dashed region indicates a\n positive eigenvalue showing instability to the onset of shear\n banding. Green dot-dashed line: stress response in a full nonlinear\n simulation that allows banding. {\\bf Right:} Velocity profiles\n corresponding to stages in the cycle indicated by matching symbols\n in the left panel. Each profile is normalised by the speed of the\n moving plate.}\n\\label{fig:portraitNonMon}\n\\end{figure}\n\nIndeed, following the calculation first set out in\nRef.~\\cite{Moorcroft2013}, it is straightforward to show that\nthe condition for a linear instability to banding in this `elastic'\nhigh frequency regime $\\omega\\gg 1$ is the same as in fast shear\nstartup:\n\\begin{equation} \n\\label{eqn:criterionElastic}\n-\\textrm{tr}\\tens{M} \\frac{\\partial \\Sigma}{\\partial\\gamma} +\n\\dot{\\gamma}\\frac{\\partial^2\\Sigma}{\\partial\\gamma^2} < 0.\n\\end{equation}\nAs already discussed, this gives a window of instability setting in\njust before the stress overshoot (or negative overshoot) in each half\ncycle in the right panels of Fig.~\\ref{fig:nonmon} and~\\ref{fig:mon}\ndue to (in the positive $\\dot{\\gamma}$ part of the cycle in which the stress\ntransits from bottom left to top right) the negatively sloping and\ncurving $\\Sigma(\\gamma)$. An analogous statement applies in the other\npart of the cycle, with the appropriate sign reversals. Note that\nthese overshoots are sufficiently weak as to be difficult to resolve\nby eye on the scale of Figs.~\\ref{fig:nonmon} and~\\ref{fig:mon}.\n\nInterestingly,~(\\ref{eqn:criterionElastic}) also predicts a region of\n(weaker) instability immediately after the reversal of strain in each\nhalf cycle, as also seen in the right panels of Figs.~\\ref{fig:nonmon}\nand~\\ref{fig:mon}. Analytical considerations show that this\nadditional regime of instability is not driven by any negative slope or\ncurvature in $\\Sigma(\\gamma)$, but instead arises from a change in\nsign of $\\textrm{\\tens{M}}$. This instability has no counterpart that\nwe know of in shear startup. Its associated eigenvector is dominated\nby the normal stress component $W_{yy}$ rather than the strain rate or\nshear stress. Heterogeneity in this quantity could feasibly be\naccessed in birefringence experiments.\n\nThe results of Figs.~\\ref{fig:nonmon} and~\\ref{fig:mon} can be\nsummarised as follows. At low frequencies the system sweeps slowly up\nand down the underlying constitutive curve $\\Sigma(\\dot{\\gamma})$ as the\nshear rate varies through the cycle. If that curve is non-monotonic,\nthis homogeneous base state is unstable to shear banding in the region\nof negative constitutive slope, $d\\Sigma\/d\\dot{\\gamma}<0$. At high\nfrequencies the system instead executes a process reminiscent of\nelastic shear startup in each half cycle, but with an initial\ncondition corresponding to the state left by the shear of opposite\nsense in the previous half cycle. Associated with this is a stress\novershoot in each half cycle, giving instability to elastic shear\nbanding. Crucially, this elastic instability occurs whether or not the\nunderlying constitutive curve is non-monotonic or monotonic, and\ntherefore whether or not the fluid would shear band in steady shear.\n\nFrom a practical experimental viewpoint it is important to note that,\nwhereas in a single shear startup run these `elastic' strain bands\nwould form transiently then heal back to homogeneous flow (unless the\nsample has a non-monotonic underlying constitutive curve and so also\nbands in steady state), in LAOStrain the banding will recur in each\nhalf cycle and so be potentially easier to access experimentally.\nAny time-averaging measurement should of course only take data in the\nforward part of each cycle, to avoid averaging to zero over the\ncycle.\n\nHaving explored in Figs.~\\ref{fig:nonmon} and~\\ref{fig:mon} the\ntendency to form shear bands in two particular LAOStrain runs (one in\nthe limit of low frequency, $X_L$ in Fig.~\\ref{fig:sketch}, and one in\nthe limit of high frequency, $X_H$), we now explore the full\n$(\\dot{\\gamma}_0,\\omega)$ plane of Fig.~\\ref{fig:sketch} by showing in\nFigs.~\\ref{fig:pinPointNonMon} and~\\ref{fig:pinPointMon} colour maps of\nthe extent of shear banding across this plane. Recall that each\npinpoint in this plane corresponds to a single LAOS experiment with\nstrain rate amplitude $\\dot{\\gamma}_0$ and frequency $\\omega$. To build up\nthese colour maps, we sweep over a grid of 100x100\nvalues of $\\dot{\\gamma}_0,\\omega$ and execute a LAOStrain run at each point.\nSolving the model's full nonlinear dynamics on such a dense grid would\nbe unfeasibly time-consuming computationally. Therefore at each\n$\\dot{\\gamma}_0,\\omega$ we instead integrate the linearised equations set out\nin Sec.~\\ref{sec:lsa}. In each such run we record the degree of\nbanding $\\delta\\dot{\\gamma}$, maximised over the cycle after many cycles. It\nis this quantity, normalised by the maximum strain rate amplitude $\\dot{\\gamma}_{0}$, that is represented by the colourscale in\nFigs.~\\ref{fig:pinPointNonMon} and~\\ref{fig:pinPointMon}.\n\nFig.~\\ref{fig:pinPointNonMon} pertains to the nRP model with model\nparameters for which the underlying constitutive curve is\nnon-monotonic. As expected, significant banding (bright\/yellow region)\nis observed even in the limit of low frequency $\\omega\\to 0$ for\nstrain rate amplitudes $\\dot{\\gamma}_0$ exceeding the onset of negative slope\nin the underlying constitutive curve. This region of banding is the\ndirect (and relatively trivial) analogue of banding in a slow strain\nrate sweep along the steady state flow curve.\nFig.~\\ref{fig:pinPointMon} shows results for the nRP model with\nparameters such that the underlying constitutive curve is monotonic.\nHere steady state banding is absent in the limit $\\omega\\to 0$. In\nboth Fig.~\\ref{fig:pinPointNonMon} and~\\ref{fig:pinPointMon}, however,\nsignificant banding is observed at high frequencies for a strain\namplitude $\\gamma_0>1 $: this is the elastic banding associated with\nthe stress overshoot in each half cycle, described in detail above for\nthe $(\\dot{\\gamma}_0,\\omega)$ values denoted by $X_H$ in\nFig.~\\ref{fig:sketch}.\n\nIt is important to emphasise, therefore, that even a fluid with a\npurely monotonic constitutive curve, which does not shear band in\nsteady flow, is still nonetheless capable of showing strong shear\nbanding in a time-dependent protocol of high enough frequency\n(Fig.~\\ref{fig:pinPointMon}). Also important to note is that for a\nfluid with a non-monotonic constitutive curve the region of steady\nstate `viscous' banding at low frequencies crosses over smoothly to\nthat of `elastic' banding as the frequency increases\n(Fig.~\\ref{fig:pinPointNonMon}). \n\nCorresponding to the degree of banding in the shear rate\n$\\delta\\dot{\\gamma}$, as plotted in Figs.~\\ref{fig:pinPointNonMon}\nand~\\ref{fig:pinPointMon}, is an equivalent degree of banding $G\\delta\nW_{xy}=-\\eta\\delta\\dot{\\gamma}$ (to within small corrections of order the\ncell curvature, $q$) in the shear component of the polymeric\nconformation tensor. This follows trivially by imposing force balance\nat zero Reynolds number. Counterpart maps for the degree of banding in\nthe component $\\delta W_{yy}$ of the polymeric conformation tensor can\nalso be built up. These reveal closely similar regions of banding to\nthose shown in Figs.~\\ref{fig:pinPointNonMon}\nand~\\ref{fig:pinPointMon}. (Data not shown.) Experimentally,\nheterogeneities in the polymeric conformation tensor can be accessed\nby flow birefringence.\n\n\n\n\\begin{figure}[tbp]\n\\includegraphics[width=8cm]{figure9.eps}\n\\caption{As in Fig.~\\ref{fig:portraitNonMon} but for the nRP model with\n a CCR parameter $\\beta=1.0$ for which the underlying homogeneous\n constitutive curve is monotonic, and for a LAOStrain with strain\n rate amplitude $\\gamma_{0}=56.2$ and frequency\n $\\omega=10.0$. Number of numerical grid points $J=512$.}\n\\label{fig:portraitMon}\n\\end{figure}\n\n\nAs noted above, to build up such comprehensive roadmaps as in\nFigs.~\\ref{fig:pinPointNonMon} and~\\ref{fig:pinPointMon} in a\ncomputationally efficient way, we omitted all nonlinear effects and\nintegrated instead the linearised equations of Sec.~\\ref{sec:lsa}.\nThese are only strictly valid in any regime where the amplitude of the\nheterogeneity remains small. In omitting nonlinear effects, they tend\nto overestimate the degree of banding in any regime of sustained\npositive eigenvalue, in predicting the heterogeneity to grow\nexponentially without bound, whereas in practice it would be cutoff by\nnonlinear effects. We now remedy this shortcoming by exploring the\nmodel's full nonlinear spatiotemporal dynamics. To do so within\nfeasible computational run times, we focus on a restricted grid of\nvalues in the $\\dot{\\gamma}_0,\\omega$ plane, marked by crosses in\nFigs.~\\ref{fig:pinPointNonMon} and~\\ref{fig:pinPointMon}.\n\nThe results are shown in Fig.~\\ref{fig:PipkinNonMon} for the nRP model\nwith model parameters for which the underlying constitutive curve is\nnon-monotonic. At low frequencies the results tend towards the\nlimiting fluid-like behaviour discussed above, in which the the stress\nslowly tracks up and down the steady state flow curve $\\Sigma(\\dot{\\gamma})$.\n(Progression towards this limit can be seen by following the top row\nof panels in Fig.~\\ref{fig:PipkinNonMon}b) to the left.) Viscous\nbanding is seen for sufficiently high strain rate amplitudes $\\dot{\\gamma}_0$\ndue to the negatively sloping underlying homogeneous constitutive\ncurve. At high frequencies the response tends instead towards the\nlimiting elastic-like behaviour discussed above. For large enough\nstrain amplitudes the stress then shows an open cycle $\\Sigma(\\gamma)$\nas a function of strain, with an overshoot in each half cycle that\ntriggers the formation of `elastic' banding. (Progression towards\nthis limit is seen by following the top row of panels in\nFig.~\\ref{fig:PipkinNonMon}a to the right.)\n\nOvershoots in the elastic Lissajous-Bowditch curve of stress as a\nfunction of strain have been identified in earlier\nwork~\\cite{Ewoldt2009loops} as leading to self-intersection of the\ncorresponding viscous Lissajous-Bowditch curve of stress as a function\nof strain-rate. Such an effect is clearly seen here in the Rolie-poly\nmodel: see for example the runs highlighted by the thicker boxes in\nFig.~\\ref{fig:PipkinNonMon} and in Fig.~\\ref{fig:PipkinMon}.\n\n\\begin{figure}[tbp]\n\\includegraphics[width=8.5cm]{figure10.eps}\n\\caption{Effect of CCR parameter $\\beta$ and entanglement number $Z$\n (and so of chain stretch relaxation time $\\tau_R=\\tau_d\/3Z$) on shear\n banding in LAOStrain. (Recall that the non-stretching version of\n the model has $\\tau_R\\to 0$ and so $Z\\to\\infty$.) Empty circles: no\n observable banding. Hatched circles: observable banding, typically\n $\\Delta_{\\dot{\\gamma}}\/\\dot{\\gamma}_0 \\approx 10\\%-100\\%$. Filled circles: significant\n banding $\\Delta_{\\dot{\\gamma}}\/\\dot{\\gamma}_0 \\ge 100\\%$. For hatched and filled\n symbols we used the criterion that banding of the typical magnitude\n stated is apparent in a region spanning at least half a decade by\n half a decade in the plane of $\\dot{\\gamma}_0,\\omega$, by examining maps as\n in Fig.~\\ref{fig:stretchPinPoint} in by eye. The square shows the\n parameter values explored in detail in\n Fig.~\\ref{fig:stretchPinPoint}.}\n\\label{fig:stretchMaster}\n\\end{figure}\n\nFor intermediate frequencies the stress is a complicated function of\nboth strain rate and also, separately, the strain. The three\ndimensional curve $(\\Sigma,\\dot{\\gamma},\\gamma)$ is then best shown as two\nseparate projections: first in the elastic representation of the\n$\\Sigma,\\gamma$ plane (Fig.~\\ref{fig:PipkinNonMon}a), and second, in\nthe viscous representation of the $(\\Sigma,\\dot{\\gamma})$ plane\n(Fig.~\\ref{fig:PipkinNonMon}b). Collections of these\nLissajous-Bowditch curves on a grid of $(\\dot{\\gamma}_0,\\omega)$ values as in\nFig.~\\ref{fig:PipkinNonMon} are called Pipkin diagrams.\n\n\\begin{figure}[tbp]\n \\includegraphics[width=10.0cm]{figure11.eps}\n\\caption{Colour map of the normalised degree of shear banding for the\n sRP model with a monotonic constitutive curve. Each point in this\n $\\dot{\\gamma}_0,\\omega$ plane corresponds to a particular LAOStrain run\n with strain rate amplitude $\\dot{\\gamma}_0$ and frequency $\\omega$. For\n computational efficiency, these calculations are performed by\n integrating the linearised equations in Sec.~\\ref{sec:lsa}. Reported\n is the maximum degree of banding at any point in the cycle, after\n many cycles. Model parameters: $\\beta=0.7$, $Z=75$ (and so\n $\\tau_R=0.0044$), $\\eta=10^{-5}$. Cell curvature $q=2\\times\n 10^{-3}$. Note the different colour scale from Figs.~\\ref{fig:nonmon} and~\\ref{fig:mon}. The model's full nonlinear dynamics for the ($\\dot{\\gamma}_{0}, \\omega$) value marked by the cross are explored in Fig.~\\ref{fig:stretchPortrait}.}\n\\label{fig:stretchPinPoint}\n\\end{figure}\n\n\\begin{figure}[tbp]\n\\includegraphics[width=9cm]{figure12.eps}\n\\caption{sRP model with a monotonic constitutive curve in LAOStrain of\n strain rate amplitude $\\dot{\\gamma}_{0}=20.0$ and frequency $\\omega=8.0$.\n Model parameters $\\beta=0.7, Z=75, \\eta=10^{-5}$. Cell curvature\n $q=2\\times 10^{-3}$. Number of numerical grid points $J=512$. {\\bf\n Left:} stress response in the elastic representation. Solid black\n and red-dashed line: calculation in which the flow is constrained to\n be homogeneous. Red-dashed region indicates a positive eigenvalue\n showing instability to the onset of shear banding. Green dot-dashed\n line: stress response in a full nonlinear simulation that allows\n banding (almost indistinguishable from homogeneous signal in this\n case.) {\\bf Right:} Velocity profiles corresponding to stages in the\n cycle indicated by matching symbols in left panel.}\n\\label{fig:stretchPortrait}\n\\end{figure}\n\nFor the particular LAOStrain run highlighted by a thicker box in\nFig.~\\ref{fig:PipkinNonMon}, a detailed portrait of the system's\ndynamics is shown in Fig.~\\ref{fig:portraitNonMon}. Here the stress is\nshown in the elastic representation, as a function of strain $\\gamma$\n(left hand panel). Two curves are shown here. The first shows the\nstress signal in a calculation in which the flow is artificially\nconstrained to remain homogeneous. A linear instability analysis for\nthe dynamics of small heterogeneous perturbations about this\ntime-evolving homogeneous base state then reveals instability towards\nthe formation of shear bands (a positive eigenvalue) in the portion of\nthat curve shown as a red dashed line. A full nonlinear simulation\nthen reveals the formation of shear bands, and leads to a stress\nsignal (green dot-dashed line) that deviates from that of the\nhomogeneously constrained calculation, in particular in having a much\nmore precipitous stress drop due to the formation of bands.\n\nThe associated velocity profiles at four points round the part of the\ncycle with increasing strain are shown in the right hand panel. Before\nthe stress overshoot, no banding is apparent (black circles). The\novershoot then triggers strong shear banding (red squares), with most\nof the shear concentrated in a thin band at the left hand edge of the\ncell. Interestingly, the shear in the right hand part of the cell is\nin the opposite sense to the overall applied shear. This is consistent\nwith the fact that the stress is a decreasing function of strain in\nthis regime: the material is being unloaded, and an elastic-like\nmaterial being unloaded will shear backwards. As the overall applied\nstrain increases towards the end of the window of instability, the\nflow heterogeneity gradually decays away. This process repeats in each\nhalf cycle (with the obvious sign reversals in the part of the cycle\nin which the strain is decreasing).\n\nThe corresponding Pipkin diagram for a fluid with a monotonic\nconstitutive curve (Fig.~\\ref{fig:PipkinMon}) likewise confirms its\ncounterpart linear diagram in Fig.~\\ref{fig:pinPointMon}. Here\n`viscous' banding is absent at low frequencies, because the fluid is\nnot capable of steady state banding. Crucially, however, a strong\neffect of elastic banding is still seen at high frequencies. A\ndetailed portrait of the system's dynamics in this elastic regime, for\nthe strain rate amplitude and frequency marked by the thicker box in\nFig.~\\ref{fig:PipkinMon}, is shown in Fig.~\\ref{fig:portraitMon}. As\ncan be seen, it shows similar features to those just described in\nFig.~\\ref{fig:portraitNonMon}. We emphasise, then, that even polymeric\nfluids that do not band under conditions of steady shear are still\ncapable of showing strong banding in a time-dependent protocol at high\nenough frequency. This important prediction is consistent with the\nearly insight of Adams and Olmsted in Ref.~\\cite{Adams2009}.\n\nSo far we have presented results for the nRP model, which assumes an\ninfinitely fast rate of chain stretch relaxation compared to the rate\nof reptation, such that the ratio $\\tau_R\/\\tau_d\\to 0$. This corresponds\nto assuming that the polymer chains are very highly entangled, with a\nnumber of entanglements per chain $Z=\\tau_d\/3\\tau_R\\to\\infty$. We now\nconsider the robustness of these results to reduced entanglement\nnumbers, and accordingly increased chain stretch relaxation time\n$\\tau_R$ (in units of $\\tau_d$). \n\nThe results are summarised in Fig.~\\ref{fig:stretchMaster}, which\nshows the regions of the plane of the CCR parameter $\\beta$ and\nentanglement number $Z$ in which significant banding (filled circles),\nobservable banding (hatched circles), and no banding (open circles)\noccur. (Recall that results presented for the nRP model above pertain\nto the values $\\beta=0.4,1.0$ in the limit $Z\\to\\infty$.) As can be\nseen, by reducing the number of entanglements per chain the effect of\nshear banding is reduced and eventually eliminated. However it is\nimportant to note that, depending on the value of $\\beta$, significant\nbanding is still observed for experimentally commonly used\nentanglement numbers, typically in the range $20-100$. Furthermore, significant banding is seen in a large region of the ($\\beta, Z$) plane for which the material's underlying constitutive curve is monotonic. As discussed\nabove, there is no current consensus as to the value of the CCR\nparameter $\\beta$ in the range $0<\\beta<1$. Using the routemap\nprovided in Fig.~\\ref{fig:stretchMaster}, a study of shear banding in\nLAOStrain experiments could provide one way to obtain a more accurate\nestimate of the value of this parameter.\n\nFor the pairing of $\\beta$ and $Z$ values marked by the square in\nFig.~\\ref{fig:stretchMaster}, we show in\nFig.~\\ref{fig:stretchPinPoint} a colour map of the degree of banding\nexpected in LAOStrain in the space of strain rate amplitude $\\dot{\\gamma}_0$\nand frequency $\\omega$. This figure, which is for the sRP model,\n is the counterpart of the earlier Figs.~\\ref{fig:pinPointNonMon}\n and~\\ref{fig:pinPointMon} discussed above for the nRP model, with\nthe degree of banding calculated for computational\nefficiency within the assumption of linearised dynamics. Recall that\neach pinpoint in this plane corresponds to a single LAOStrain run with\napplied strain rate $\\dot{\\gamma}(t)=\\dot{\\gamma}_0\\cos(\\omega t)$.\n\nConsistent with the underlying constitutive curve being monotonic for\nthese parameters, `viscous' banding is absence in the limit of low\nfrequencies $\\omega\\to 0$. However significant banding is still\nobserved for runs with strain rate amplitudes $O(10-100)$ and\nfrequencies $O(1-10)$. This is the counterpart of the `elastic'\nbanding reported above in the nRP model, though the effect of finite\nchain stretch in the sRP model is to moderate the degree of banding. A\ndetailed portrait of the model's nonlinear banding dynamics at a\nstrain rate amplitude $\\dot{\\gamma}_0$ and frequency $\\omega$ marked by the\ncross in Fig.~\\ref{fig:stretchPinPoint} is shown in\nFig.~\\ref{fig:stretchPortrait}. Significant shear banding associated with the stress overshoot is apparent in each half cycle.\n\n\\section{Large amplitude oscillatory stress}\n\\label{sec:LAOStress}\n\nWe now consider the time-dependent stress-imposed oscillatory protocol\nof LAOStress. Here the sample is subject for times $t>0$ to a stress\nof the form\n\\begin{equation}\n\\Sigma(t)=\\Sigma_0\\sin(\\omega t),\n\\end{equation}\ncharacterised by the frequency $\\omega$ and stress amplitude\n$\\Sigma_0$. As for the case of LAOStrain above, all the results\npresented below are in the long-time regime, once many ($N=20$) cycles\nhave been executed and the response of the system has settled to be\ntime-translationally invariant from cycle to cycle, $t\\to\nt+2\\pi\/\\omega$.\n\nIn Sec.~\\ref{sec:recapCreep} we reviewed existing work demonstrating\nthe tendency to form shear bands in a {\\rm step} stress experiment.\nHere an initially well relaxed sample is suddenly subject at time\n$t=0$ to the switch-on of a shear stress of amplitude $\\Sigma_0$,\nwhich is held constant for all subsequent times. The criterion for an\nunderlying base state of initially homogeneous creep response to\nbecome linearly unstable to the formation of shear bands is then that\nthe time-differentiated creep response curve $\\dot{\\gamma}(t)$\nobeys~\\cite{Moorcroft2013}:\n\\begin{equation}\n\\label{eqn:critCreep}\n\\frac{\\partial^2\\dot{\\gamma}}{\\partial t^2}\/\\frac{\\partial\\dot{\\gamma}}{\\partial t}>0.\n\\end{equation}\nTherefore, shear banding is expected in any regime where the\ntime-differentiated creep curve simultaneously slopes up and curves\nupwards; or instead simultaneously slopes down and curves downwards.\n\n\\begin{figure}[tbp]\n\\includegraphics[width=8.0cm]{figure13.eps}\n\\caption{LAOStress in the nRP model with a non-monotonic constitutive\n curve. Model parameters: $\\beta=0.1$, $\\eta=10^{-4}$. Frequency\n $\\omega=0.01$ and stress amplitude $\\Sigma_0=0.7$. {\\bf Left:}\n stress versus strain rate (shown on a log scale) in the positive\n stress part of the cycle. Colour scale shows eigenvalue, with\n negative values also shown as black. Green dashed line: underlying\n constitutive curve. {\\bf Right:} corresponding stress versus time\n plot.}\n\\label{fig:eigenNonMon}\n\\end{figure}\n\n\n\\begin{figure}[tbp]\n\\includegraphics[width=8.0cm]{figure14.eps}\n\\caption{As in Fig.~\\ref{fig:eigenNonMon} but at a higher imposed\n frequency $\\omega=1.0$ and for a value of the CCR $\\beta=0.9$, for\n which the nRP model has a monotonic underlying constitutive curve.\n {\\bf Right:} corresponding stress versus time plot.}\n\\label{fig:eigenMon}\n\\end{figure}\n\n\n\nHaving been derived within the assumption of an imposed stress that is\nconstant in time, criterion (\\ref{eqn:critCreep}) would not {\\it a\n priori} be expected to hold for the case of LAOStress. Nonetheless\nit might reasonably be expected to apply, to good approximation, in any\nregime of a LAOStress experiment where a separation of timescales\narises such that the shear rate $\\dot{\\gamma}(t)$ evolves on a much shorter\ntimescale than the stress. In this case, from the viewpoint of the\nstrain rate signal, the stress appears constant in comparison and the\nconstant-stress criterion (\\ref{eqn:critCreep}) is expected to hold.\nIndeed, in what follows we shall show that many of our results for\nLAOStress can be understood on the basis of this simple piece of\nintuition.\n\nWe start in Fig.~\\ref{fig:eigenNonMon} by considering the nRP model in\na parameter regime for which the underlying constitutive curve is\nnon-monotonic (see the dotted line in the left panel), such that shear\nbanding would be expected under conditions of a steadily applied shear\nrate. With the backdrop of this constitutive curve we consider a\nLAOStress run at low frequency $\\omega\\to 0$. For definiteness we will\nfocus on the part of the cycle where the stress is positive, but\nanalogous remarks will apply to the other half of the cycle, with\nappropriate changes of sign.\n\nConsider first the regime in which the stress is slowly increased from\n$0$ towards its maximum value $\\Sigma_0$. In this part of the cycle,\nat the low frequencies of interest here, we expect the system to\ninitially follow the high viscosity branch of the constitutive curve.\nIn any experiment for which the final stress $\\Sigma_0$ exceeds the\nlocal maximum in the constitutive curve, the system must at some stage\nduring this increasing-stress part of the cycle transit from the high\nto low viscosity branch of the constitutive curve. This transition is\nindeed seen in Fig.~\\ref{fig:eigenNonMon}: it occurs via ``top\njumping'' from the stress maximum across to the low viscosity branch.\nConversely, on the downward part of the sweep as the stress decreases\nfrom its maximum value $\\Sigma_0$, the system initially follows the\nlow viscosity branch until it eventually jumps back to the high\nviscosity branch. (We return in our concluding remarks to discuss the\npossible effect of thermal nucleation events, which are not included\nin these simulations, on these process of jumping between the two\nbranches of the constitutive curve.)\n\n\n\nThe corresponding signal of strain rate versus time during this slow\nup-then-down stress oscillation is shown in the right panel of\nFig.~\\ref{fig:eigenNonMon}. As can be seen, the regimes where the\nshear rate transits between the two different branches of the\nconstitutive curve occur over relatively short time intervals. (The\nduration of this process is informed by the short timescale $\\eta\/G$,\nwhereas the stress evolves on the much longer timescale $2\\pi\/\\omega$.)\nThis separation of timescales renders the stress signal approximately\nconstant in comparison to the fast evolution of the strain rate.\nCriterion (\\ref{eqn:critCreep}) might therefore be expected to apply\nin this regime of transition, at least to good approximation.\n\n\n\n\nFurthermore, during the transition from the high to low viscosity\nbranch we see a regime in which the shear rate signal simultaneously\nslopes up and curves up as a function of time: criterion\n(\\ref{eqn:critCreep}) not only applies but is also met, and we\ntherefore expect an instability to banding. Plotting, by means of a\ncolourscale, the eigenvalue as defined in Sec.~\\ref{sec:lsa}, we find\nthat it is indeed positive. Likewise, during the rapid transition back\nfrom the low to high viscosity branch, we find a regime in which the\nshear rate signal simultaneously slopes down and curves down. As seen\nfrom the colourscale, the eigenvalue is also positive in this regime\n(although more weakly than during the upward transition). In what\nfollows, we will confirm the expectation of shear band formation\nduring these times of positive eigenvalue by simulating the model's\nfull nonlinear spatiotemporal dynamics.\n\n\\begin{figure}[tbp]\n\\includegraphics[width=9.5cm]{figure15.eps}\n\\caption{Colour map of the normalised degree of shear banding for the\n nRP model with a non-monotonic constitutive curve. Each point in\n this $\\Sigma_0,\\omega$ plane corresponds to a particular LAOStress\n run with stress amplitude $\\Sigma_0$ and frequency $\\omega$. For\n computational efficiency, these calculations are performed by\n integrating the linearised equations in Sec.~\\ref{sec:lsa}. Reported\n is the maximum degree of banding that occurs at any point in the\n cycle, after many cycles. Model parameters: $\\beta=0.4$,\n $\\eta=10^{-4}$. Cell curvature $q=2\\times 10^{-3}$. Crosses\n indicate the grid of values of $\\Sigma_{0}$ and $\\omega$ in\n Fig.~\\ref{fig:PipkinNonMon2}.}\n\\label{fig:pinPointNonMon2}\n\\end{figure}\n\n\n\\begin{figure}[tbp]\n\\includegraphics[width=9.5cm]{figure16.eps}\n\\caption{ As in Fig.~\\ref{fig:pinPointNonMon}, but with a CCR\n parameter $\\beta=0.9$, for which the fluid has a monotonic\n underlying constitutive curve. Crosses indicate the grid of values\n of $\\Sigma_{0}$ and $\\omega$ used in the Pipkin diagram of\n Fig.~\\ref{fig:PipkinMon2}.}\n\\label{fig:pinPointMon2}\n\\end{figure}\n\nThese processes of rapid transition between different branches of the\nconstitutive curve are of course not expected in a LAOStress\nexperiment at low frequency for a fluid with a monotonic constitutive\ncurve. In this case, for a LAOStress run in the limit $\\omega\\to 0$\nthe system instead sweeps quasi-statically along the monotonic\nconstitutive curve, with no associated banding. As in the case of\nLAOStrain, however, it is crucial to realise that the absence of\nbanding in an experiment at zero frequency does not preclude the\npossibility of banding in a time-dependent protocol at finite\nfrequency, even in a fluid with a monotonic constitutive curve.\n\n\\begin{figure}[tbp]\n\\includegraphics[width=8.5cm]{figure17.eps}\n\\caption{Lissajous-Bowditch curves in LAOStress for the nRP model with\n a non-monotonic constitutive curve. Results are shown as shear-rate\n versus time in (a), and in the viscous representation of stress\n versus strain rate in (b). Columns of fixed frequency $\\omega$ and\n rows of fixed strain-rate amplitude $\\dot{\\gamma}_0$ are labeled at the top\n and right-hand side. Colourscale shows the time-dependent degree of\n shear banding. Model parameters: $\\beta=0.4, \\eta=10^{-4}, l=0.02$.\n Cell curvature: $q=2\\times 10^{-3}$. Number of numerical grid points $J=512$. A detailed portrait of the run\n outlined by the thicker box is shown in\n Fig.~\\ref{fig:portraitNonMon2}.}\n\\label{fig:PipkinNonMon2}\n\\end{figure}\n\n\n\\begin{figure}[tp!]\n\\includegraphics[width=8.5cm]{figure18.eps}\n\\caption{As in Fig.~\\ref{fig:PipkinNonMon2} but for a value of the CCR\n parameter $\\beta=0.9$, for which the fluid's underlying constitutive\n curve is monotonic. Number of numerical grid points $J=512$. A detailed portrait of the run outlined by the thicker box is shown in Fig.~\\ref{fig:portraitMon2}.}\n\\label{fig:PipkinMon2}\n\\end{figure}\n\nWith that in mind, we show in Fig.~\\ref{fig:eigenMon} the counterpart\nof Fig.~\\ref{fig:eigenNonMon}, but now for the nRP model with a\nmonotonic constitutive curve subject to a LAOStress run at a finite\nfrequency $\\omega=1$, of order the fluid's reciprocal stress\nrelaxation timescale. The key to understanding the emergent dynamics\nin this case is the existence in the underlying zero-frequency\nconstitutive curve (shown by a dotted line in the left panel) of a\nregion in which the stress is a relatively flat (though still\nincreasing) function of the strain rate. As the system transits this\nregion during the increasing stress part of a finite-frequency stress\ncycle, we again observe a regime of quite sudden progression from low\nto high strain rate. This is seen in the left to right transition in\nthe stress versus strain rate representation in the left panel of\nFig.~\\ref{fig:eigenMon}, and (correspondingly) in the rapid increase\nof strain rate versus time in the right panel.\n\n\n\nDuring this regime of rapid transit we again have conditions in which\nthe strain rate evolves rapidly compared to the stress, such that the\nconstant-stress criterion (\\ref{eqn:critCreep}) should apply to good\napproximation. Furthermore, during the first part of the transition,\nthe strain rate signal simultaneously slopes and curves upward as a\nfunction of time. The eigenvalue is therefore positive, indicating\nlinear instability of an initially homogeneous base state to the\nformation of shear bands. We will again confirm this prediction by\nsimulating the model's full nonlinear spatiotemporal dynamics below.\n\nIn the context of Figs.~\\ref{fig:eigenNonMon} and~\\ref{fig:eigenMon}\nwe have discussed the dynamics of the nRP model with a non-monotonic\nand monotonic constitutive curve respectively, focusing in each case\non one particular value of the imposed frequency $\\omega$ and stress\namplitude $\\Sigma_0$. We now consider the full plane of\n$(\\Sigma_0,\\omega)$ by showing in Figs.~\\ref{fig:pinPointNonMon2}\nand~\\ref{fig:pinPointMon2} colour maps of the extent of banding across\nit. Recall that each point in this plane corresponds to a single LAOS\nexperiment with stress amplitude $\\Sigma_0$ and frequency $\\omega$.\nTo build up these maps we sweep over a grid of 20x20 values of\n$\\Sigma_0,\\omega$ and execute at each point a LAOStress run,\nintegrating the model's linearised equations set out in\nSec.~\\ref{sec:lsa}. We then represent the degree of banding\n$\\delta\\dot{\\gamma}$, maximised over the cycle after many cycles, by the\ncolourscale.\n\n\\begin{figure}[tbp]\n\\includegraphics[width=9cm]{figure19.eps}\n\\caption{ LAOStress in the nRP model with a non-monotonic constitutive\n curve. Stress amplitude $\\Sigma_{0}=0.8$ and frequency $\\omega=1.0$.\n Model parameters $\\beta=0.4, \\eta=10^{-4}, l=0.02$. Cell curvature\n $q=2\\times 10^{-3}$. Number of numerical grid points $J=512$. {\\bf\n Left:} strain rate response as a function of time, focusing on the\n region in which the system transits from the high to low viscosity\n branch of the constitutive curve. Solid black and red-dashed line:\n calculation in which the flow is constrained to be homogeneous.\n Red-dashed region indicates a positive eigenvalue showing\n instability to the onset of shear banding. Green dot-dashed line:\n stress response in a full nonlinear simulation that allows banding.\n {\\bf Right:} Velocity profiles corresponding to stages in the cycle\n indicated by matching symbols in the left panel.}\n\\label{fig:portraitNonMon2}\n\\end{figure}\n\n\n\nFig.~\\ref{fig:pinPointNonMon2} shows results with model parameters for\nwhich the underlying constitutive curve is non-monotonic. As expected,\nfor stress amplitudes $\\Sigma_0$ exceeding the local maximum in the\nunderlying constitutive curve, significant banding is observed even in\nthe limit of low frequency $\\omega\\to 0$. This is associated with the\nprocesses of jumping between the two different branches of the\nconstitutive curve discussed above. \n\nFig.~\\ref{fig:pinPointMon2} shows results for the nRP model with a\nmonotonic underlying constitutive curve. Here steady state banding is\nabsent in the limit $\\omega\\to 0$, as expected. However, significant\nbanding is still nonetheless observed at frequencies of order the\nreciprocal reptation time, for imposed stress amplitudes exceeding the\nregion of weak slope in the constitutive curve, consistent with our\ndiscussion of Fig.~\\ref{fig:eigenMon} above.\n\nTo obtain the comprehensive roadmaps of\nFigs.~\\ref{fig:pinPointNonMon2} and~\\ref{fig:pinPointMon2} in a\ncomputationally efficient way, we discarded any nonlinear effects and\nintegrated the linearised model equations set out in\nSec.~\\ref{sec:lsa}. However, these linearised equations tend to\noverestimate the degree of banding in any regime of sustained positive\neigenvalue. Therefore in Figs.~\\ref{fig:PipkinNonMon2}\nand~\\ref{fig:PipkinMon2} we now simulate the model's full nonlinear\nspatiotemporal dynamics, restricting ourselves for computational\nefficiency to grids of 4x4 values of $\\Sigma_0,\\omega$ as marked by\ncrosses in Figs.~\\ref{fig:pinPointNonMon2} and~\\ref{fig:pinPointMon2}.\n\n\nFig.~\\ref{fig:PipkinNonMon2} pertains to the nRP model with model\nparameters for which the underlying constitutive curve is\nnon-monotonic. At low frequencies the results tend towards the\nlimiting behaviour discussed above, in which the stress slowly tracks\nup and down the steady state flow curve $\\Sigma(\\dot{\\gamma})$ in between\nregimes of sudden transition between the two branches of the curve,\nduring which shear bands form. This is most pronounced in the case of\nthe jump between the high and low viscosity branches during the upward\nsweep. Banding on the downward sweep is only apparent in a relatively\nmore limited region of $\\Sigma_0,\\dot{\\gamma}$ space, consistent with the\ntransition of $\\dot{\\gamma}_0$ being more modest in this part of the cycle\nduring which the stress decreases.\n\n\\begin{figure}[tbp]\n\\includegraphics[width=9cm]{figure20.eps}\n\\caption{As in Fig.~\\ref{fig:portraitNonMon2} but for the nRP model\n with a CCR parameter $\\beta=0.9$ for which the underlying\n homogeneous constitutive curve is monotonic. Number of numerical\n grid points $J=512$.}\n\\label{fig:portraitMon2}\n\\end{figure}\n\nFor the particular run highlighted by the thicker box in\nFig.~\\ref{fig:PipkinNonMon2}, a detailed portrait of the system's\ndynamics is shown in Fig.~\\ref{fig:portraitNonMon2}. The left panel\nshows the strain rate signal as a function of time, zoomed on the\nregion in which the strain rate makes its transit from the high to low\nviscosity branch of the constitutive curve. The black and red-dashed\nline show the results of a calculation in which the flow is\nartificially constrained to remain homogeneous. The red-dashed region\nindicates the regime in which the criterion (\\ref{eqn:critCreep}) for\nlinear instability to the formation of shear bands is met, which also\ncorresponds to the regime in which the strain rate signal\nsimultaneously slopes up and curves upwards.\n\n\n\nIn a simulation that properly takes account of flow heterogeneity,\nshear bands indeed develop during this regime where the criterion is\nmet, then decay again once the strain rate signal curves down and\nstability is restored. This sequence can be seen in the velocity\nprofiles in the right hand panel. The stress signal associated with\nthis run that allows bands to form is shown by the green dot-dashed\nline in the left panel, and is only barely distinguishable from that\nof the run in which the flow is constrained to remain homogeneous.\n\nFig.~\\ref{fig:PipkinMon2} pertains to the nRP model with model\nparameters for which the underlying constitutive curve is monotonic,\nwith the grid of $(\\Sigma_0,\\omega)$ values that it explores marked by\ncrosses in Fig.~\\ref{fig:pinPointMon2}. True top-jumping events are\nabsent here, and no shear banding arises in the limit of zero\nfrequency. As discussed above, however, a similar rapid transition\nfrom low to high shear rate is seen in runs at a frequency $O(1)$, as\nthe stress transits the region of weak slope in the constitutive curve\nduring the increasing-stress part of the cycle. Associated with this\ntransit is a pronounced tendency to form shear bands.\n\n\n\\begin{figure}[tbp]\n\\includegraphics[width=8.5cm]{figure21.eps}\n\\caption{Effect of CCR parameter $\\beta$ and entanglement number $Z$\n (and so of chain stretch relaxation time $\\tau_R=\\tau_d\/3Z$) on shear\n banding in LAOStress. (Recall that the non-stretching version of\n the model has $\\tau_R\\to 0$ and so $Z\\to\\infty$.) Empty circles: no\n observable banding. Hatched circles: observable banding,\n $\\Delta_{\\dot{\\gamma}}\/(1+|\\dot{\\gamma}(t)|) = 10\\%-31.6\\%$. Dot-filled circles:\n significant banding, $\\Delta_{\\dot{\\gamma}}\/(1+|\\dot{\\gamma}(t)|) = 31.6\\% - 100\\%$.\n Filled circles: strong banding, $\\Delta_{\\dot{\\gamma}}\/(1+|\\dot{\\gamma}(t)|) > 100\\%$.\n For the hatched, dot-filled and filled symbols we used the criterion\n that banding of the typical magnitude stated is apparent for any of\n $\\omega=0.1,0.316$ or $1.0$, given a stress amplitude $\\Sigma_0$\n exceeding the region of weak slope in the constitutive curve. The\n square shows the parameter values explored in detail in\n Fig.~\\ref{fig:stretchPortrait1}. The solvent viscosity $\\eta$ is $3.16\\times10^{-5}$.}\n\\label{fig:stretchMaster2}\n\\end{figure}\n\n\nThis can be seen for the run highlighted by the thicker box in\nFig.~\\ref{fig:PipkinMon2}, of which a detailed portrait is shown in\nFig.~\\ref{fig:portraitMon2}. This shows very similar features to its\ncounterpart for a non-monotonic underlying constitutive curve. In\nparticular, the regime of simultaneous upward slope and upward\ncurvature in the strain rate signal as the stress transits the region\nof weak positive constitutive slope triggers pronounced shear banding.\n\nThese results illustrate again the crucial point: that shear bands can\nform in a protocol with sufficiently strong time-dependence, even in a\nfluid for which the underlying constitutive curve is monotonic such\nthat banding is forbidden in steady state flows.\n\nSo far, we have restricted our discussion of LAOStress to the nRP\nmodel, for which the stretch relaxation time $\\tau_R$ is set to zero\nupfront so that any chain stretch relaxes to zero instantaneously,\nhowever strong the applied flow. The results of these calculations are\nexpected to apply, to good approximation, to experiments performed in\nflow regimes where chain stretch remains small. This typically imposes\nthe restriction $\\dot{\\gamma}\\tau_R\\ll 1$. We now turn to the sRP model to\nconsider the effects of finite chain stretch in experiments where this\nrestriction is not met.\n\n\n\\begin{figure}[tbp]\n\\includegraphics[width=8.5cm]{figure22.eps}\n\\caption{sRP model with a monotonic constitutive curve in LAOStress of\n stress amplitude $\\Sigma_{0}=0.8$ and frequency $\\omega=0.1$.\n Model parameters $\\beta=0.7, Z=100, \\eta=3.16\\times10^{-5}$. Cell curvature\n $q=2\\times 10^{-3}$. Number of numerical grid points $J=512$. {\\bf\n Left:} strain rate signal versus time. Solid black\n and red-dashed line: calculation in which the flow is constrained to\n be homogeneous. Red-dashed region indicates a positive eigenvalue\n showing instability to the onset of shear banding. Green dot-dashed\n line: stress response in a full nonlinear simulation that allows\n banding (indistinguishable from homogeneous signal in this\n case.) {\\bf Right:} Velocity profiles corresponding to stages in the\n cycle indicated by matching symbols in left panel.}\n\\label{fig:stretchPortrait1}\n\\end{figure}\n\nFig.~\\ref{fig:stretchMaster2} shows the regions of the plane of the CCR\nparameter $\\beta$ and entanglement number $Z$ in which banding can be\nexpected even with chain stretch. As for the case of LAOStrain above\nwe note that, depending on the value of $\\beta$, significant banding\nis still observed for experimentally commonly used entanglement\nnumbers, typically in the range $20-100$. Furthermore, observable\nbanding is clearly evident over a large region of this plane in which\nthe underlying constitutive curve is monotonic, precluding steady\nstate banding. Again, we hope that this figure might act as a roadmap\nto inform the discussion concerning the value of the CCR parameter\n$\\beta$.\n\n\nFor the pairing of $\\beta$ and $Z$ values marked by the square in\nFig.~\\ref{fig:stretchMaster2}, we show in\nFig.~\\ref{fig:stretchPortrait1} a detailed portrait of the model's nonlinear dynamics at a stress amplitude $\\Sigma_0$ and frequency $\\omega$ for which observable banding occurs.\n\n\n\\section{Conclusions}\n\\label{sec:conclusions}\n\nWe have studied theoretically the formation of shear bands in large\namplitude oscillatory shear (LAOS) in the Rolie-poly model of polymers and wormlike micellar surfactants, with the particular aims of\nidentifying the regimes of parameter space in which shear banding is\nsignificant, and the mechanisms that trigger its onset.\n\n\nAt low frequencies, the protocol of LAOStrain effectively corresponds\nto a repeating series of quasi-static sweeps up and down the steady\nstate flow curve. Here, as expected, we see shear banding in those\nregimes of parameter space for which the fluid's underlying\nconstitutive curve is non-monotonic, for strain rate amplitudes large\nenough to enter the banding regime in which the stress is a\ncharacteristically flat function of strain rate.\n\nIn LAOStrain at higher frequencies we report banding not only in the\ncase of a non-monotonic constitutive curve, but also over a large\nregion of parameter space for which the constitutive curve is\nmonotonic and so precludes steady state banding. We emphasise that\nthis is an intrinsically time-dependent banding phenomenon that would\nbe absent under steady state conditions, and we interpret it as the\ncounterpart of the `elastic' banding predicted recently in the context\nof shear startup experiments at high strain rates~\\cite{Moorcroft2013}.\n\nIn LAOStress we report shear banding in those regimes of parameter\nspace for which the underlying constitutive curve is either negatively\nor weakly positively sloping. In this case, the bands form during the\nprocess of yielding associated with the dramatic increase in shear\nrate that arises during that part of the cycle in which the stress magnitude\ntransits the regime of weak constitutive slope in an upward direction.\nAlthough the banding that we observe here is dramatically apparent\nduring those yielding events, these events are nonetheless confined to\na relatively small part of the stress cycle as a whole and would\ntherefore need careful focus to be resolved experimentally. (A\npossible related protocol, more focused on the banding regime,\ncould be to perform a shifted stress oscillation\n$\\Sigma(t)=\\Sigma_{\\rm plat} + \\Delta\\Sigma\\sin(\\omega t)$ where\n$\\Sigma_{\\rm plat}$ is a characteristic stress value in the region of\nweak slope in the constitutive curve and $\\Delta\\Sigma$ smaller in\ncomparison.) \n\nThe dramatic increase in strain rate associated with transiting to the\nhigh shear branch in LAOStress is likely to present practical\nexperimental difficulties in open flow cells such as Couette or\ncone-and-plate. To circumvent this, flow in a closed microfluidic channel provides an attractive alternative to those macroscopic\ngeometries in seeking to access this effect experimentally. \n\nIn each case we have demonstrated that the onset of shear banding can,\nfor the most part, be understood on the basis of previously derived\ncriteria for banding in simpler time-dependent\nprotocols~\\cite{Moorcroft2013}. In particular, the trigger for\nbanding in LAOStrain at low frequencies is that of a negatively\nsloping stress versus strain rate, which has long been recognised as\nthe criterion for banding under conditions of a steady applied shear\nflow. The trigger in LAOStrain at high frequencies is instead that of\nan overshoot in the signal of stress as a function of strain, in close\nanalogy to the criterion for banding onset during a fast shear startup\nrun. The trigger for banding in LAOStress is that of a regime of\nsimultaneous upward slope and upward curvature in the\ntime-differentiated creep response curve of strain rate as a function\nof time. This again is a close counterpart to the criterion for\nbanding following the imposition of a step stress.\n\nFor both LAOStrain and LAOStress we have provided a map of shear\nbanding intensity in the space of entanglement number $Z$ and CCR\nparameter $\\beta$. We hope that this will provide a helpful roadmap\nexperimentalists, and might even help to pin down the value of the CCR\nparameter, for which no consensus currently exists. \n\nWe have also commented that the value of the Newtonian viscosity\n$\\eta$ is typically much smaller than the zero shear viscosity $G\\tau$\nof the viscoelastic contribution, giving $\\eta\\ll 1$ in our units.\n\nExperimental literature suggests a range $\\eta=10^{-7}$ to $10^{-3}$.\nWe have typically used $\\eta=10^{-5}$ or\n$\\eta=10^{-4}$ in our numerics, and noted that the degree of banding\ntends to increase with decreasing $\\eta$. We also noted that the\ntimescale to transit from the high to low viscosity branch during\nyielding in each half cycle in LAOStress decreases with decreasing\n$\\eta\/G$. In view of these facts, a study of time-dependent banding in\nfluids with smaller values of $\\eta$ than those used here might\nwarrant the inclusion of inertia, because the small timescale for the\npropagation of momentum might exceed the short timescale $\\eta\/G$ in\nthose cases\n\nIn all our numerical studies the initial seed triggering the formation\nof shear bands was taken to be the weak curvature that is present in\ncommonly used experimental flow cells. In order to demonstrate the\nprinciple that the banding we report requires only a minimal seed,\nrather than being an artefact of strong cell curvature, all our runs\nhave assumed a curvature that is much smaller than that of most flow\ncells in practice. We also neglected stochastic noise altogether in\nall the results presented here. (We have nonetheless also performed\nruns with small stochastic noise instead of cell curvature and find\nqualitatively all the same effects.)\n\nHowever, one obvious shortcoming to this approach of taking only a very\nsmall initial seed is that it tends to suppress the nucleation events\nthat are, in a real experimental situation, likely to trigger banding\neven before the regime of true linear\ninstability~\\cite{Grand1997}, particularly in low frequency\nruns. It would therefore be interesting in future work to study the\neffect of a finite temperature with particular regard to the\nnucleation kinetics to which it would give rise.\n\nThe calculations performed in this work all assumed from the outset\nthat spatial structure develops only in the flow gradient direction,\nimposing upfront translational invariance in the flow and vorticity\ndirections. We defer to future work a study of whether, besides the\nbasic shear banding instabilities predicted here, secondary\ninstabilities~\\cite{Fardin2014} of the interface between the\nbands~\\cite{Nghe2010,Fielding2005}\nor of the high shear band itself~\\cite{Fielding2010} will have\ntime to form in any given regime of amplitude and frequency space.\n\nWe have ignored throughout the effects of spatial variations in\n the concentration field. However, it is well known that in a\n viscoelastic solution heterogeneities in the flow field, and in\n particular in the normal stresses, can couple to the dynamics of\n concentration fluctuations via a positive feedback mechanism that\n enhances the tendency to form shear\n bands~\\cite{Milner1993,Schmitt1995,Fielding2003a,Fielding2003b,Fielding2003c}.\n In the calculations performed here in LAOS we have observed\n significant differences in the viscoelastic normal stresses between\n the bands (approaching $50-70\\%$ of the cycle-averged value of the\n same quantity, at least in the calculations without chain stretch).\n It would therefore clearly be interesting in future work to consider\n the effects of concentration coupling on the phenomena reported\n here.\n\nThroughout we have ignored the possibility of edge fracture,\n because the one-dimensional calculations performed here lack any\n free surfaces and are unable to address it. It would clearly be\n interesting in future work to address the effects of edge fracture\n with regards the phenomena considered here~\\cite{Skorski2011,\n Li2013, Li2015}.\n\nAll the calculations performed here have adopted what is\n essentially a single-mode approach, taking account of just one\n reptation relaxation timescale $\\tau_d$ and one stretch relaxation\n timescale $\\tau_R$. It would be interesting in future work to\n consider the effect of multiple relaxation timescales, which is\n likely to be an important feature of the dynamics of unbreakable\n polymers. (In wormlike micelles, in contrast, chain breakage and\n recombination narrows the relaxation spectrum significantly such\n that the single-mode approach adopted here is already likely to\n provide a reasonably full picture.)\n\nWe hope that this work will stimulate further experimental studies of\nshear banding in time-dependent flows of complex fluids, with a\nparticular focus on the concept that banding is likely to arise rather\ngenerically during yielding-like events (following a stress overshoot\nin strain controlled protocols, or during a sudden increase in strain\nrate in stress controlled protocols) even in fluids with a monotonic\nconstitutive curve that precludes steady state banding in a\ncontinuously applied shear. In polymers this could form part of the\nlively ongoing debate concerning the presence or otherwise of shear\nbanding in those materials. In wormlike micelles it would be\ninteresting to see a study of LAOS across the full phase diagram (as\nset out, for example, in Ref.~\\cite{Berret1997}), from\nsamples that band in steady state to those above the dynamical\ncritical point, which don't.\n\n{\\it Acknowledgements} The authors thank Alexei Likhtman, Elliot\nMarsden, Peter Olmsted, Rangarajan Radhakrishnan, Daniel Read and\nDimitris Vlassopoulos for interesting discussions. The research\nleading to these results has received funding from the European\nResearch Council under the European Union's Seventh Framework\nProgramme (FP\/2007-2013), ERC grant agreement number 279365.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nMyocardial Contrast Echocardiography (MCE) is a cardiac ultrasound imaging technique that utilizes vessel-bound microbubbles as contrast agents. In contrast to conventional B-mode echocardiography which only captures the structure and motion of the heart, MCE also allows for the assessment of myocardial perfusion through the controlled destruction and replenishment of microbubbles \\cite{wei_quantification_1998}. The additional perfusion information gives it great potential for the detection of coronary artery diseases. However, current perfusion analysis of MCE data mainly relies on human visual assessment which is time consuming and not reproducible \\cite{DBLP:conf\/fimh\/MaSRBL09}. There is generally a lack of automatic computerized algorithms and methods to help clinician perform accurate perfusion quantification \\cite{DBLP:conf\/fimh\/MaSRBL09}. One major challenge is the automatic segmentation of the myocardium before subsequent perfusion analysis can be carried out.\n\nIn this paper, we extend the Random Forests (RF) framework \\cite{DBLP:journals\/ml\/Breiman01} to segment the myocardium in our MCE data. RF is a machine learning technique that has gained increasing use in the medical imaging field for tasks such as segmentation \\cite{lempitsky_random_2009} and organ localization \\cite{DBLP:journals\/mia\/CriminisiRKSPWS13}. RF has been successful due to its accuracy and computational efficiency. Promising results of myocardial delineation on 3D B-mode echo has also been demonstrated in \\cite{lempitsky_random_2009}. However, classic RF has two limitations. First, our MCE data exhibit large sources of intensity variations \\cite{tang_quantitative_2011} due to factors such as speckle noise, low signal-to-noise ratio, attenuation artefacts, unclear and missing myocardial borders, presence of structures (papillary muscle) with similar appearance to the myocardium. These intensity variations reduce the discriminative power of the classic RF that utilizes only local intensity features. Second, RF segmentation operates on a pixel basis where the RF classifier predicts a class label for each pixel independently. Structural relationships and contextual dependencies between pixel labels are ignored \\cite{DBLP:conf\/iccv\/KontschiederBBP11,DBLP:conf\/ipmi\/MontilloSWIMC11} which results in segmentation with inconsistent pixel labelling leading to unsmooth boundaries, false detections in the background and holes in the region of interest. To overcome the above two problems, we need to incorporate prior knowledge of the shape of the structure and use additional contextual and structural information to guide the RF segmentation.\n\\\\\n\\indent\nThere are several works which have incorporated local contextual information into the RF framework. Lempitsky et al. \\cite{lempitsky_random_2009} use the pixel coordinates as position features for the RF so that the RF learns the myocardial shape implicitly. Tu et Bai \\cite{DBLP:journals\/pami\/TuB10} introduce the concept of auto-context which can be applied to RF by using the probability map predicted by one RF as features for training a new RF. Montillo et al. \\cite{DBLP:conf\/ipmi\/MontilloSWIMC11} extend the auto-context RF by introducing entanglement features that use intermediate probabilities derived from higher levels of a tree to train its deeper levels. Kontschieder et al. \\cite{DBLP:conf\/iccv\/KontschiederBBP11} introduce the structured RF that builds in structural information by using RF that predicts structured class labels for a patch rather than the class label of an individual pixel. Lombaert et al. \\cite{DBLP:conf\/miccai\/LombaertCA15} use spectral representations of shapes to classify surface data.\n\\\\\n\\indent\nThe above works use local contextual information that describes the shape of a structure implicitly. The imposed structural constraint are not strong enough to guide the RF segmentation in noisy regions of MCE data. In this paper, we proposed the Shape Model guided Random Forests (SMRF) which provides a new way to incorporate global contextual information into the RF framework by using a statistical shape model that captures the explicit shape of the entire myocardium. This imposes stronger, more meaningful structural constraints that guide the RF segmentation more accurately. The shape model is learned from a set of training shapes using Principal Component Analysis (PCA) and is originally employed in Active Shape Model (ASM) where the model is constrained so that it can only deform to fit the data in ways similar to the training shapes \\cite{DBLP:journals\/cviu\/CootesTCG95}. However, ASM requires a manual initialization and the final result is sensitive to the position of this initialization. Our SMRF is fully automatic and enjoys both the local discriminative power of the RF and the prior knowledge of global structural information contained in the statistical shape model. The SMRF uses the shape model to guide the RF segmentation in two ways. First, it directly incorporates the shape model into the RF framework by introducing a novel Shape Model (SM) feature which has outperformed the other contextual features and produced a more accurate RF probability map. Second, the shape model is fitted to the probability map to generate a smooth and plausible myocardial boundary that can be used directly for subsequent perfusion analysis.\n\n\\section{Method}\nIn this section, we first review some basic background on statistical shape model and RF. We then introduce the two key aspects of our SMRF---the novel SM feature and the fitting of the shape model.\n\\subsubsection{\\textit{Statistical Shape Model:}}\nA statistical shape model of the myocardium is built from 89 manual annotations using PCA \\cite{DBLP:journals\/cviu\/CootesTCG95}. Each annotation has $N=76$ landmarks comprising 4 key landmarks with 18 landmarks spaced equally in between along the boundary of manual tracing (Fig. \\ref{fig:Model} left). The shape model is represented as:\n\\begin{equation}\\label{eq:Model}\n \\boldsymbol{x}=\\bar { \\boldsymbol{x} } + \\boldsymbol{Pb}\n\\end{equation}\n\\noindent where $\\boldsymbol{x}$ is a 2$N$-D vector containing the 2D coordinates of the $N$ landmark points, $\\bar{\\boldsymbol{x}}$ is the mean coordinates of all training shapes, $\\boldsymbol{b}$ is a set of $K$ shape parameters and $\\boldsymbol{P}$ contains $K$ eigenvectors with their associated eigenvalues ${ \\lambda }_{ i }$. $K$ is the number of modes and set to 10 to explain 98\\% of total variance so that fine shape variations are modeled while noise is removed. Values of ${b}_{i}$ are bounded between $\\pm s\\sqrt { \\lambda _{ i } } $ so that only plausible shape similar to the training set is generated (Fig. \\ref{fig:Model} right). Refer to \\cite{DBLP:journals\/cviu\/CootesTCG95} for details on statistical shape model.\n\\subsubsection{\\textit{Random Forests:}}\nMyocardial segmentation can be formulated as a problem of binary classification of image pixels. An RF classifier \\cite{DBLP:journals\/ml\/Breiman01} is developed that predicts the class label (myocardium or background) of a pixel using a set of features. The RF is an ensemble of decision trees. During training, each branch node of a tree learns a pair of feature and threshold that results in the best split of the training pixels into its child nodes. The splitting continues recursively until the maximum tree depth is reached or the number of training pixels in the node falls below a minimum. At this time, a leaf node is created and the class distribution of the training pixels reaching the leaf node is used to predict the class label of unseen test pixels. The average of the predictions from all the trees gives a segmentation probability map. Refer to \\cite{DBLP:journals\/ml\/Breiman01}, \\cite{lempitsky_random_2009} for details on RF.\n\\begin{figure}\n\\centering\n\\begin{subfigure}[b]{.5\\textwidth}\n \\centering\n \\includegraphics[height=0.16\\textheight]{Fig1}\n \\caption{}\n \\label{fig:Model}\n\\end{subfigure}%\n\\begin{subfigure}[b]{.5\\textwidth}\n \\centering\n \\includegraphics[height=0.16\\textheight]{Fig2}\n \\caption{}\n \\label{fig:Feature}\n\\end{subfigure}\n\\caption{(a) Left: A manual annotation from training set showing key landmarks (\\textit{red}) and other landmarks in between (\\textit{green}). Right: First two modes of variations of the shape model. (b) Left: Landmarks $\\boldsymbol{x}$ (\\textit{blue dots}) generated randomly by the shape model in (\\ref{eq:Model}). Right: $d_1$($d_2$) is the SM feature value measuring the signed shortest distance from pixel $\\boldsymbol{p_1}$($\\boldsymbol{p_2}$) to the myocardial boundary $B(\\boldsymbol{x})$ (\\textit{blue contour}). $d_1$ is positive and $d_2$ is negative.}\n\\label{fig:Result}\n\\end{figure}\n\\subsubsection{\\textit{Shape Model Feature:}}\nThe classic RF uses local appearance features which are based on surrounding image intensities of the reference pixel \\cite{DBLP:journals\/mia\/CriminisiRKSPWS13}. We introduced an additional novel SM feature that is derived from the shape model. The SM feature randomly selects some values for the shape model parameters $\\boldsymbol{b}$ and generates a set of landmarks $\\boldsymbol{x}$ using (\\ref{eq:Model}) (Fig. \\ref{fig:Feature} left). The landmarks can be joined to form a myocardial boundary. Let $B(\\bar { \\boldsymbol{x} } +\\boldsymbol{Pb})$ be the myocardial boundary formed by joining the landmarks generated using some values of $\\boldsymbol{b}$. The SM feature value is then given by the signed shortest distance $d$ from the reference pixel $\\boldsymbol{p}$ to the boundary $B$ (Fig. \\ref{fig:Feature} right). The distance is positive if $\\boldsymbol{p}$ lies inside the boundary and negative if it lies outside. The SM feature is essentially the signed distance transform of a myocardial boundary generated by the shape model. Each SM feature is defined by the shape parameters $\\boldsymbol{b}$. During training, an SM feature is created by random uniform sampling of each $b_{i}$ in the range of $\\pm s_{feature}\\sqrt { \\lambda _{ i } }$ where $s_{feature}$ is set to 1 in all our experiments. The binary SM feature test, parameterized by $\\boldsymbol{b}$ and a threshold $\\tau$, is written as:\n\\begin{equation}\n{ t }_{ \\textrm{SM} }^{ \\boldsymbol{b},\\tau }(\\boldsymbol{p})=\\begin{cases} 1,\\qquad \\textrm{if}\\quad D(\\boldsymbol{p},B(\\bar { \\boldsymbol{x} } +\\boldsymbol{Pb}))>\\tau \\\\ 0,\\qquad \\textrm{otherwise}. \\end{cases}\n\\end{equation}\n\\noindent where $D(.)$ is the function that computes $d$. Depending on the binary test outcome, pixel $\\boldsymbol{p}$ will go to the left (1) or right (0) child node of the current split node. During training, the RF learns the values of $\\boldsymbol{b}$ and $\\tau$ that best split the training pixels at a node. The SM features explicitly impose a global shape constraint in the RF framework. The random sampling of $\\boldsymbol{b}$ also allows the RF to learn plausible shape variations of the myocardium.\n\\subsubsection{\\textit{Shape Model Fitting:}}\nThe RF output is a probability map which cannot be used directly in subsequent analysis and application. Simple post-processing on the probability map such as thresholding and edge detection can produce segmentations with inaccurate and incoherent boundaries due to the nature of the pixel-based RF classifier. Our SMRF fits the shape model to the RF probability map to extract a final myocardial boundary that is smooth and which preserves the integrity of the myocardial shape. The segmentation accuracy is also improved as the shape constraint imposed by the shape model can correct some of the misclassifications made by the RF.\n\nLet ${ T }_{ \\boldsymbol{\\theta}}$ be a pose transformation defined by the pose parameter $\\boldsymbol{\\theta}$ which includes translation, rotation and scaling. The shape model fitting is then an optimization problem where we want to find the optimal values of $(\\boldsymbol{b},\\boldsymbol{\\theta})$ such that the model best matches the RF probability map under some shape constraints. We minimize the following objective function:\n\\begin{equation}\n\\begin{aligned}\n& \\underset{\\boldsymbol{b},\\boldsymbol{\\theta}}{\\text{min}}\n& & { { \\left\\| { \\boldsymbol{I} }_{ \\textrm{RF} }-{ \\boldsymbol{I} }_{ \\textrm{M} }({ T }_{ \\boldsymbol{\\theta} }(\\bar { \\boldsymbol{x} } +\\boldsymbol{Pb})) \\right\\| }^{ 2 }+\\alpha \\frac { 1 }{ K } \\sum _{ i=1 }^{ K }{ \\frac { \\left| { b }_{ i } \\right| }{ \\sqrt { { \\lambda }_{ i } } } } } \\\\\n& \\text{subject to}\n& & -{ s }_{ fit }\\sqrt { { \\lambda }_{ i } } <{ b }_{ i }<{ s }_{ fit }\\sqrt { { \\lambda }_{ i } }, \\; i = 1, \\ldots, K.\n\\end{aligned}\n\\end{equation}\nThe first term of the objective function compares how well the match is between the model and the RF probability map $\\boldsymbol{I}_{\\textrm{RF}}$. $\\boldsymbol{I}_{\\textrm{M}}(.)$ is a function that converts the landmarks generated by the shape model into a binary mask of the myocardial shape. This allows us to evaluate a dissimilarity measure between the RF segmentation and the model by computing the sum of squared difference between the RF probability map and the model binary mask. The second term of the objective function is a regularizer which imposes a shape constraint. It is related to the probability of a given shape \\cite{DBLP:journals\/pr\/CristinacceC08} and ensures that it does not deviate too much away from its mean shape. $\\alpha$ is the weighting given to the regularization term and its value is determined empirically. Finally, an additional shape constraint is imposed on the objective function by limiting the upper and lower bounds of $b_i$ to allow for only plausible shapes. $s_{fit}$ is set to 2 in all our experiments. The optimization is carried out using direct search which is a derivative-free solver from the MATLAB global optimization toolbox. At the start of the optimization, each $b_i$ is initialized to zero. Pose parameters are initialized such that the model shape is positioned in the image center with no rotation and scaling.\n\\section{Experiments}\n\\subsubsection{\\textit{Datasets:}}\n2D+t MCE sequences were acquired from 15 individuals using a Philips iE33 ultrasound machine and SonoVue as the contrast agent. Each sequence is taken in the apical 4-chamber view under the triggered mode which shows the left ventricle at end-systole. One 2D image was chosen from each sequence and the myocardium manually segmented by two experts to give inter-observer variability. This forms a dataset of 15 2D MCE images for evaluation. Since the appearance features of the RF are not intensity invariant, all the images are pre-processed with histogram equalization to reduce intensity variations between different images. The image size is approximately 351$\\times$303 pixels.\n\\begin{figure}\n\\centering\n\\begin{subfigure}[b]{.55\\textwidth}\n \\centering\n \\includegraphics[width=\\linewidth]{Fig3}\n \\caption{}\n \\label{fig:ResultVisual}\n\\end{subfigure}%\n\\begin{subfigure}[b]{.45\\textwidth}\n \\centering\n \\includegraphics[width=\\linewidth]{Fig4}\n \\caption{}\n \\label{fig:ResultQuant}\n\\end{subfigure}\n\\caption{(a) Visual segmentation results with one MCE example on each row. First three columns: Probability maps from classic RF, position feature RF and SMRF respectively. Last column: Ground truth boundary (\\textit{red}) and the SMRF boundary (\\textit{blue}) obtained from fitting the shape model to the SMRF probability map in the third column. \\textit{Black arrows} indicate papillary muscle. (b) Segmentation accuracy of different RF classifiers at different tree depths.}\n\\label{fig:Result}\n\\end{figure}\n\\subsubsection{\\textit{Validation Methodology:}}\nWe compared our SMRF segmentation results to the classic RF that uses appearance features \\cite{DBLP:journals\/mia\/CriminisiRKSPWS13}, as well as RFs that use other contextual features such as entanglement \\cite{DBLP:conf\/ipmi\/MontilloSWIMC11} and position features \\cite{lempitsky_random_2009}. We also compared our results to repeated manual segmentations and the Active Shape Model (ASM) method \\cite{DBLP:journals\/tmi\/GinnekenFSRV02}. Segmentation accuracy is assessed quantitatively using pixel classification accuracy, Dice and Jaccard indices, Mean Absolute Distance (MAD) and Hausdorff Distance (HD). To compute the distance error metrics (MAD and HD), a myocardial boundary is extracted from the RF probability map using the Canny edge detector. This is not required for the SMRF in which the shape model fitting step directly outputs a myocardial boundary.\n\\\\\n\\indent\nWe performed leave-one-out cross-validation on our dataset of 15 images. The RF parameters are determined experimentally and then fixed for all experiments. 20 trees are trained with maximum tree depth of 24. 10\\% of the pixels from the training images are randomly selected for training. The RF and the shape model fitting were implemented in C\\# and MATLAB respectively. Given an unseen test image, RF segmentation took 1.5min with 20 trees and shape model fitting took 8s on a machine with 4 cores and 32GB RAM. RF training took 38mins.\n\\section{Results}\nFig. \\ref{fig:ResultVisual} qualitatively shows that our SMRF probability map (column 3) has smoother boundary and more coherent shape than the classic RF (column 1) and position feature RF (column 2). Fitting the shape model to the SMRF probability map produces the myocardial boundary (\\textit{blue}) in column 4. The fitting guides the RF segmentation especially in areas where the probability map has a low confidence prediction. In the example on the second row, our SMRF predicts a boundary that correctly excludes the papillary muscle (\\textit{black arrows}). This is often incorrectly included by the other RFs due to its similar appearance to the myocardium.\n\\begin{table}[]\n\\centering\n\\caption{Quantitative comparison of segmentation results between the proposed SMRF and other methods. Results presented as (Mean $\\pm$ Standard Deviation).}\n\\label{table:Result}\n\\begin{tabular}{|M{10.5em}|c|c|c|c|r|}\n\\hline\n & \\multicolumn{1}{c|}{Accuracy} & \\multicolumn{1}{c|}{Dice} & \\multicolumn{1}{c|}{Jaccard} & \\multicolumn{1}{c|}{\\begin{tabular}[c]{@{}c@{}}MAD\\\\ (mm)\\end{tabular}} & \\multicolumn{1}{c|}{\\begin{tabular}[c]{@{}c@{}}HD\\\\ (mm)\\end{tabular}} \\\\ \\hline\nIntra-observer & 0.96$\\pm$0.01 & 0.89$\\pm$0.02 & 0.80$\\pm$0.03 & 1.02$\\pm$0.26 & 3.75$\\pm$0.93 \\\\\nInter-observer & 0.94$\\pm$0.02 & 0.84$\\pm$0.05 & 0.72$\\pm$0.07 & 1.59$\\pm$0.57 & 6.90$\\pm$3.24 \\\\\nASM \\cite{DBLP:journals\/tmi\/GinnekenFSRV02} & 0.92$\\pm$0.03 & 0.77$\\pm$0.08 & 0.64$\\pm$0.11 & 2.23$\\pm$0.81 & 11.44$\\pm$5.23 \\\\ \\hline\nClassic RF & 0.91$\\pm$0.04 & 0.74$\\pm$0.12 & 0.60$\\pm$0.14 & 2.46$\\pm$1.36 & 15.69$\\pm$7.34 \\\\\nEntangled RF \\cite{DBLP:conf\/ipmi\/MontilloSWIMC11} & 0.91$\\pm$0.05 & 0.75$\\pm$0.13 & 0.62$\\pm$0.15 & 2.43$\\pm$1.62 & 15.06$\\pm$7.92 \\\\\nPosition Feature RF \\cite{lempitsky_random_2009} & \\textbf{0.93$\\pm$0.03} & \\textbf{0.81$\\pm$0.10} & 0.69$\\pm$0.13 & 1.81$\\pm$0.84 & 9.51$\\pm$3.80 \\\\\nSMRF & \\textbf{0.93$\\pm$0.03} & \\textbf{0.81$\\pm$0.10} & \\textbf{0.70$\\pm$0.12} & \\textbf{1.68$\\pm$0.72} & \\textbf{6.53$\\pm$2.61} \\\\ \\hline\n\\end{tabular}\n\\end{table}\n\nTable. \\ref{table:Result} compares the quantitative segmentation results of our SMRF to other methods. Both SM features and position features encode useful structural information that produces more accurate RF probability maps than the classic RF and entangled RF. This is reflected by the higher Dice and Jaccard indices. For MAD and HD metrics, SMRF outperforms all other RF methods because the shape model fitting step in SMRF produces more accurate myocardial boundaries than those extracted using the Canny edge detector. In addition, SMRF also outperforms ASM \\cite{DBLP:journals\/tmi\/GinnekenFSRV02} and comes close to the inter-observer variations.\n\nFig. \\ref{fig:ResultQuant} compares the segmentation accuracy of the probability maps of different RF classifiers. Our SMRF obtained higher Jaccard indices than the classic and entangled RFs at all tree depths. At lower tree depths, SMRF shows notable improvement over the position feature RF. The SM features have more discriminative power than the position features as it captures the explicit geometry of the myocardium using the shape model. The SM feature binary test partitions the image space using more complex and meaningful myocardial shapes as opposed to position feature which simply partitions the image space using straight lines. This provides a stronger global shape constraint than the position feature and allows a decision tree to converge faster to the correct segmentation at lower tree depths. This gives the advantage of using trees with smaller depths which speeds up both training and testing.\n\\section{Conclusion}\nWe presented a new method SMRF for myocardial segmentation in MCE images. We showed how our SMRF utilizes a statistical shape model to guide the RF segmentation. This is particular useful for MCE data whose image intensities are affected by many variables and therefore prior knowledge of myocardial shape becomes important in guiding the segmentation. Our SMRF introduces a new SM feature which captures the global myocardial structure. This feature outperforms other contextual features to allow the RF to produce a more accurate probability map. Our SMRF then fits the shape model to the RF probability map to produce a smooth and coherent final myocardial boundary that can be used in subsequent perfusion analysis. In future work, we plan to validate our SMRF on a larger, more challenging dataset which includes different cardiac phases and chamber views.\n\\subsubsection*{Acknowledgments.} The authors would like to thank Prof. Daniel Rueckert, Liang Chen and other members from the BioMedIA group for their help and advice. This work was supported by the Imperial College PhD Scholarship.\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\\label{sec:intro}\n\\subsection{Background}\nThe present paper studies phase transitions from an ergodic theory and dynamical system viewpoint. It investigates the relations between renormalization, substitutions and phase transition initiated in \\cite{baraviera-leplaideur-lopes} and continued in \\cite{BL}.\n\nPhase transitions are an important topic in statistical mechanics and also in probability theory (see {\\em e.g.\\ } \\cite{georgii,grimmett}). \nThe viewpoint presented here is different for several reasons. One of them is that, here, the geometry of the lattice is not relevant\\footnote{and we only consider a one-dimensional lattice.}, whereas in statistical mechanics, \nthe geometry of the lattice is the most relevant part. \n\nA {\\em phase transition} is characterized by a lack of analyticity of the \npressure function. \nThis definition of phase transition is inspired by statistical mechanics \nand is now standard for dynamical systems, see \\cite{bowen, ruelle, sinai}. \nGiven a dynamical systems, say $(X,T)$, and a potential $\\varphi:X\\to{\\mathbb R}$, the pressure function is given by \n$$\n\\CP(\\beta):=\\sup\\left\\{h_{\\mu}(T)+\\beta \\int\\varphi\\,d\\mu\\right\\},\n$$\nwhere the supremum is taken over the invariant probability measures $\\mu$, $h_{\\mu}(T)$ is the Kolmogorov entropy and $\\beta$ is a real parameter. \n\nFor a uniformly hyperbolic dynamical system $(X,T)$ and \na H\\\"older continuous potential $\\varphi$, the pressure function $\\beta\\mapsto \\CP(\\beta)$ is analytic (see {\\em e.g.\\ } \\cite{bowen, ruelle, keller}). Even if analyticity is usually considered as a very rigid property and thus quite rare, it turns out that proving non-analyticity for the pressure function is not so easy. \nCurrently, this has become an important challenge in smooth ergodic theory to produce and study phase transitions, see {\\em e.g.\\ } \\cite{Makarov-Smirnov, coronel-Rivera-Letelier, BL, iommi-todd}. \n\nTo observe phase transitions, one has to weaken hyperbolicity of the system\nor of regularity of the potential; it is the latter one that we continue to investigate here. Our dynamical system is the full shift, which is uniformly hyperbolic. \nThe first main question we want to investigate is thus what potentials $\\varphi$ will produce phase transitions. More precisely, we are looking for a machinery to produce potentials with phase transitions. \n\nThe main purpose of \\cite{baraviera-leplaideur-lopes} was to investigate possible relation between {\\em renormalization} and phase transition. In the shift\nspace $(\\{0,1\\}^{{\\mathbb N}}, \\sigma)$, a renormalization is a function $H$ satisfying an equality of the form \n\\begin{equation}\\label{equ1-renorm}\n\\sigma^{k}\\circ H=H\\circ \\sigma.\n\\end{equation}\n The link with potentials was made in \\cite{baraviera-leplaideur-lopes} by introducing a renormalization operator $\\CR$ acting on potentials and related to a solution $H$ for \\eqref{equ1-renorm}.\n\nIt is easy to check that constant length $k$ substitutions are \nsolutions to the renormalization equation.\nIn \\cite{BL}, we studied the Thue-Morse case substitution,\nwhich has constant length $2$. \nHere we investigate the Fibonacci substitution, which is not of constant length. Several reasons led us to study the Fibonacci case:\n\n$\\bullet$ Together with the Thue-Morse substitution, the Fibonacci substitutions is the most ``famous'' substitution and it has been well-studied. In particular, the dynamical properties of their respective attracting sets are well-known and this will be\nused extensively in this paper for the Fibonacci shift. As a result, we were \nable to describe the relevant fixed point of renormalization exactly. \nInformation of the left and right-special words in these attractors is \na key ingredient to prove existence of a phase transition; it \nis a crucial issue in the relations between \nsubstitutions and phase transitions. \n\n$\\bullet$ The type of phase transition we establish is a {\\em freezing phase transition}. This means that beyond the phase transition ({\\em i.e.,\\ } for large $\\beta$), \nthe pressure function is affine and equal to its asymptote, and the equilibrium state ({\\em i.e.,\\ } ground state) is the unique shift-invariant measure\nsupported on an aperiodic subshift space, sometimes called {\\em quasi-crystal}.\nOne open question in statistical mechanics (see \\cite{vanEnter?}) is whether freezing phase transitions can happen and whether {\\em quasi-crystal ground state}\ncan be reach at {\\em positive temperature}. An affirmative answer was given for the Thue-Morse quasi-crystal in \\cite{BL}; we show here this also holds for the Fibonacci quasi-crystal. \n\n$\\bullet$ \nWe think that Fibonacci shift opens the door to study more cases. One natural question is whether any quasi-crystal can be reached as a ground state at positive temperature. In this context we emphasize that the Fibonacci substitution also has a Sturmian shift, that is, it is related to the irrational rotation with angle the golden mean $\\gamma:=\\frac{1+\\sqrt5}2$. \nWe expect that the machinery developed here for the Fibonacci substitution can\nbe extended to the Sturmian shift associated to general irrational rotation\nnumbers \n(although those with bounded entries in the continued fraction expansion\nwill be the easiest), possibly to rotations on higher-dimensional tori, and also to more general substitutions. \n\n\n \n\n\\subsection{Results}\n\nLet $\\S = \\{0,1\\}^{{\\mathbb N}}$ be the full shift space; points in $\\S$ \nare sequences $x:=(x_{n})_{n\\ge 0}$ or equivalently infinite {\\em words} $x_{0}x_{1}\\ldots$. \nThroughout we let $\\overline x_j = 1-x_j$ denote the opposite symbol.\nThe dynamics is the left-shift \n$$\n\\sigma: x=x_{0}x_{1}x_{2}\\ldots\\mapsto x_{1}x_{2}\\ldots.\n$$\nGiven a word $w=w_{0}\\ldots w_{n-1}$ of {\\em length} $|w|=n$, the corresponding\n{\\em cylinder} (or {\\em $n$-cylinder}) is the set of infinite words starting as $w_{0}\\ldots w_{n-1}$. We use the notation\n$C_n(x)=[x_0\\dots x_{n-1}]$\nfor the $n$-cylinder containing $x=x_{0}x_{1}\\ldots$ \nIf $w=w_{0}\\ldots w_{n-1}$ is a word with length $n$ and $w'=w'_{0}\\ldots$ a word of any length, the {\\em concatenation} $ww'$ is the word $w_{0}\\ldots w_{n-1}w'_{0}\\ldots$. \n\nThe Fibonacci substitution on $\\S$ is defined by: \n$$\nH: \\begin{cases}\n0 \\to 01\\\\\n1 \\to 0.\n\\end{cases}\n$$\nand extended for words by the concatenation rule $H(ww')=H(w)H(w')$.\nIt is convenient for us to count the Fibonacci numbers starting with index $-2$:\n\\begin{equation}\\label{eq:Fibo}\nF_{-2} = 1,\\ F_{-1} = 0,\\ F_0=1, \\ F_1 = 1,\\ F_2 = 2,\\ F_{n+2} = F_{n+1} + F_n,\n\\end{equation}\nWe have\n\\begin{equation}\\label{eq:Fiboa}\nF_{n}^{a}:=|H^{n}(a)| = \\begin{cases}\nF_{n+1} & \\text{ if } a = 0,\\\\\nF_{n} & \\text{ if } a = 1.\n\\end{cases}\n\\end{equation}\nThe Fibonacci substitution has a unique fixed point\n$$\n\\rho = 0\\ 1\\ 0\\ 01\\ 010\\ 01001\\ 01001010\\ 0100101001001\\dots\n$$\nWe define the orbit closure ${\\mathbb K} = \\overline{\\cup_n \\sigma^n(\\rho)}$; it forms a subshift\nof $(\\S, \\sigma)$ associated to $\\rho$. More properties on ${\\mathbb K}$ are given in Section~\\ref{sec-H-K-R}.\n\n\\bigskip\nWe define the renormalization operator \nacting on potentials $V:\\S\\to {\\mathbb R}$ by \n$$\n(\\CR V)(x)= \\begin{cases}\n V\\circ \\sigma\\circ H(x)+V\\circ H(x) & \\text{ if }x\\in[0],\\\\\n V\\circ H(x) & \\text{ if }x\\in[1].\n\\end{cases}\n$$\nWe are interested in finding fixed points for $\\CR$ and, where possible, studying their stable leaves,\n{\\em i.e.,\\ } potentials converging to the fixed point under iterations of $\\CR$. \nContrary to the Thue-Morse substitution, \nthe Fibonacci substitution is not of constant length. This is the source of several complications, in particular for the correct expression for $\\CR^{n}$. \n\nFor $\\alpha>0$, let $\\CX_{\\alpha}$ be the set of functions $V: \\S \\to {\\mathbb R}$ \nsuch that $}%{\\displaystyle V(x) \\sim n^{-\\alpha}$ if $d(x,{\\mathbb K})=2^{-n}$. \nMore precisely, $\\CX_{\\alpha}$ is the set of functions $V$ such that:\n\\begin{enumerate}\n\\item $V$ is continuous and non-negative. \n\\item There exist two continuous functions $g,h:\\S \\to {\\mathbb R}$, satisfying $}%{\\displaystyle h_{|{\\mathbb K}}\\equiv 0$ and $g>0$, such that \n$$\nV(x) = \\frac{g(x)}{n^\\alpha} + \\frac{h(x)}{n^\\alpha} \\quad \\text{ when } \\quad \nd(x,{\\mathbb K}) = 2^{-n}.\n$$\n\\end{enumerate}\nWe call $g$ the \\textit{$\\alpha$-density}, or just the \\textit{density} \nof $V\\in \\CX_{\\alpha}$. Continuity and the assumption $h_{|{\\mathbb K}}\\equiv 0$ imply \nthat $h(x)\/n^{\\alpha} = o(n^{-\\alpha})$.\n\nOur first theorem achieves the existence of a fixed point for $\\CR$ and shows that the germ of $V$ close to ${\\mathbb K}$, {\\em i.e.,\\ } its $\\alpha$-density, allows us to \ndetermine the stable leaf of that fixed point. \n\nGiven a finite word $w$, let\n$\\kappa_a(w)$ denote the number of symbols $a\\in\\{0,1\\}$ in $w$.\nIf $x \\in \\S \\setminus {\\mathbb K}$, we denote by $\\widetilde\\kappa_{a}(x)$ the number of symbols $a$ in the finite word $x_{0}\\ldots x_{n-1}$ where $d(x,{\\mathbb K})=2^{-n}$. \n\n\\begin{theorem} \\label{theo-fixedpoint}\nIf $V \\in \\CX_\\alpha$, with $\\alpha$-density function $g$, then\n$$\n\\lim_{k\\to\\infty} \\CR^{k}V(x) =\n\\begin{cases}\n\\quad \\infty & \\text{ for all } x\\in \\S\\setminus {\\mathbb K} \\text{ if } \\alpha < 1; \\\\[2mm]\n\\quad 0 & \\text{ for all } x\\in \\S \\text{ if } \\alpha > 1; \\\\[2mm]\n\\quad }%{\\displaystyle\\int g \\ d\\mu_{\\mathbb K} \\cdot \\widetilde V(x)\n& \\text{ for all } x\\in \\S \\text{ if } \\alpha = 1,\n\\end{cases}\n$$\nwhere $\\widetilde V\\in\\CX_{1}$ is a fixed point for $\\CR$, given by \n\\begin{equation}\\label{eq:tildeV}\n\\widetilde V(x) = \n\\begin{cases}\n \\log\\left(}%{\\displaystyle\\frac{\\widetilde\\kappa_{0}(x)+\\frac1\\gamma\\widetilde\\kappa_{1}(x)+\\gamma}{}%{\\displaystyle\\widetilde\\kappa_{0}(x)+\\frac1\\gamma\\widetilde\\kappa_{1}(x)+\\gamma-1}\\right) & \\text{ if } x \\in [0];\\\\[4mm]\n \\log\\left(}%{\\displaystyle\\frac{\\gamma\\widetilde\\kappa_{0}(x)+\\widetilde\\kappa_{1}(x)+\\gamma^{2}}{}%{\\displaystyle\\gamma\\widetilde\\kappa_{0}(x)+\\widetilde\\kappa_{1}(x)+\\gamma^{2}-1}\\right) & \\text{ if } x \\in [1].\n\\end{cases}\n\\end{equation}\n\\end{theorem}\nThis precise expression of $\\widetilde V$ corresponds to a $\\alpha$-density\n$\\tilde g(x) = \\gamma^2\/(2\\gamma-1)$ if $x \\in [0] \\cap K$ and $\\tilde g(x) =\n\\gamma\/(2\\gamma-1)$ if $x \\in [1] \\cap {\\mathbb K}$,\nand $\\int \\widetilde V(x) d\\mu_{\\mathbb K} = 1$.\n\nOur second theorem suggests that renormalization for potentials is a machinery to produce potentials with phase transition. \nWe recall that a {\\em freezing phase transition} is characterized\nby the fact that the pressure is of the form \n$$\n\\CP(\\beta)=a\\beta+b \\quad \\text{ for } \\beta\\ge\\beta_{c}\n$$\nand that the equilibrium state is fixed for $\\beta\\ge\\beta_c$.\nThe word ``freezing'' comes from the fact that in statistical mechanics \n$\\beta$ is the inverse of the temperature (so the temperature \ngoes to $0$ as $\\beta\\to+\\infty$) and that a {\\em ground-state} \nis reached at positive temperature $1\/\\beta_c$, see \\cite{CLT, Dyson}. \n\n\\begin{theorem}\\label{theo-pt}\nAny potential $\\varphi:=-V$ with $V\\in \\CX_{1}$ admits a freezing phase transition at finite $\\beta$: there exists $\\beta_{c}>0$ such that \n\\begin{enumerate}\n\\item for $0\\le \\beta<\\beta_{c}$ the map $\\CP(\\beta)$ is analytic, there exists a unique equilibrium state for $\\beta \\varphi$ and this measure has full support;\n\\item for $\\beta>\\beta_{c}$, $\\CP(\\beta)=0$ and $\\mu_{{\\mathbb K}}$ is the unique \nequilibrium state for $\\beta \\varphi$. \n\\end{enumerate}\n\\end{theorem}\n\nThese two theorems explain a link between substitution, renormalization and phase transition on quasi-crystals: a substitution generates a quasi-crystal but also allows to define a renormalization operator acting on the potentials. This operator has some fixed point, and the stable leaf of that fixed point furnishes a family of potentials with freezing phase transition. \n\n\\subsection{Outline of the paper}\nIn Section~\\ref{sec-H-K-R} we recall and\/or prove various\nproperties of the Fibonacci subshift and its special words.\nWe establish the form of $H^n$ and $\\CR^nV$ for arbitrary $n$ and relate this\nto (special words of) the Fibonacci shift.\nIn Section~\\ref{sec-prooftheofix}, after clarifying the role of accidents on\nthe computation of $\\CR^nV$, we prove Theorem~\\ref{theo-fixedpoint}.\nSection~\\ref{sec-proffthpt} deals with the thermodynamic formalism.\nFollowing the strategy of \\cite{leplaideur-synth} we specify and estimate the required (quite involved) quantities that are the core of the proof of Theorem~\\ref{theo-pt}.\n\n\\section{Some more properties of $H$, ${\\mathbb K}$ and $\\CR$}\\label{sec-H-K-R}\n\\subsection{The set \\boldmath ${\\mathbb K}$ as Sturmian subshift \\unboldmath}\nIn addition to being a substitution subshift, $({\\mathbb K},\\sigma)$ is the Sturmian subshift associated to the golden mean rotation, $T_{\\gamma}:x \\mapsto x+\\gamma \\pmod 1$. The golden mean is\n$\\gamma=\\frac{1+\\sqrt5}2$ and it satisfies $\\gamma^{2}=\\gamma+1$. \n\nFixing an orientation on the circle, \nlet $\\arc{ab}$ denote the arc of points between $a$ and $b$ in the circle \nin that orientation. \nIf we consider the itinerary of $2\\gamma$ under the action of $T_{\\gamma}$ with the code $0$ if the point belongs to $\\arc{0\\gamma}$ and $1$ if the point belongs to $\\arc{\\ga0}$ (see Figure~\\ref{fig-codi-fibo}), we get $\\rho$, the fixed point of the substitution.\n\n\\begin{figure}[htbp]\n\\begin{center}\n\\includegraphics[scale=0.5]{fibocode3.pdf}\n\\caption{Coding for Fibonacci Sturmian subshift.}\n\\label{fig-codi-fibo}\n\\end{center}\n\\end{figure}\n\nThere is an almost ({\\em i.e.,\\ } up to a countable set) one-to-one correspondence between points in ${\\mathbb K}$ and codes of orbits of $(\\SS, T_\\gamma)$,\n expressed by the commutative diagram\n$$\n\\begin{array}{ccc}\\SS & \\stackrel{}%{\\displaystyle T_\\gamma}{\\longrightarrow} & \\SS \\\\\\pi\\downarrow & \\circlearrowleft & \\downarrow\\pi \\\\{\\mathbb K} & \\stackrel{}%{\\displaystyle\\sigma}{\\longrightarrow} & {\\mathbb K}\\end{array}\n$$\nand $\\pi$ is a bijection, except at points $T_\\gamma^{-n}(\\gamma) \\in \\SS$, $n \\ge 0$.\nSince Lebesgue measure is the unique $T_\\gamma$-invariant probability measure,\n$\\mu_{\\mathbb K} := \\text{Leb} \\circ \\pi^{-1}$ is the unique\ninvariant probability measure of $({\\mathbb K},\\sigma)$. \n\nWe will use the same terminology for both ${\\mathbb K}$ and $\\SS$. \nFor instance, a cylinder $C_{n}(x)$ for $x \\in \\SS$ is an interval\\footnote{Some work has to be done to check that it actually is an interval.}, \nwith the convention that $C_{n}(x)=\\pi^{-1}(C_{n}(\\pi(x)))$, and we may confuse \na point $x \\in \\SS$ and its image $\\pi(x) \\in {\\mathbb K}$. \n\n\\begin{definition}\\label{def-words}\nLet $\\CA_{{\\mathbb K}}$ denote the set of finite words that appear in $\\rho$.\n A word $\\omega:=\\omega_{0}\\ldots \\omega_{n-1}\\in \\CA_{{\\mathbb K}}$ is said to be {\\em left-special} if $0w$ and $1w$ both appear in $\\CA_{{\\mathbb K}}$. It is {\\em right-special} if $w0$ and $w1$ both appear in $\\CA_{{\\mathbb K}}$.\nA left and right-special word is called {\\em bi-special}. A {\\em special} word is either left-special or right-special. \n\\end{definition}\n \nSince $\\rho$ has $n+1$ words of length $n$ \n(a characterization of Sturmian words), there is exactly one left-special \nand one right-special word of length $n$.\nThey are of the form $\\rho_{0}\\ldots \\rho_{n-1}$ and $\\rho_{n-1}\\ldots \\rho_{0}$\nrespectively, which can be seen from the forward itinerary\nof $x \\approx \\gamma$ and backward itinerary of $x \\approx 0$ in the circle. \nSometimes the left and right-special word merge into a single bi-special \nword $\\omega$, but only\none of the two words $0\\omega0$, and $1\\omega1$ appears in $\\CA_{{\\mathbb K}}$, see \\cite[Section 1]{arnoux-rauzy}, the construction of $\\Gamma_{n+1}$ from $\\Gamma_{n}$.\n\n\\begin{proposition}\\label{prop-bispecialfibo}\nBi-special words in $\\CA_{{\\mathbb K}}$ are of the form $\\rho_{0}\\ldots \\rho_{F_m-3}$\nand for each $m \\ge 3$, $\\rho_{0}\\ldots \\rho_{F_m-3}$ is bi-special.\n\\end{proposition}\n\nWe prove this proposition at the end of Section~\\ref{subsec:Hn}\n\n\\subsection{Results for \\boldmath $H^{n}$ \\unboldmath}\\label{subsec:Hn}\nWe recall that $\\kappa_{a}(w)$ is the number of symbol $a$ in the finite word $w$. \n\n\\begin{lemma}\\label{lem-lengthHn}\nFor any finite word $w$, the following recursive relations hold:\n\\begin{eqnarray*}\n\\kappa_0(H^n(w)) &=& F_n \\kappa_0(w) + F_{n-1} \\kappa_1(w);\\\\\n\\kappa_1(H^n(w)) &=& F_{n-1} \\kappa_0(w) + F_{n-2} \\kappa_1(w);\\\\\n|H^n(w)| &=& F_{n+1} \\kappa_0(w) + F_n \\kappa_1(w) = |H^{n-1}(w)|+|H^{n-2}(w)|, \n\\end{eqnarray*}\nwhere $|H^{0}(w)|=|w|,\\ |H^{1}(w)|=|H(w)|$.\n\\end{lemma}\n\nSince we have defined $F_{-2} = 1$ and $F_{-1} = 0$, see \\eqref{eq:Fibo},\nthese formulas hold for $n = 0$ and $n=1$ as well.\n\n\\begin{proof}\nSince $H^n(0)$ contains $F_{n+1}$ zeroes and $F_{n-1}$ ones,\nwhile $H^n(0)$ contains $F_{n-1}$ zeroes and $F_{n-2}$ ones,\nthe first two lines follow from concatenation.\nThe third line is the sum of the first two, and naturally\nthe recursive relation follows from the same recursive relation\nfor Fibonacci numbers.\n\\end{proof}\n\nSince $({\\mathbb K}, \\sigma, \\mu_{\\mathbb K})$ is uniquely ergodic, and isomorphic to \n$(\\SS, T_\\gamma, \\text{Leb})$, we immediately get that\n\\begin{equation}\\label{eq:symfreq}\n\\lim_{n \\to +\\infty} \\frac{\\kappa_a(H^n(w))}{ |H^n(w)| } =\n\\begin{cases} \n|\\arc{0\\gamma}| = \\frac1\\gamma & \\text{ if } a = 0, \\\\\n|\\arc{\\ga0}| = 1-\\frac1\\gamma & \\text{ if } a = 1. \n\\end{cases}\n\\end{equation}\n\n\\iffalse\n\\begin{definition}\\label{def-stopblock}\nWe say that $x, y \\in \\S$ coincide up to a {\\em stopping block} if there exists a word $w$ such that \n$$x=w01\\text{ and }y=w10.$$\nWe shall also write that $(x,y)$ has a stopping block, and\/or that $x$ and $y$ have a stopping block. \n\\end{definition}\n\nThe Fibonacci substitution is not of constant length, and this \ncomplicates the calculation how long two points $H^{n}(x)$ and $H^{n}(y)$ coincide if we know how long $x$ and $y$ coincide. \nThe main interest of stopping-block is that it propagates itself under the iterations of $H^{n}$. \n\\fi\n\n\\begin{lemma}\\label{lem-prop-stopbloc}\nAssume that $x$ and $y$ have a maximal common prefix $w$.\nThen $H^{n}(x)$ and $H^{n}(y)$ coincide for $T_{n}(w)+F_{n+2}-2$ digits, where $T_{n}(w)$ is defined by \n\\begin{equation}\\label{eq:Tnw}\nT_{0}= |w|,\\ T_{1}=|H(w)|,\\ T_{n+2}(w) = T_{n+1}(w)+T_{n}(w).\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}\nFor $x = w0\\dots$ and $y = w1\\dots$, we find\n\\begin{align*}\n\\begin{array}{c} w \\\\ w \\\\\\end{array}\n& \\begin{array}{|c|}\\hline 0 \\ {\\hbox{{\\it 1}} \\!\\! I} \\\\\\hline \\end{array}\n\\ \\stackrel{H}{\\longrightarrow}\n\\begin{array}{cc} H(w) & 0\\ \\\\ H(w) & 0\\ \\\\\\end{array}\n\\begin{array}{|c|}\\hline 1 \\\\0 \\\\\\hline \\end{array}\n\\ \\stackrel{H}{\\longrightarrow}\\begin{array}{cccc} H^2(w) & 0 & 1 & 0\\ \\\\ H^2(w) & 0 & 1 & 0\\ \\end{array}\n\\begin{array}{|c|}\\hline 0 \\\\ 1 \\\\\\hline \\end{array} \\\\[3mm]\n& \\stackrel{H}{\\longrightarrow}\n\\begin{array}{ccccccc} H^3(w) & 0 & 1 & 0 & 0 & 1 & 0\\ \\\\\nH^3(w) & 0 & 1 & 0 & 0 & 1 & 0\\ \\end{array}\n\\begin{array}{|c|}\\hline 1 \\\\0 \\\\\\hline \\end{array}\n\\ \\stackrel{H}{\\longrightarrow} \\ \\cdots\n\\end{align*}\nwhere we used that $H(a)$ starts with $0$ for both $a=0$ and $a=1$.\nWe set $T_n(w) = |H^n(w)|$, then the recursive formula \\eqref{eq:Tnw}\nfollows as in Lemma~\\ref{lem-lengthHn}.\n\nIterating $H$ on the words $01$ and $10$, we find:\n\\begin{equation}\\label{eq:Hn0110}\n\\begin{array}{|cc|}\\hline 0 & 1 \\ {\\hbox{{\\it 1}} \\!\\! I} & 0 \\\\\\hline \\end{array}\\stackrel{H}{\\longrightarrow}\\begin{array}{c} 0 \\\\0 \\\\\\end{array}\\begin{array}{|cc|}\\hline 1 & 0 \\\\0 & 1 \\\\\\hline \\end{array}\\stackrel{H}{\\longrightarrow}\\begin{array}{ccc}0 & 1 & 0 \\\\0 & 1 & 0\\end{array}\\begin{array}{|cc|}\\hline 0 & 1 \\ {\\hbox{{\\it 1}} \\!\\! I} & 0 \\\\\\hline \\end{array}\n\\stackrel{H}{\\longrightarrow}\\begin{array}{cccccc}0 & 1 & 0 & 0 & 1 & 0 \\\\0 & 1 & 0 & 0 & 1 & 0\\end{array}\n\\begin{array}{|cc|}\\hline 1 & 0 \\\\0 & 1 \\\\\\hline \\end{array}\\ .\n\\end{equation}\nThus $|H^n(10)| = |H^n(01| = F_{n+2}$ and the common prefix\nof $H^n(10)$ and $H^n(01)$ has length $F_{n+2}-2$ is precisely the same as\nthe common block of $H^n(w0)$ and $H^n(w0)$ between $H^n(w)$ and\nthe first difference.\n\nTherefore, $H^n(x)$ and $H^n(y)$\ncoincide for $T_n(w) + F_{n+2}-2$ digits.\n\\end{proof}\n\n\\begin{corollary}\\label{cor-coinc-hn-rho}\nFor $x \\in {\\mathbb K}$ and $n\\in{\\mathbb N}$, $H^{n}(x)$ and $\\rho$ coincide for at least $F_{n+3}-2$ digits if $x\\in [0]$ and for at least $F_{n+2}-2$ digits if $x\\in[1]$. \n\\end{corollary}\n\n\\begin{proof}\nIf $x \\in [0]$, then, by Lemma~\\ref{lem-prop-stopbloc}, \n$H^n(x)$ coincides with $H^n(\\rho) = \\rho$\nfor at least $T_n(0) + F_{n+2}-2$ digits.\nBut $T_n(0) = |H^n(0)| = F_{n+1}$, so $T_n(0) + F_{n+2}-2 = F_{n+3}-2$.\n\nIf $x \\in [1]$, then $H(x) \\in [0]$ and the previous argument gives that \n$H^n(x)$ coincides with $H^n(\\rho) = \\rho$\nfor at least $F_{n+2}-2$ digits.\n\\end{proof}\n\n\n\\begin{proof}[Proof of Proposition~\\ref{prop-bispecialfibo}]\nWe iterate the blocks $0\\cdot01$, $0\\cdot10$ and $1\\cdot01$ under $H$:\\\\\n$$\n\\begin{array}{|cccc|}\\hline 0 & \\cdot & 0 & 1 \\\\ 0 & \\cdot & 1 & 0 \\\\ 1 & \\cdot & 0 & 1 \\\\ \\hline \\end{array}\n\\stackrel{H}{\\longrightarrow}\n\\begin{array}{|cc|}\\hline 0 & 1 \\\\ 0 & 1 \\\\ & 0 \\\\ \\hline \\end{array}\\\n\\begin{array}{c} 0 \\\\0 \\\\ 0 \\end{array}\\\n\\begin{array}{|cc|}\\hline 1 & 0 \\\\ 0 & 1 \\\\ 1 & 0 \\\\ \\hline \n\\end{array}\\stackrel{H}{\\longrightarrow}\n\\begin{array}{|ccc|}\\hline \\dots\\!\\!\\! & 1 & 0 \\\\ \\dots\\!\\!\\! & 1 & 0 \\\\ & 0 & 1 \\\\ \\hline \\end{array}\\\n\\begin{array}{ccc} 0 & 1 & 0 \\\\ 0 & 1 & 0 \\\\ 0 & 1 & 0 \\end{array}\\\n\\begin{array}{|cc|}\n\\hline 1 & 0 \\\\ 0 & 1 \\\\ 1 & 0 \\\\ \\hline \n\\end{array}\n\\stackrel{H}{\\longrightarrow} \\cdots\\ , \\\\\n$$\nso the common central block here is bi-special, and it is the same \nas the common block $v$ of $H^n(01)$ and $H^n(10)$ \nof length $F_{n+2}-2$ in the proof of Lemma~\\ref{lem-prop-stopbloc}.\nThus we have found the bi-special word of length $F_{n+2}-2$, and every\nprefix and suffix of $v$ is left and right-special respectively.\nThe fact that these are the only bi-special words can be derived from \nthe Rauzy graph for this Sturmian shift, see\n{\\em e.g.\\ } \\cite[Sec. 1]{arnoux-rauzy}.\nIn their notation, there is a bi-special word of length $k$\nif the two special nodes in the Rauzy graph coincide: $D_k = G_k$.\nThe lengths of the two ``buckles'' of non-special nodes between $D_k = G_k$\nare two consecutive Fibonacci numbers minus one, as follows from the \ncontinued fraction expansion \n$$\n\\gamma=1+\\frac1{1+\\frac1{1+\\ddots}}.\n$$\nTherefore, the complexity satisfies\n$$\nk+1 = p(k) = \\#\\{ \\text{nodes of Rauzy graph of order } k\\}\n= F_n-1 + F_{n-1}-1 + 1,\n$$\nso indeed only the numbers $k = F_{n+1}-2$ can be the lengths of bi-special \nwords.\n\\end{proof}\n\n\n\\subsection{Iterations of the renormalization operator}\n\nThe renormalization operator for potentials can be rewritten under as \n(recall the definition of $F_{n}^{a}$, $a=0,1$, from \\eqref{eq:Fiboa})\n\\begin{equation}\\label{equ-def-cr}\n\\CR V|_{[a]} = \\sum_{j=0}^{F^{a}_{1}-1} V \\circ \\sigma^j \\circ H|_{[a]}.\n\\end{equation}\nThis general formula may be extended to other substitutions and leads to an expression for $\\CR^{n}V$. The main result here is Lemma~\\ref{lem:RkV}, where we show that\n\\begin{equation}\\label{eq:RnV}\n(\\CR^n V)(x) = \\sum_{j=0}^{F_{n^{*}}-1} V \\circ \\sigma^j \\circ H^n(x),\n\\end{equation}\nwhere\n\\begin{equation}\\label{eq:n*}\nn^* = \\begin{cases}\nn+1 & \\text{ if } x \\in [0],\\\\\nn & \\text{ if } x \\in [1].\n\\end{cases}\n\\end{equation}\nThe substitution $H$ solves a renormalization equation of the form \\eqref{equ1-renorm}. If $x=0x_{1}\\ldots$, then $H(x)=01H(x_{1})\\ldots$ and $\\sigma^{2}\\circ H(x)=H\\circ \\sigma(x)$. If $x=1x_{1}\\ldots$ then we simply have \n$\\sigma\\circ H(x)=H\\circ \\sigma(x)$. The renormalization equation is thus more complicated than for the constant length case. We need an expression for iterations of $H$ and $\\sigma$. \n\\begin{lemma}\\label{lem:commute_shift_H}\nGiven $k \\ge 0$ and $a=0,1$, let \n$w = w_1w_2\\dots w_{F^{a}_{k}} = H^k(a)$.\nThen for every $0 \\le i < F^{a}_{k}$ we have\n$$\nH \\circ \\sigma^i \\circ H^k|_{[a]} = \\sigma^{| H(w_1\\dots w_i)|} \\circ H^{k+1}|_{[a]}.\n$$\n\\end{lemma}\n\\begin{proof}\nFor $k = 0$ this is true by default and for $k= 1$, this is precisely\nwhat is done in the paragraph before the lemma.\nLet us continue by induction, assuming that the statement is true for $k$.\nThen $\\sigma^i$ removes the first $i$ symbols of $w = H^k(a)$,\nwhich otherwise, under $H$, would be extended to a word of\nlength $|H(w_1\\dots w_i)|$. We need this number of shifts\nto remove $H(w_1\\dots w_i)$ from $H([w]) = H^{k+1}([a])$.\n\\end{proof}\n\n\n\\begin{lemma}\\label{lem:RkV}\nFor every $k \\ge 0$ and $a=0,1$, we have\n$$\n\\CR^kV|_{[a]} = S_{F^{a}_{k}}V \\circ H^k|_{[a]},\n$$\nwhere $S_nV = \\sum_{i=0}^{n-1} V \\circ \\sigma^i$ denotes the $n$-th ergodic sum.\n\\end{lemma}\n\n\\begin{proof}\nFor $k = 0$ this is true by default. For $k = 1$, this follows by the definition of the renormalization operator $\\CR$.\nLet us continue by induction, assuming that the statement is true for $k$.\nWrite $w = H^k(a)$ and $t_i = |H(w_i)| = F_{w_i}$.\nThen\n\\begin{eqnarray*}\n\\CR^{k+1}V|_{[a]} &=& (\\CR V) \\circ S_{F^a_k}V \\circ H^k|_{[a]} \\hskip 1cm \\text{\\small (Induction assumption)}\\\\\n &=& \\sum_{i=0}^{F^{a}_{k}-1} \\left( \\sum_{j=0}^{t_i-1} V \\circ \\sigma^j \\circ H \\right) \\sigma^i\\circ H^k|_{[a]}\\hskip 1cm \\text{\\small (by formula \\eqref{equ-def-cr})}\\\\\n &=& \\sum_{i=0}^{F^{a}_{k}-1} \\left( \\sum_{j=0}^{t_i-1} V \\circ \\sigma^{j+|H(w_1\\dots w_i)|} \\circ H \\right) \\circ H^k|_{[a]} \\hskip 1cm\\text{\\small (by Lemma~\\ref{lem:commute_shift_H})}\\\\\n &=& \\sum_{l=0}^{F^{a}_{k+1}-1} V \\circ \\sigma^l \\circ H^{k+1}|_{[a]}, \n\\end{eqnarray*}\nas required.\n\\end{proof}\n\n\n\n\n\n\\subsection{Special words are sources of accidents}\nOverlaps of $\\rho$ with itself are strongly related to bi-special words. They are of prime importance to determine the fixed points of $\\CR$ and their \nstable leaves, see {\\em e.g.\\ } formula \\eqref{equ-crkV} below. Dynamically, they correspond to what we call {\\em accident} in the time-evolution of the distance between the orbit and ${\\mathbb K}$. \n\n\n\n\nFor most $x$ close to ${\\mathbb K}$, $d(\\sigma(x),{\\mathbb K}) = 2d(x,{\\mathbb K})$, but\nthe variation of $d(\\sigma^{j}(x),{\\mathbb K})$ is not always monotone with respect to $j$. When it decreases, it generates an accident:\n\\begin{definition}\\label{def-accident}\nLet $x\\in\\S$ and $d(x,{\\mathbb K})=2^{-n}$. If $d(\\sigma(x),{\\mathbb K})\\le 2^{-n}$, we say that we have an {\\em accident} at $\\sigma(x)$. \nIf there is an accident at $\\sigma^{j}(x)$, then we shall simply say we have an accident at $j$. \n\\end{definition}\n\nThe next lemma allows us to detect accidents. \n\\begin{lemma}\\label{lem-accident-bispecial}\nLet $x=x_{0}x_{1}\\ldots$ coincide with some $y\\in{\\mathbb K}$ for $d$ digits. Assume that the first accident occurs at $b$. Then $x_{b}\\ldots x_{d-1}$ is a bi-special word in $\\CA_{{\\mathbb K}}$. Moreover, the word $x_{0}\\ldots x_{d-1}$ is not right-special. \n\\end{lemma}\n\\begin{proof}\nBy definition of accident, there exists $y$ and $y'$ in ${\\mathbb K}$ such that $d(x,{\\mathbb K})=d(x,y)$ and $d(\\sigma^{b}(x),{\\mathbb K})=d(\\sigma^{b}(x),y')$. \n\n\\begin{figure}[htbp]\n\\begin{center}\n\\includegraphics[scale=0.7]{accident.pdf}\n\\caption{Accident and bi-special words}\n\\label{fig-accident}\n\\end{center}\n\\end{figure}\nFigure~\\ref{fig-accident} shows that the word $x_{b}\\ldots x_{d-1}$ is bi-special because its two extensions $y$ and $y'$ in ${\\mathbb K}$ \nhave different suffix and prefix for this word. \n\nIt remains to prove that $x_{0}\\ldots x_{d-1}$ is not right-special. If it was, then $x_{0}\\ldots x_{d-1}x_{d}=y_{0}\\ldots y_{d-1}\\overline{y_{d}}$ \nwould a ${\\mathbb K}$-admissible word, thus $d(x,{\\mathbb K})\\le 2^{-(d+1)}\\neq 2^{-d}$. \n\\end{proof}\n\n\n\\section{Proof of Theorem~\\ref{theo-fixedpoint}}\\label{sec-prooftheofix}\n\\subsection{Control of the accidents under iterations of $\\CR$}\nNext we compute $\\CR^{n}V$ and show that accidents do not crucially\nperturb the Birkhoff sum involved. This will follow from Corollaries~\\ref{coro-dHnK} and \\ref{cor-coinc-hn-rho}.\n\nNote that Lemma~\\ref{lem-prop-stopbloc} shows that $H$ is one-to-one. The next proposition explains the relation between the attractor ${\\mathbb K}$ and its image by $H$. \n\n\\begin{proposition}\\label{prop-K-H}\nThe subshift ${\\mathbb K}$ is contained in $H({\\mathbb K}) \\cup \\sigma\\circ H({\\mathbb K})$. More precisely, if $[0]\\cap {\\mathbb K} \\subset H({\\mathbb K})$ and $x\\in[1]\\cap {\\mathbb K} \\subset \\sigma\\circ H({\\mathbb K})$. \n\\end{proposition}\n\n\\begin{proof}\nFirst note that Lemma~\\ref{lem-prop-stopbloc} shows that $H$ is one-to-one. We also recall that the word $11$ is forbidden in ${\\mathbb K}$. Hence, each digit $1$\nin $x=x_{0}x_{1}x_{2}\\ldots\\in {\\mathbb K}$ is followed and preceded by a digit $0$ (unless the $1$ is in first position). \n\nAssuming $x_{0}=0$, we can unique split $x$ into blocks of the form \n$0$ and $01$.\nIn this splitting, we replace \neach single $0$ by $1$ and each pair $01$ by $0$. \nThis produces a new word, say $y$, and by construction, $H(y)=x$. \nThis operation is denoted by $H^{-1}$. \nIt can be used on finite words too, provided that the last digit is $1$. \nIf $x_{0}=1$, we repeat the above construction with $0x$, and $x=\\sigma\\circ H(y)$. \n\nIt remains to prove that $y\\in{\\mathbb K}$. \nFor every $x\\in{\\mathbb K}$, there is a sequence $k_{n}\\to\\infty$ \nsuch that $\\sigma^{k_{n}}(\\rho)\\to x$. Assume again that $x_0 = 0$.\nThen we can find a sequence $l_n \\sim k_n\/\\gamma$ such that \n$H \\circ \\sigma^{l_n}(\\rho)=\\sigma^{k_{n}}(\\rho)$.\nTherefore $\\lim_n \\sigma^{l_n}(\\rho) \\in {\\mathbb K}$, and this limit is indeed the sequence\n$y$ that satisfies $H(y) = x$.\nFinally, for $x_0 = 1$, we repeat the argument with $0x$.\n\\end{proof}\n\n\n\\begin{corollary}\\label{coro-dHnK}\nIf $d(x,{\\mathbb K})=d(x,y)$ with $y\\in {\\mathbb K}$, then $d(H^{n}(x),{\\mathbb K})=d(H^{n}(x),H^{n}(y))$ for $n\\ge 0$.\n\\end{corollary}\n\\begin{proof}\nWrite $x=wa$ and $y=w\\overline a$ where $a$ is an unknown digit and $\\overline a$ its opposite. Note that $H^{n}(x)$ starts with $0$ for any $n\\ge 1$.\nAssume that there is some $z\\in {\\mathbb K}$ such that $d(H(x),z) n+1$.\n\n\n\n\nHence $\\rho_{0}\\ldots\\rho_{F_{n+2}-1}$ can be written as $BBB'$ where $B$ is the suffix of $\\rho$ of length $j$ and $B'$ is a suffix of $\\rho$ of length $\\ge |B|\/\\gamma$.\nClearly $B$ starts with $0$. We can split it uniquely into blocks $0$ and $01$, and $B$ fits an integer number of such blocks, because if\nthe final block would overlap with the second appearance of $B$, then $B$ \nwould start with $1$, which it does not.\n\nTherefore we can perform an inverse substitution $H^{-1}$, for each block $B$ and also for $B'$ because we cal globally do $H^{-1}$ for $\\rho_{0}\\ldots\\rho_{F_{n+2}-1}$. We find $H^{-1}(BBB') = CCC'$ which has the same characteristics.\nRepeating this inverse iteration, we find that $\\rho$ starts with $0101$, or with $00$, \na contradiction.\n\\end{proof}\n\nLet $N(x,n)$ be the integer such that $2^{-N(x,n)} = d(H^{n}(x),{\\mathbb K})$.\nBy the previous lemma $d(\\sigma^{j}(H^{n}(x)){\\mathbb K})=2^{-(N(x,n)-j)}$\nfor every $j 0$ and \na sequence of $z_{n}$ such that for every $n$, \n$}%{\\displaystyle |\\frac1{F_{n^{*}}}\\sum_{j=0}^{F_{n^{*}}}\\frac{g\\circ \\sigma^{j}(z_{n})}{X_{n}-\\frac{j}{F_{n^{*}}}} - \\widetilde V(x)\\int g\\,d\\mu_{{\\mathbb K}}| > \\varepsilon$ \nfor every $n$. \nThen any accumulation point $\\mu_{\\infty}$ of the family of measures \n$$\n\\mu_{n}:=\\frac1{F_{n^{*}}}\\sum_{j=0}^{F_{n^{*}}}\\frac{1}{X_{n}-\\frac{j}{F_{n^{*}}}}\\delta_{\\sigma^{j}(z_{n})}\n$$\nis $\\sigma$-invariant (because $F_{n^{*}}\\to+\\infty$), supported on ${\\mathbb K}$, and \n$\\int g\\,d\\mu_{\\infty}\\neq \\int g\\,d\\mu_{{\\mathbb K}}$. This would contradict the unique ergodicity for $({\\mathbb K}, \\sigma)$. \n\nTherefore, the convergence in \\eqref{equ-cv-toepli} is uniform in $z$ and this shows that \n$$\n\\frac1{F_{n^{*}}}\\sum_{j=0}^{F_{n^{*}}}\\frac{g\\circ \\sigma^{j}(H^{n}(y))}{X_{n}-\\frac{j}{F_{n^{*}}}} \\to \\widetilde V(x) \\cdot \\int g\\,d\\mu_{{\\mathbb K}}.\n$$ \nThis finishes the proof of Theorem~\\ref{theo-fixedpoint}.\n\n\n\\section{Proof of Theorem~\\ref{theo-pt}}\\label{sec-proffthpt}\n\n\\subsection{The case \\boldmath $-\\log \\frac{n+1}{n}$ \\unboldmath}\n\\label{subsec-logcase}\nWe first consider the potential $\\varphi(x) = -\\log \\frac{n+1}{n}$ if $d(x, {\\mathbb K}) = 2^{-n}$, leaving the general potential in $\\CX_1$ for later.\n\n\\subsubsection{Strategy, local equilibria}\nFix some cylinder $J$ such that the associated word, say $\\omega_{J}$, does not appear in $\\rho$ (as {\\em e.g.\\ } 11). We follow the induction method presented in \\cite{leplaideur-synth}. Let $\\tau$ be the first return time into $J$ (possibly $\\tau(x)=+\\infty$), and consider the family of transfer operators \n\\begin{eqnarray*}\n\\CL_{Z,\\beta}:\\psi&\\mapsto& \\CL_{Z,\\beta}(\\psi)\\\\\nx&\\mapsto& \\CL_{Z,\\beta}(\\psi)(x):=\\sum_{n=1}^{+\\infty}\\sum_{\\stackrel{y\\in J\\ \\tau(y)=n}{ \\sigma^{n}(y)=x}}e^{\\beta \\cdot (S_{n}\\varphi)(y)-nZ}\\psi(y),\n\\end{eqnarray*}\nwhich acts on the set of continuous functions $\\psi:J\\to{\\mathbb R}$. \nFollowing \\cite[Proposition 1]{leplaideur-synth}, for each $\\beta$ there exists $Z_{c}(\\beta)$ such that $\\CL_{Z,\\beta}$ is well defined for every $Z>Z_{c}(\\beta)$. \nBy \\cite[Theorem 1]{leplaideur-synth}, $Z_{c}(\\beta)\\ge 0$ because the pressure of the dotted system \n(which in the terminology of \\cite{leplaideur-synth} is the system restricted to the trajectories that avoid $J$)\nis larger (or equal) than the pressure of ${\\mathbb K}$ which is zero. \n\nWe shall prove\n\\begin{proposition}\n\\label{prop-spectral&zc}\nThere exists $\\beta_{0}$ such that $\\CL_{0,\\beta}({1\\!\\!1}_{J})(x)<1$ for every $\\beta>\\beta_{0}$ and $x\\in J$.\n\\end{proposition}\nWe claim that if Proposition~\\ref{prop-spectral&zc} holds, then\n\\cite[Theorem 4]{leplaideur-synth} proves that $\\CP(\\beta)=0$ for every $\\beta>\\beta_{0}$, and $\\mu_{{\\mathbb K}}$ is the unique equilibrium state for $\\beta \\varphi$. \n\nTo summarize \\cite{leplaideur-synth} (and adapt it to our context), the \npressure function satisfies (see Figure~\\ref{fig-graphes-press}\\footnote{We will see that $\\CL_{0,\\beta}({1\\!\\!1}_{J})(x)$ is a constant function on $J$.}),\n$$\nZ_{c}(\\beta) \\le \\CP(\\beta) \\le \\max(\\log(\\CL_{0,\\beta}({1\\!\\!1}_{J})),0).\n$$\n\nAs long as $\\CP(\\beta)>0$, there is a unique equilibrium state and it has full support. In particular this shows that the construction does not depend on the choice of $J$. If Proposition~\\ref{prop-spectral&zc} holds, then \n$$Z_{c}(\\beta)=\\CP(\\beta)=\\max(\\log(\\CL_{0,\\beta}({1\\!\\!1}_{J})),0)=0,\n\\text{ for } \\beta>\\beta_{c}$$\nand $\\mu_{{\\mathbb K}}$ is the unique equilibrium state because $\\CL_{0,\\beta}({1\\!\\!1}_{J})<1$. \n\n\\begin{figure}[htbp]\n\\begin{center}\n\\includegraphics[scale=.5]{graphe1na.pdf}\n\\caption{The Pressure between $Z_{c}(\\beta)$ and $\\log\\lambda_{0,\\beta}:=\\CL_{0,\\beta}({1\\!\\!1}_{J})$}\n\\label{fig-graphes-press}\n\\end{center}\n\\end{figure}\n\n\\subsubsection{Proof of Proposition~\\ref{prop-spectral&zc}-Step 1}\\label{subsubsec-upper=series}\nWe reduce the problem to the computation of a series depending on $\\beta$. \nNote that $\\varphi(x)$ only depends on the distance from $x$ to ${\\mathbb K}$. This shows that if $x, x' \\in J$ and $y, y' \\in J$ are such that \n$$y=\\omega x\\ , \\quad \u00a0y'=\\omega x',$$\nwith $\\omega\\in \\{0,1\\}^{n}$, $\\tau(y)=\\tau(y')=n$, then \n$$(S_{n}\\varphi)(y)=(S_{n}\\varphi)(y').$$\nIn other words, $\\CL_{Z,\\beta}({1\\!\\!1}_{J})$ is a constant function, and then equal to the spectral radius $\\lambda_{Z,\\beta}$ of $\\CL_{Z,\\beta}$. \n\nConsequently, to compute $\\lambda_{Z,\\beta}$, it suffices to compute the sum of all \n$}%{\\displaystyle e^{\\beta \\cdot (S_{n}\\varphi)(\\omega)-nZ}$, where $\\omega$ is a word of length $n+|\\omega_{J}|$, starting and finishing as $\\omega_{J}$. Such a word $\\omega$ can also be seen as a path of length $n$ starting from $J$ and returning (for the first time) to $J$ at time $n$. \n\nWe split such a path in several sub-paths. We fix an integer $N$ and say that the path is {\\em free} at time $k$ if $\\omega_{k}\\ldots \\omega_{n-1}\\omega_{J}$ is at distance larger than $2^{-N}$ to ${\\mathbb K}$. Otherwise, we say that we have an {\\em excursion}. The path is thus split into intervals of free moments and excursions. \nWe assume that $N$ is chosen so large that $0$ is a free moment. This also shows that for every $k\\le n$, $d(\\sigma^{k}(\\omega\\omega_{J}),{\\mathbb K})$ is determined by $\\omega_{k}\\ldots \\omega_{n-1}$. \n\nIf $k$ is a free time, $}%{\\displaystyle \\varphi(\\sigma^{k}(\\omega\\omega_{J}))\\le A_{N}:=-\\log\\left(1+\\frac 1N\\right)$. Denote by $k_{0}$ the maximal integer such that \n$k$ is a free time for every $k\\le k_{0}$. Then $S_{k_{0}+1}\\varphi\\le (k_{0}+1)A_{N}$ and there are fewer than $2^{k_{0}+1}$ such prefixes of length $k_0+1$. \n\nNow, assume that every $j$ for $k_{0}+1\\le j\\le k_{0}+k_{1}$ is an excursion time, and assume that $k_{1}$ is the maximal integer with this property. To the contribution $(S_{k_{0}+1}\\varphi)(\\omega\\omega_{J})$ we must add the contribution $(S_{k_{1}}\\varphi)(\\sigma^{k_{0}+1}(\\omega\\omega_{J}))$ of the excursion. \nThen we have a new interval of free times, and so on. \nThis means that we can compute $\\CL_{0,\\beta}({1\\!\\!1}_{J})$ by gluing together paths with the same decompositions of free times and excursion times. If we denote by $C_{E}$ the total contribution of all paths with exactly one excursion (and only starting at the beginning of the excursion), then we have \n\\begin{equation}\n\\label{equ1-upperboundCL0}\n\\lambda_{0,\\beta}=\\CL_{0,\\beta}({1\\!\\!1}_{J}) \\le \\sum_{k=1}^{+\\infty}\\left(\\sum_{k_{0}=0}^{+\\infty}e^{(k_{0}+1)(\\beta A_{N}+\\log2)}\\right)^{k+1}C_{E}^k.\n\\end{equation}\nThe sum in $k$ accounts for $k+1$ intervals of free moment with $k$ intervals of excursions times between them. The sum in $k_{0}$ accounts for the possible length $k_{0}+1$ for an interval of free times. \nThese events are maybe not independent but the sum in \\eqref{equ1-upperboundCL0} includes all paths, possible or not, and therefore yields an upper bound. \n\nThe integer $N$ is fixed, and we can take $\\beta$ so large that $\\beta A_{N}<-\\log2$. This shows that the sum in $k_{0}$ in \\eqref{equ1-upperboundCL0} converges and is as close to $0$ as we \nwant if $\\beta$ is taken sufficiently large. \n\nTo prove Proposition~\\ref{prop-spectral&zc}, it is thus sufficient to prove that $C_{E}$ can be made as small as we want if $\\beta$ increases. \n\n\\subsubsection{Proof of Proposition~\\ref{prop-spectral&zc}-Step 2}\\label{subsubsec-splitCE}\nWe split excursions according to their number of accidents, see Definition~\\ref{def-accident}. \nLet $x$ be a point at a beginning of an excursion. \n \nLet $B_{0}:=0=b_{0}$, $B_1 := b_1>b_{0},\\ B_2 := b_1+b_2>b_{1},\\ \nB_3 := b_1+b_2+b_3, \\dots ,\nB_M := b_1+b_2+ \\dots + b_M$\nbe the times of accidents in the excursion.\nThere is $y_0 \\in {\\mathbb K}$ such that $x$ shadows $y_0$ at the beginning\nof the excursion, say for $d_0$ iterates.\nLet $y_i \\in {\\mathbb K}$, $i = 1, \\dots, M$, be the points that $x$ starts to shadow \nat the $i$-th accident, for $d_i$ iterates.\n\n\\begin{figure}[htbp]\n\\begin{center}\n\\includegraphics[scale=0.5]{excursion.pdf}\n\\caption{Accidents during an excursion.}\n\\label{fig-excursion}\n\\end{center}\n\\end{figure}\n\nThen by Lemma~\\ref{lem-accident-bispecial}, $x_{b_{i+1}}\\ldots x_{d_{i}}$ is bi-special and by Proposition~\\ref{prop-bispecialfibo}, $d_i-b_{i+1} = F_{n_{i+1}}-2$ for some $n_{i+1}$. \n\n\\begin{remark}\\label{rem-yispe}\nWe emphasize that the first $d_i$ entries of $y_i$\ndo not form a special word. Indeed, it is neither right-special (due to\nLemma~\\ref{lem-accident-bispecial}) nor left-special, because otherwise there would be an accident earlier. \n\\hfill $\\blacksquare$\\end{remark}\n\nIf there are $M+1$ accidents (counting the first as 0), the ergodic sums for $\\varphi$ are \n\\begin{eqnarray*}\n(S_{b_{i+1}}\\varphi)(\\sigma^{B_{i}} (x) ) &=& \n \\sum_{k=0}^{b_{i+1}-1}\\varphi \\circ \\sigma^{B_{i}+k} (x)\\\\\n &=& \n \\sum_{k=0}^{b_{i+1}-1} -\\log \\frac{d_{i}+1-k}{d_{i}-k} \\\\\n&=& -\\log \\frac{d_{i}+1}{d_{i}+1-b_{i+1}} = -\\log(1+ \\frac{b_{i+1}}{d_i+1-b_{i+1}}),\n\\end{eqnarray*}\nfor $0\\le i\\le M-1$, while the ergodic sum of the tail of the excursion is\n\n\\begin{equation}\n\\label{equ-estiEM+1}\n(S_{d_M}\\varphi)(\\sigma^{B_M} (x)) = \n\\sum_{k=0}^{d_M-1} \\varphi \\circ \\sigma^{B_M+k}( x) =\n-\\log \\frac{d_M+1}{N+1}.\n\\end{equation}\n\nWe set $}%{\\displaystyle \\mathbf{ e}_{i} := e^{\\beta \\cdot (S_{b_{i}}\\varphi)(\\sigma^{B_{i-1}} (x) )}$ for $i=1\\ldots M$ and $}%{\\displaystyle \\mathbf{ e}_{M+1} := e^{\\beta \\cdot (S_{d_M}\\varphi)(\\sigma^{B_M} (x))}$. \nComputing $C_{E}$, we can order excursions according to their number of accidents ($M+1$) and then according to the contribution of each accident. Let $E_{i}$ stand for the total contribution of all possible $\\mathbf{ e}_{i}$'s \nbetween accidents $i-1$ and $i$. \nThen\n\\begin{equation}\\label{equ-defCE}\nC_{E}=\\sum_{M=0}^{+\\infty}\\prod_{i=1}^{M+1}E_{i}.\n\\end{equation}\n\n\\subsubsection{Proof of Proposition~\\ref{prop-spectral&zc}-Step 3}\\label{subsubsec-computCE}\nLet us now find an upper bound for $E_{i}$. \nBy definition, $E_{i}$ is the sum over the possible $d_{i-1}$ and $b_{i}$ of $\\mathbf{ e}_{i}$. \n\nRecall $d_{i-1}-b_{i}=F_{n_{i}}-2$, so $b_{i}$ and $F_{n_{i}}$\ndetermine $d_{i-1}$. \nThe key idea is that $F_{n_{i}}$ and $F_{n_{i+1}}$ determine the possible values of $b_{i}$. \nThis implies that $E_{i}$ can be written as an expression over the $F_{n_{i}}$ and $F_{n_{i+1}}$. \n\n\\medskip\n$\\bullet$ For $2\\le i\\le M$ each $\\mathbf{ e}_{i}$ depends on $F_{n_{i}}$ and $b_{i}$. \nLet us show that for $2\\le i\\le M$, $b_{i}$ depends on $n_{i}$ and $n_{i-1}$. \nIndeed, the sequence $y_{i} \\in {\\mathbb K}$ coincides for\n$F_{n_{i}}-2$ initial symbols with $\\rho$, and from entry $b_{i+1}$ has another\n$d_{i} - b_{i+1} = F_{n_{i+1}}-2$ symbols in common with the head of $\\rho$,\nbut differs from $x_{B_{i}+ d_i}$ at entry $d_i$, see Figure~\\ref{fig-excursion}.\nThus we need to find all the values of $d_i > F_{n_i}-2$ such that\n$\\rho_0 \\dots \\rho_{d_i-1}$ ends the bi-special word $\\rho_0 \\dots \\rho_{F_{n_{i+1}-3}}$ but is itself not bi-special.\nThe possible starting positions of this appearance\nof $\\rho_0 \\dots \\rho_{F_{n_{i+1}-3}}$ are the required numbers $b_{i+1}$.\n\n\\begin{lemma}\\label{lem:bij}\nLet us denote by $b_{i+1}(j)$, $j \\ge 1$, the $j$-th value that\n $b_{i+1}$ can assume. Then \n\\begin{equation}\\label{equ-estibj}\nb_{i+1}(j)\\ge\\max(F_{n_{i}}-F_{n_{i+1}},F_{n_{i}-1})+j F_{n_{i+1}-2}.\n\\end{equation}\n\\end{lemma}\n\nThis will allow us to find an upper bound for $E_{i}$ for $1\\le i\\le M-1$\nlater in this section. \n\n\\begin{proof}\nWe abbreviate the bi-special words $L_k = \\rho_0 \\dots \\rho_{F_k-3}$\nfor $k \\ge 4$. \nFor the smallest value $d_i \\ge F_{n_i}-2$ so that\n$\\rho_0 \\dots \\rho_{d_i-1}$ ends in (but is not identical to) a block $L_{n_{i+1}}$,\nthis block starts at entry:\n$$\nb_{i+1}(0) =\n\\begin{cases}\nF_{n_i}-F_{n_{i+1}} &\\text{ if } n_{i+1} < n_i \\text{ and } n_i-n_{i+1} \\text{ is even,}\\\\\nF_{n_i}-F_{n_{i+1}-1} &\\text{ if } n_{i+1} < n_i \\text{ and } n_i-n_{i+1} \\text{ is odd,}\\\\\nF_{n_{i+1}-1} &\\text{ if } n_{i+1} \\ge n_i.\n\\end{cases}\n$$\nHowever, if $n_{i+1} < n_i$ then $d_i = F_{n_i}-2$ and if \n $n_{i+1} \\ge n_i$ then $d_i = F_{n_{i+1}+1}-2$ in this case, and thus\n$\\rho_0 \\dots \\rho_{d_i-1}$ is right-special, contradicting\nLemma~\\ref{lem-accident-bispecial}.\nTherefore we need to wait for the next appearance of $L_{n_{i+1}}$.\nFor the Rauzy graph of the Fibonacci shift, the bi-special word $L_k$\nis the single node connecting loops of length $F_{k-1}$ and $F_{k-2}$,\nsee \\cite[Section 1]{arnoux-rauzy}. Therefore the gap between two \nappearances of $L_k$ is always $F_{k-2}$ or $F_{k-1}$.\nThis gives $b_{i+1}(j+1) \\ge b_{i+1}(j) + F_{n_{i+1}-2}$ for all $j \\ge 0$\nand \\eqref{equ-estibj} follows.\n\\end{proof}\n\n$\\bullet$ For $i=1$, formula \\eqref{equ-estibj} can be applied, if we introduce the quantity $n_{0}$, coinciding with the overlap of the end of the previous \n``fictitious'' word, say $y_{-1}$. The point is that $y_{0}$ is the ``beginning'' of the excursion, thus the first accident. Then, $F_{n_{0}}\\le N$ and $F_{n_{1}}>N$, which yields $n_{0} 1$, we obtain\n\\begin{eqnarray*}\nE_{i} &=& \\sum_{j \\ge 1} \ne^{}%{\\displaystyle-\\beta \\log\\left(1 + \\frac{ \\max(F_{n_{i}}-F_{n_{i+1}},F_{n_{i}-1}) + j F_{n_{i+1}-2}}{F_{n_{i+1}}-1}\\right)} \\\\\n&\\simeq& \\sum_{j \\ge 1} \\left(1+\\max(\\gamma^{n_{i} - n_{i+1}}-1,\\gamma^{n_{i}-n_{i+1}-1} )+ j\/\\gamma^2\\right)^{-\\beta}\\\\\n&\\le& \\frac{\\gamma^2}{\\beta-1} \\left(1+\\max(\\gamma^{n_{i} - n_{i+1}}-1,\\gamma^{n_{i}-n_{i+1}-1} )\\right)^{1-\\beta}.\n\\end{eqnarray*} \nfor $2\\le i\\le M$\n\nLet $P\\approx }%{\\displaystyle \\frac{\\log\\frac{N}{\\sqrt5}}{\\log\\gamma}$ be the largest integer $n$ such that $F_{n}\\le N$. Then \\eqref{equ-defCE} yields \n\\begin{align}\\label{eq:est0}\n\\nonumber C_{E}\\le & \\sum_{M=0}^{+\\infty}\\left(\\frac{\\gamma^2}{\\beta-1}\\right)^{M}\\frac{(N+1)}{\\beta-1} \\ \\cdot \\\\\n& \\sum_{\\stackrel{n_{1},\\ldots n_{M}> P}{n_{0}\\le P}} E_{1}\\left(1+\\max(\\gamma^{n_{i} - n_{i+1}}-1,\\gamma^{n_{i}-n_{i+1}-1} )\\right)^{1-\\beta}\\gamma^{(P-n_{M})(\\beta-1)}.\n\\end{align}\n\n\n\\subsubsection{Proof of Proposition~\\ref{prop-spectral&zc}-Step 4}\\label{subsubsec-CEsmall}\nWe show that $C_{E}\\to0$ as $\\beta\\to+\\infty$. \n\n\n\\begin{proposition}\n\\label{prop-Cezero}\nThere exists $A=A(\\beta)\\in (0,1)$ with $\\lim_{\\beta\\to+\\infty} A = 0$ such that \n$$\nC_{E}\\le 2P\\, \\frac{N+1}{\\beta-1}\\sum_{n=1}^{+\\infty}\\gamma^{-n(\\beta-1)}\\sum_{M=0}^{+\\infty}A^{M}\\sum_{i=0}^{M}\\frac{n^{i}}{i!}.\n$$\n\\end{proposition}\n\nBefore proving this proposition, we show that is finishes the proof of Proposition~\\ref{prop-spectral&zc}. \nThe series has only positive terms. Clearly, $}%{\\displaystyle \\sum_{M=0}^{+\\infty}A^{M}\\sum_{i=0}^{M}\\frac{n^{i}}{i!}\\le \\frac1{1-A}e^{n}$, so the main sum converges if\n$\\gamma^{\\beta-1}>e$. Thus Proposition~\\ref{prop-Cezero} implies that $C_{E}\\to 0$ as $\\beta\\to+\\infty$. \n\nTherefore, inequality \\eqref{equ1-upperboundCL0} shows that if $\\beta\\to+\\infty$, \nthen $\\lambda_{0,\\beta}\\to0$ too, and hence Proposition~\\ref{prop-spectral&zc} is proved. \n\nThe rest of this subsection is then devoted to the proof of Proposition~\\ref{prop-Cezero}. \n\\begin{lemma}\n\\label{lem-gamma-n}\nLet $\\eta$ and $y$ be positive real numbers. Then for every $n$, \n$$\\int_{y}^{\\infty}x^{n}e^{-\\eta (x-y)}dx=\\sum_{j=0}^{n}\\frac{n!}{j!}\\frac{y^{j}}{\\eta^{n+1-j}}.$$\n\\end{lemma}\n\\begin{proof}\nSet $u_{n}:=}%{\\displaystyle \\int_{y}^{\\infty}x^{n}e^{-\\eta (x-y)}dx$. \nThen\n\\begin{eqnarray*}\nu_{n}&=& \\int_{0}^{\\infty}(x+y)^{n}e^{-\\eta x}dx\\\\\n&=&\\left[\\frac{-1}{\\eta}(x+y)^{n}e^{-\\eta x}\\right]_{0}^{\\infty}+\\frac{n}\\eta\\int_{0}^{\\infty}(x+y)^{n-1}e^{-\\eta x}\\\\\n&=& \\frac{y^{n}}\\eta+\\frac{n}\\eta u_{n-1}.\n\\end{eqnarray*}\nThe formula follows by induction.\n\\end{proof}\n\n\nLet $n$ be some positive integer and $\\xi$ and $\\zeta$ two positive real numbers. We consider a matrix $D_{n} = (d_{n,i,j})_{i=1, j=1}^{n+1, n}$ with $n+1$ rows and $n$ columns defined by \n$$\nd_{n,i,j}:= \\begin{cases}\n \\frac{(j-1)!}{(i-1)!}\\zeta^{j-i+1} & \\text{ if } i \\le j,\\\\[1mm]\n \\frac\\xi{j} & \\text{ if }i=j+1,\\\\[1mm]\n 0 &\\text{ if }i>j+1.\n\\end{cases}\n$$\nor in other words:\n$$\nD_n=\\left(\\begin{array}{cc cc ccc}\n0!\\zeta & 1!\\zeta^2 & 2!\\zeta^3 & \\ldots & (j-1)!\\zeta^j & \\ldots & (n-1)!\\zeta^{n} \\\\\n\\xi & \\zeta & \\ldots & & & & (n-2)!\\zeta^{n-1} \\\\\n0 & \\frac\\xi2 & \\zeta & & & & \\vdots \\\\\n0 & 0 & \\frac\\xi3 & \\ddots & \\frac{(j-1)!}{(i-1)!}\\zeta^{j-i+1} & & \\vdots\\\\\n\\vdots & & 0 & \\ddots & \\ddots & & \\vdots\\\\\n\\vdots & & & 0 & \\frac{\\xi}{j} & \\zeta & \\zeta^2 \\\\\n0 & & & & 0 & \\frac{\\xi}{n-1} & \\zeta \\\\\n0 & 0 & \\ldots & \\ldots & 0 & 0 & \\frac{\\xi}{n} \n\\end{array}\\right).\n$$\nWe call $\\mathbf{ w}$ non-negative (and write $\\mathbf{ w}\\succeq 0$) if all a entries of $\\mathbf{ w}$ are non-negative. This defines a partial ordering on vectors by \n$$\\mathbf{ w}' \\succeq \\mathbf{ w} \\iff \\mathbf{ w}'-\\mathbf{ w}\\succeq 0.$$ \n\n\\begin{lemma}\n\\label{lem-matrix}\nAssume $0<\\zeta<1$ and \nset $K:=\\frac1{1-\\zeta}$. Then, for every $n$,\n$$\nD_n \\cdot \\left(\\begin{array}{c}}%{\\displaystyle\\frac{K^{n-1}}{0!} \\\\[1mm]\n}%{\\displaystyle\\frac{K^{n-1}}{1!} \\\\[1mm]\n}%{\\displaystyle\\frac{K^{n-1}}{2!} \\\\[1mm]\n\\vdots \\\\[1mm]\n}%{\\displaystyle\\frac{K^{n-1}}{(n-1)!}\\end{array}\\right)\n\\preceq\n\\left(\\begin{array}{c}}%{\\displaystyle\\frac{K^{n}}{0!} \\\\[1mm]\n}%{\\displaystyle\\frac{K^{n}}{1!} \\\\[1mm]\n}%{\\displaystyle\\frac{K^{n}}{2!} \\\\[1mm]\n\\vdots \\\\[1mm]\n}%{\\displaystyle\\frac{K^{n}}{n!}\\end{array}\\right).$$\n\\end{lemma}\n\n\\begin{proof}\nThis is just a computation. For the first row we get \n$$\n\\sum_{j=1}^{n}(j-1)!\\zeta^{j}.\\frac{K^{n-1}}{(j-1)!}\\le K^{n-1}.\\frac\\zeta{1-\\zeta}\\le K^{n}.$$\nFor row $i>1$ we get \n$$\\frac1{(i-1)}\\frac{K^{n-1}}{(i-2)!}+\\sum_{j=i}^{n}\\frac{(j-1)!}{(i-1)!}\\zeta^{j-i+1}\\frac{K^{n-1}}{(j-1)!}=\\frac{K^{n-1}}{(i-1)!}\\left(1+\\zeta+\\zeta^{2}\\ldots\\right)\\le \\frac{K^{n}}{(i-1)!}.$$\n\\end{proof}\n\n\n\n\\begin{proposition}\n\\label{prop-calcul-matrix-majo}\nSet $\\zeta:=}%{\\displaystyle\\frac1{(\\beta-1)\\log\\gamma}$ and $K=\\frac{1}{1-\\zeta}$. \nConsider $M$ integers $n_{1},\\ldots n_{M}$, with $n_{M}> P$. Then, \nfor every $M\\ge 2$, \n$$\n\\sum_{n_1, \\dots, n_{M-1} > P} \\prod_{i=1}^{M-1} \\left(1+\\max(\\gamma^{n_{i} - n_{i+1}}-1,\\gamma^{n_{i}-n_{i+1}-1} ) \\right)^{1-\\beta}\\le K^{M-1}\\sum_{i=0}^{M-1}\\frac{(n_{M}-P)^{i}}{i!}$$\n\\end{proposition}\n\\begin{proof}\nNote that \n\\begin{align*}\n\\sum_{n_1, \\dots, n_{M-1} > P} & \\prod_{i=1}^M \\left(1+\\max(\\gamma^{n_{i} - n_{i+1}}-1,\\gamma^{n_{i}-n_{i+1}-1} )\\right)^{1-\\beta}\\\\\n= & \\sum_{n_{M-1}=1}^{\\infty}\\left(\\ldots\\left(\\sum_{n_{2}=1}^{\\infty}\\left(\\sum_{n_{1}=1}^{\\infty}\\right.\\right.\\right.\n\\left(1+\\max(\\gamma^{n_{1} - n_{2}}-1,\\gamma^{n_{1}-n_{2}-1} ))^{1-\\beta}\\right)\\cdot\\\\\n& \\left.\\left((1+\\max(\\gamma^{n_{2} - n_{3}}-1,\\gamma^{n_{2}-n_{3}-1} ))^{1-\\beta}\\right)\\ldots\\right) \\\\\n& \\left(1+\\max(\\gamma^{n_{M-1} - n_{M}}-1,\\gamma^{n_{M-1}-n_{M}-1} \\right)^{1-\\beta}.\n\\end{align*}\nThis means that we can proceed by induction. Now \n\\begin{align*}\n\\sum_{n_{1}=P+1}^{\\infty} & (1+\\max(\\gamma^{n_{1} - n_{2}}-1,\\gamma^{n_{1}-n_{2}-1} ))^{1-\\beta}\\\\\n&\\le\\int_{P}^{n_{2}} (1+\\gamma^{x-n_{2}-1} ))^{1-\\beta}dx\n+\\int_{n_{2}}^{\\infty} (\\gamma^{x - n_{2}})^{1-\\beta}dx\\\\\n&\\le n_{2}-P+\\int_{n_{2}}^{\\infty}e^{-(\\beta-1)\\,(x-n_{2})\\,\\log\\gamma}\\,dx\\\\\n&= n_{2}-P+\\int_{n_{2}}^{\\infty}e^{-\\frac{x-n_{2}}{\\zeta}}\\,dx,\n\\end{align*}\nbecause $\\zeta=\\frac1{(\\beta-1)\\log\\gamma}$. This shows that the result holds for $M=2$.\n\nAssuming that the sum for $M=p$ is of the form \n$\\sum_{j=0}^{p-1}a_{j}(n_{p}-P)^{j}$, we compute the sum for $M=p+1$.\n\\begin{align*}\n\\sum_{n_{p}=P+1}^{\\infty} & \\sum_{j=0}^{p-1} a_{j}\\frac{(n_{p}-P)^{j}}{(1+\\max(\\gamma^{n_{p}-n_{p+1}}-1,\\gamma^{n_{p}-n_{p+1}-1} ))^{\\beta-1}} \\\\ \n&\\le\\ \\sum_{j}a_{j}\\int_{P}^{n_{p+1}}\\frac{(x-P)^{j}}{(1+\\gamma^{x-n_{p+1}-1})^{\\beta-1}}\\,dx+\n\\sum_{j}a_{j}\\int_{n_{p+1}}^{\\infty}\\frac{(x-P)^{j}}{(\\gamma^{x-n_{p+1}})^{\\beta-1}}\\,dx\\\\\n&\\le \\sum_{j}\\frac{a_{j}(n_{p+1}-P)^{j+1}}{(j+1)}+\\int_{n_{p+1}}^{\\infty}(x-P)^{j}e^{-\\frac{x-n_{p+1}}{\\zeta}}\\,dx.\n\\end{align*}\nSet $}%{\\displaystyle \\mathbf{ w}\\cdot\\mathbf{ w}'=\\sum w_{i}w'_{i},$\nfor vectors $\\mathbf{ w}=(w_{1},\\ldots, w_{p+1})$ and $\\mathbf{ w}'=(w'_{1},\\ldots, w'_{p+1})$. \nLemma~\\ref{lem-gamma-n} yields \n \\begin{align*}\n\\sum_{n_{p}=P+1}^{\\infty} & \\sum_{j=0}^{p-1} a_{j}\\frac{(n_{p}-P)^{j}}{(1+\\max(\\gamma^{n_{p}-n_{p+1}}-1,\\gamma^{n_{p}-n_{p+1}-1}))^{\\beta-1}}\\\\\n&\\le \\sum_{j}\\frac{a_{j}}{(j+1)}(n_{p+1}-P)^{j+1} + \n\\sum_{i=0}^{j}\\frac{j!}{i!}\\zeta^{j-i+1}(n_{p+1}-P)^{i}\\\\\n&\\le D_{p}\\left(\\begin{array}{c}a_0 \\\\a_1 \\\\ \\vdots \\\\ a_{p-1}\n\\end{array}\\right) \\cdot \n\\left(\\begin{array}{c}1 \\\\n_{p+1} \\\\\\vdots \\\\n_{p+1}^{p}\\end{array}\\right).\n\\end{align*}\nLemma~\\ref{lem-matrix} concludes the proof of the induction. \n\\end{proof}\n\n\\begin{proof}[Proof of Proposition~\\ref{prop-Cezero}]\nWe have just proven that \n\\begin{align*}\n\\sum_{n_{1},\\ldots n_{M}>P} & \\left(1+\\max(\\gamma^{n_{i} - n_{i+1}}-1,\\gamma^{n_{i}-n_{i+1}-1} )\\right)^{1-\\beta}\\gamma^{(P-n_{M})(\\beta-1)} \\\\\n& \\le K^{M-1}\\sum_{n_{M}=P+1}^{+\\infty}\\sum_{j=0}^{M-1}\\frac{(n_{M}-P)^{j}}{j!}\\gamma^{(n_{M}-P)(\\beta-1)}.\n\\end{align*}\n\nIt remains to sum over $n_{0}$. Note that in that case, there are only $P$ terms of the form \n$}%{\\displaystyle \\sum_{j=0}^{+\\infty}\\frac1{\\left(1+\\gamma^{n_{0}-n_{1}-2}+\\frac{j}{\\gamma}\\right)^{\\beta}}$ because $n_{0}\\le P\\beta_{0}$. \nThis also shows that $\\CP(\\beta)=0$ for $\\beta>\\beta_{0}$. \nSince $\\CP(\\beta)$ is a continuous and convex function, it is constant for \n$\\beta>\\beta_{0}$. As $\\CP(0)=\\log2$, there exists \na minimal $\\beta_{c} > 0$ such that $\\CP(\\beta)>0$ for every $0\\le \\beta<\\beta_{c}$. \nClearly, $\\beta_{c}\\le \\beta_{0}$. \n\nWe claim that for $\\beta<\\beta_{c}$, there exists a unique equilibrium state and that it has full support. Indeed, there exists at least one equilibrium state, say $\\mu_{\\beta}$, and at least one cylinder, say $J$, has positive $\\mu_{\\beta}$-measure. \nTherefore, we can induce on this cylinder, and the form of potential (see \\cite[Theorem 4]{leplaideur-synth}) shows that there exists a unique local equilibrium state. It is a local Gibbs measure and therefore $\\mu_{\\beta}$ is uniquely determined on each cylinder, and unique and with full support (due to the mixing property). \n\nWe claim that the pressure function $\\CP(\\beta)$ is analytic on $[0,\\beta_{c}]$. \nIndeed, each cylinder $J$ has positive $\\mu_{\\beta}$-measure and the associated $Z_{c}(\\beta)$ is the pressure of the dotted system (that is: restricted to the trajectories that avoid $J$). This set of trajectories has a pressure strictly smaller than $\\CP(\\beta)$ because otherwise, several equilibrium states would coexist. \nTherefore $\\CP(\\beta)$ is determined by the implicit equation $\\lambda_{\\CP(\\beta),\\beta}=1$ and $\\CP(\\beta)>Z_{c}(\\beta)$ for $\\beta \\in [0,\\beta_{c}]$. \nThe Implicit Function Theorem shows that $\\CP(\\beta)$ is analytic. \n \nFor $\\beta\\ge \\beta_{c}$, the pressure $\\CP(\\beta)=0$ and for cylinders $J$ as above, we have $Z_{c}(\\beta)\\ge 0$. This shows that $Z_{c}(\\beta)=0$ for every $\\beta\\ge \\beta_{c}$. Due to the form of the potential, $\\lambda_{0,\\beta}$ is continuous and decreasing in $\\beta$. \n\nWe claim that $\\beta_{c}=\\beta_{0}$. \nIndeed, assume by contradiction $\\beta<\\beta_{c}$. Then $\\lambda_{0,\\beta_{c}}>1$, since otherwise (because $\\lambda_{0,\\beta}$ being strictly decreasing in $\\beta$), \n$\\lambda_{0,\\beta_{c}}\\le 1$ would yield that $\\lambda_{0,\\beta}<1$ for every $\\beta>\\beta_{c}$.\nThis would imply $\\beta_{c} \\ge \\beta_{0}$ (recall that $\\beta_{0}$ is minimal with this property). \nNow, for fixed $\\beta$, $Z\\mapsto \\lambda_{Z,\\beta}$ is continuous and strictly decreasing and goes to $0$ at $Z\\to+\\infty$. Therefore, if $\\lambda_{0,\\beta_{c}}>1$ then there exists $Z>0$ such that $\\lambda_{Z,\\beta_{c}}=1$. The local equilibrium state for this $Z$ generates\n some $\\sigma$-invariant probability measure\\footnote{Since $Z_{c}(\\beta_{c})=\\CP(\\beta_{c})=00$ such that \n$$-V \\le \\kappa\\varphi.$$\n This shows that the pressure function is constant equal to zero for $\\beta\\ge \\beta_{0}\/\\kappa$. Again, the pressure is convex, thus non-increasing and continuous. We can define $\\beta'_{c}$ such that $\\CP(\\beta)>0$ for$0\\le\\beta\\le \\beta'_{c}$ and $\\CP(\\beta)=0$ for $\\beta\\ge \\beta'_{c}$. \n \n The rest of the argument is relatively similar to the previous discussion. We deduce that for $\\beta<\\beta'_{c}$, there exists a unique equilibrium state, it has full support and $\\CP(\\beta)$ is analytic on this interval. For $\\beta\\ge \\beta'_{c}$, it is not clear that $\\lambda_{0,\\beta}$ decreases in $\\beta$. However,\nwe do not really need this argument, because if $\\lambda_{0,\\beta}>1$, then the decrease of $Z\\mapsto \\lambda_{Z,\\beta}$ (which follows from convexity argument and $}%{\\displaystyle\\lim_{Z\\to+\\infty}\\lambda_{Z,\\beta}=0)$, is sufficient to produce a contradiction. \n\n\n\n\\bibliographystyle{plain}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\n\n\n\n\n\n\nThe main paradigm for adapting pretrained models for classification~\\cite{GPT, UniLM, BERT} is fine-tuning via an explicit classifier head. However, an alternative approach has arisen: adapting the pretrained language model directly as a predictor through autoregressive text generation~\\cite{GPT2} or completion of a cloze task~\\cite{ASimpleMethod}. This method is notably used in T5 fine-tuning~\\cite{T5} leading to state-of-the-art results on the SuperGLUE benchmark~\\cite{SuperGLUE}.\n\n\\blfootnote{Code available at \\url{https:\/\/github.com\/TevenLeScao\/pet}}\n\n\nOne argument made for classification by direct language generation is that it allows us to pick custom \\textit{prompts} for each task~\\cite{Decathlon}. \nWhile this approach can be used for zero-shot classification~\\cite{Zeroshot} or priming~\\cite{GPT3}, it can also be used in fine-tuning to provide extra task information to the classifier, especially in the low-data regime~\\cite{PET, PET2}.\n\nIf this argument is indeed true, it is natural to ask how it impacts the sample efficiency of the model, or more directly, \\textit{how many data points is a prompt worth?} As with many low-data and pretraining-based problems, this \nquestion is complicated by the fine-tuning setup, training procedure, and prompts themselves. We attempt to isolate these variables through diverse prompts, multiple runs, and best practices in low-training data fine-tuning. We introduce a metric, the \\textit{average data advantage}, for quantifying the impact of a prompt in practice.\n\n\nOur experiments find that the impact of task-targeted prompting can nicely be quantified in terms of direct training data, and that it varies over the nature of different tasks. On MNLI~\\cite{MNLI}, we find that using a prompt contributes approximately 3500 data points. On SuperGLUE, it adds approximately 280 data points on RTE~\\cite{RTE} and up to 750 on BoolQ~\\cite{BoolQ}. In low- to medium-data settings, this advantage can be a real contribution to training a model. \n\n\n\\section{Related Work}\n\n\nPrompting has been used both for zero-shot and fine-tuning based methods. Zero-shot approaches attempt to answer a task with a prompt without fine-tuning through generation~\\cite{GPT2}. GPT3~\\cite{GPT3} extends this approach to a supervised priming method by taking in training data as priming at inference time, so it can attend to them while answering. T5~\\cite{T5} and other sequence-to-sequence pretrained models use standard word-based fine-tuning with a marker prompt to answer classification tasks with strong empirical success. Our setting differs in that we are interested in using task-based prompts and fine-tuning, in-between the T5 and GPT2 setting. \n\nOur setting most closely resembles PET~\\cite{PET, PET2}, which claims that task-specific prompting helps transfer learning, especially in the low-data regime. However, in order to reach the best possible results on SuperGLUE, PET introduces several other extensions: semi-supervision via additional pseudo-labeled data, ensembling models trained with several different prompts, and finally distilling the ensemble into a linear classifier rather than a language model. Our aim is to isolate the specific contributions of prompting within supervised fine-tuning. \n\nFinally, recent papers have experimented with discovering prompts through automated processes tailored to the language model~\\cite{HowCanWeKnow, AutomaticVerbalizer}. We limit ourselves to human-written prompts, as we are interested into \nwhether prompting itself specifically adds information to the supervised task. It is an interesting question as to whether automatic prompts can have this same impact (relative to the training data they require). \n\n\\section{Comparison: Heads vs Prompts}\n\n\n\nConsider two transfer learning settings for text classification: \\textit{head-based}, where a generic head \nlayer takes in pretrained representations to predict an output class; \\textit{prompt-based}, where a \ntask-specific pattern string is designed to coax the model into producing a textual output corresponding to \na given class. Both can be utilized for fine-tuning with supervised training data, but prompts further allow\nthe user to customize patterns to help the model. \n\nFor the \\textit{prompt} model we follow the notation from PET~\\cite{PET} and decompose a prompt into a \\textit{pattern} and a \\textit{verbalizer}. The \\textit{pattern} turns the input text into a cloze task, i.e. a sequence with a masked token or tokens that need to be filled. Let us use as example an excerpt from SuperGLUE task BoolQ~\\cite{BoolQ}, in which the model must answer yes-or-no binary questions. In order to let a language model answer the question in \\textit{italics}, our pattern is in \\textbf{bold}~\\cite{PET2}:\n\n\\begin{quote}\n\\small\n \"Posthumous marriage -- Posthumous marriage (or necrogamy) is a marriage in which one of the participating members is deceased. It is legal in France and similar forms are practiced in Sudan and China. Since World War I, France has had hundreds of requests each year, of which many have been accepted.\n\\textbf{Based on the previous passage, \\textit{can u marry a dead person in france ?} }\"\n\\end{quote}\n\n\n\nThe masked word prediction is mapped to a \\textit{verbalizer} which produces a class. (here \"Yes\": True. \"No\": False\\footnote{The correct answer here is, of course, \\textit{yes}. Originated in 1803 as Napoleon rose to power, this practice was mainly to the benefit of war widows.}). \nSeveral \\textit{pattern-verbalizer pairs} (\\textit{PVPs}) could be used for a single task, differing either through the pattern, the verbalizer, or both. Fine-tuning is done by training the model to produce the correct verbalization. The loss is the cross-entropy loss between the correct answer and the distribution of probabilities amongst the tokens in the verbalizer. We re-use pattern choices from~\\citet{PET2}; examples are available in Appendix~\\ref{prompts}.\n\n\n\n\n\n\n\n\\section{Experimental Setting}\n\n\nWe run all experiments with the same pretrained checkpoint, \\textit{roberta-large} (355M parameters) from RoBERTa~\\cite{Roberta}, which we load from the \\textit{transformers}~\\cite{Transformers} library.\\footnote{After experimenting with RoBERTa, AlBERT~\\cite{Albert} and BERT~\\cite{BERT}, we found \\textit{roberta-large} to have the most consistent performance.}\nIn line with previous observations~\\cite{Feather,Finetuning,Mixout}, head-based fine-tuning performance varies considerably. We follow recommendations of~\\citet{Stability} and~\\citet{Revisiting} to train at a low learning rate ($10^{-5}$) for a large number of steps (always at least $250$, possibly for over 100 epochs).\n\n\n\n\n\n\n\\begin{figure*}[h!]\n\\centering\n\\hspace*{-1cm}\\includegraphics[width=1.1\\textwidth]{Graphs\/Master_figure.png}\n\\caption{Prompting vs head (classifier) performance across data scales, up to the full dataset, for six SuperGLUE tasks. Compares the best prompt and head performance at each level of training data across 4 runs. Highlighted region shows the accuracy difference of the models. Cross-hatch region highlights the lowest- and highest- accuracy matched region in the curves. The highlighted area in this region is used to estimate the data advantage. }\n\\label{main_figure}\n\\end{figure*}\n\nWe perform our evaluation on SuperGLUE and MNLI~\\cite{MNLI}. These datasets comprise a variety of tasks, all in English, including entailment (MNLI, RTE~\\cite{RTE}, CB~\\cite{CB}), multiple choice question answering (BoolQ~\\cite{BoolQ}, MultiRC~\\cite{MultiRC}), and common-sense reasoning (WSC~\\cite{WSC}, COPA~\\cite{COPA}, WiC~\\cite{WiC}). We do not include ReCoRD~\\cite{ReCORD} in our comparisons as there is no head model to compare with, since it is already a cloze task. Data sizes range from $250$ data points for CB to $392,702$ for MNLI. As test data is not publicly available for SuperGLUE tasks, we set aside part of training (from $50$ for CB, COPA and MultiRC to $500$ for BoolQ) to use for development, and evaluate on their original validation sets. For MNLI, we use the available matched validation and test sets. \n\nWe compare models across a scale of available data, starting with $10$ data points and increasing exponentially (as high-data performance tends to saturate) to the full dataset. For example, for MultiRC, which has 969 data points initially, we start by reserving 50 data points for development. This leaves us with 919 training points, and we train models with 10, 15, 20, 32, 50, 70, 100, 150, 200, 320, 500, 750, and 919 training points. We run every experiment 4 times in order to reduce variance, for a total of 1892 training runs across all tasks. At every point, we report the best performance that has been achieved at that amount of data or lower. Full graphs are presented in Appendix~\\ref{reduction}.\n\n\n\n\\section{Results}\n\n\n\n\n\nFigure~\\ref{main_figure} shows the main results comparing head- and prompt-based fine-tuning with the best-performing pattern on that task. \nPrompting enjoys a substantial advantage on every task, except for WiC as is reported in previous results~\\cite{PET2}.\nBoth approaches improve with more training data, but prompting remains better by a varying amount. Many tasks in SuperGLUE have relatively few data points, but we also see an advantage in large datasets like BoolQ and MNLI.\n\nTo quantify how many data points the prompt is worth, we first isolate the $y$-axis band of the lowest- and highest- accuracy where the two curves match in accuracy.\\footnote{We assume asymptotically the two curves would match, but are limited by data.} The horizontal line at these points represents the advantage of prompting. We then take the integral in this region, i.e. area between the linearly-interpolated curves\\footnote{In areas where the head model is better, if any, get subtracted from the total.}, divided by the height of the band. The area has the dimension of a quantity of data points times the metric unit, so dividing by the performance range yields a \\# of data points advantage. \nAs low data training is sensitive to noise, in addition to following best training practices we \nrun several different experiments for each $x$-point. We use a bootstrapping approach to estimate confidence over these runs. Specifically, we hold out one of the 4 head runs and 4 prompt runs (16 combinations total), and compute the standard deviation of those outcomes.\n\n\nWe report these quantities for every task in Table~\\ref{main_table} as \\textit{Average advantage}. For almost all the tasks, we see that prompting gives a substantial advantage in terms of data efficiency, adding the equivalent of hundreds of data points on average.\n\n\n\n\n\n\n\n\\begin{table*\n\n\\hspace*{-0.7cm}\\begin{tabular}{@{}l rrrrrrrr@{}}\n\\toprule \n& \\multicolumn{8}{c}{Average Advantage (\\# Training Points)} \\\\\n& MNLI & BoolQ & CB & COPA & MultiRC* & RTE & WiC & WSC\\\\\\midrule \\multicolumn{1}{l}{\\textit{P vs H}} & $3506\\pm536$ & $752\\pm46$ & $90\\pm2$ & $288\\pm242$ & $384\\pm378$ & $282\\pm34$ & $-424\\pm74$ & $281\\pm137$ \\\\\n\\hline\n\\multicolumn{1}{l}{\\textit{P vs N}} & $150\\pm252$ & $299\\pm81$ & $78\\pm2$ & -& $74\\pm56\\phantom{0}$ & $404\\pm68$ & $-354\\pm166$ & -\\\\\n\\multicolumn{1}{l}{\\textit{N vs H}} & $3355\\pm612$ & $453\\pm90$ & $12\\pm1$ & -& $309\\pm320$ & $-122\\pm62$ & $-70\\pm160$ & -\\\\\n\\bottomrule\n\\end{tabular}\n\n\\caption{Average prompting advantage in number of data points for MNLI \\& SuperGLUE tasks. \\textit{P} denotes the prompt model, \\textit{H} the head model. On average across performance levels, an MNLI prompt model yields the results of an MNLI head model trained with 3500 additional data points. Confidence levels are based on a multiple random runs (see text). \\textit{N} indicates a null-verbalizer prompting task that replaces the verbalizer with a non-sensical mapping. *The comparison band of MultiRC is too small as the head baseline fails to learn beyond majority class; we use the full region for a lower-bound result.}\n\\label{main_table}\n\\end{table*}\n\n\n\n\n\n\\section{Analysis}\n\n\\paragraph{Impact of Pattern vs Verbalizer}\n\nThe intuition of prompts is that they introduce a task description in natural language,\neven with few training points. \nTo better understand the zero-shot versus adaptive nature of prompts,\nwe consider a \\textit{null verbalizer}, a control with a verbalizer that cannot yield semantic information without training. For every task that requires filling in one word (which excludes the more free-form COPA and WSC), we replace the verbalizers, for example, \"yes\", \"no\", \"maybe\", \"right\" or \"wrong\", with random first names.\n\nTable~\\ref{main_table} shows the advantage of the standard prompt over the null verbalizer to estimate this control.\nWe see that for small data tasks such as CB, the null verbalizer removes much of the benefits of prompting. However, with more training data, the model seems to adapt the verbalizer while still gaining the inductive bias benefits of the pattern. Figure~\\ref{neutral_run_figure} showcases this dynamic on MNLI. This result further shows that prompting yields data efficiency even if it is not directly analogous to the generation process of training. \n\n\n\\begin{figure}\n\\centering\n\\includegraphics[width=0.8\\columnwidth]{Graphs\/MNLI_partial_neutral_run.png}\n\\caption{Comparison of full prompt and null verbalizer advantage on MNLI at lower data scales.}\n\\label{neutral_run_figure}\n\\end{figure}\n\n\\paragraph{Impact of Different Prompts}\n\nIf the prompt acts as a description of the task, one would expect different valid descriptions to vary in their benefits. In order to compare the different prompts we used on each task, we chart the median performance for each of them under different runs. In nearly every experiment, we find that the confidence intervals of those curves largely overlap, implying that prompt choice is not a dominant hyperparameter, i.e. the variance across random seeds usually outweighs the possible benefits of prompt choice. One exception is the low-data regime of BoolQ, where one of the prompts enjoys a significant few-shot advantage over the others. We plot this curve for MultiRC in Figure~\\ref{median_comparison} and the rest in Appendix~\\ref{all_results}.\n\n\\begin{figure}\n\\centering\n\\includegraphics[width=0.8\\columnwidth]{Graphs\/median_f1_multirc.png}\n\\caption{Median performance on MultiRC across runs for three prompts. Differences are inconsistent and eclipsed by the variance within one prompt's runs.}\n\\label{median_comparison}\n\\end{figure}\n\n\\paragraph{Metric sensitivity}\n\n\nWe treat each metric linearly in calculating advantage; alternatively, we could re-parameterize the $y$ axis for each task. This choice does not have a consistent effect for or against prompting. For example, emphasizing gains close to convergence increases prompting advantage on CB and MNLI but decreases it on COPA or BoolQ. \n\n\n\n\n\n\\section{Conclusion}\n\nWe investigate prompting through a systematic study of its data advantage. Across tasks, prompting consistently yields a varying improvement throughout the training process. Analysis shows that prompting is mostly robust to pattern choice, and can even learn without an informative verbalizer. On large datasets, prompting is similarly helpful in terms of data points, although they are less beneficial in performance. In future work, we hope to study the mechanism and training dynamics of the prompting benefits.\n\n\\section{Impact statement}\n\nSignificant compute resources were used to run this paper's experiments. A single experiment (defined as one model run, at one data level, on one task) was quite light-weight, taking usually a little under an hour on a single Nvidia V100. However, as we computed a little under two thousand runs, this adds up to about 1800 GPU hours, to which one must add around 400 GPU hours of prototyping and hyper-parameter searching. Those 2200 GPU hours would usually have necessitated the release of about 400kg of CO2, about 40\\% of a transatlantic flight for a single passenger, in the country where we ran the experiments, although we used a carbon-neutral cloud compute provider.\n\nThe main benefit of prompting, rather than compute efficiency, is data efficiency. Although we ran all of our experiments on English, we hope that this property will be especially helpful in low-resource language applications. In a sense, a practitioner could then remedy the lack of task-specific data in their language by introducing information through a prompt. However, this comes with the inherent risk of introducing human biases into the model. Prompt completion also suffers from biases already present within the language model~\\cite{Babysitter}. This could cause a prompted model to repeat those biases in classification, especially in the few-shot setting where prompting mostly relies on the pretrained model.\n\n\\section{Acknowledgments}\n\nWe thank Steven Cao and Joe Davison for the discussions about prompting that initially spurred this paper. We further thank Timo Schick for making the code for PET available and for discussions about performance replication. We lastly thank Canwen Xu, Yacine Jernite, Victor Sanh, Dimitri Lozeve and Antoine Ogier for their help with the figures and writing of this draft.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nThe propagation and acceleration\nof cosmic rays (CRs) are governed by their interactions\nwith magnetic fields. Astrophysical magnetic fields are turbulent and, \ntherefore, the resonant and non-resonant (e.g. transient time damping, or TTD)\ninteraction of cosmic rays with MHD turbulence is the accepted\n principal mechanism to scatter and isotropize\ncosmic rays \\citep[see][]{Schlickeiser02}. In addition, efficient scattering is essential for the acceleration of cosmic rays. \nFor instance, scattering of cosmic rays back into the shock is a\nvital component of the first order Fermi acceleration \\citep[see][]{Longairbook}. At the same time, stochastic acceleration by turbulence is \nentirely based on scattering. The dynamics of cosmic rays in MHD turbulence holds the key to all high energy astrophysics and related problems. \n\nWe live in an exciting era when we are starting to test fundamental processes taking place at the beginning of the Universe, at the event horizon of black holes, when the nature of dark matter and dark energy is being probed etc. Using computers many researchers make sophisticated complex models to confront the observations in unprecedented details. In the mean time, with the launching of the new facilities, we have much more observational data available than ever before. For instance, CHANDRA observations of supernova\nremnants provide a strong constraint to diffusion coefficients and\/or magnetic fields near the shock \\citep[see, e.g.][]{Bamba05, PYL05}; \nthe diffuse\ngamma-ray measurements from Fermi from the Galactic disc have been successfully used to\nphenomenologically constrain numerical modeling of cosmic rays, e.g., with GALPROP \\citep{Ackermann12}; observations of solar\nenergetic particles (SEP) have been also fruitful over the past decades and lead to better understanding of transport in the solar\nwind \\citep[see a review by][and references therein]{Horbury05_SEP}. These developments make it urgent that we understand the key physical processes underlying astrophysical phenomena, can parameterize them and, if necessary, use as a subgrid input in our computer models.\n\n\nAt present, the propagation of the CRs is an advanced theory, which makes\nuse both of analytical studies and numerical simulations. However,\nthese advances have been done within the turbulence paradigm which\nis being changed by the current research in the field.\nInstead of the empirical 2D+slab model of turbulence, numerical\nsimulations suggest anisotropic Alfv\\'enic modes following \\cite[GS95]{GS95} scalings (an analog of 2D, but not an\nexact one, as the anisotropy changes with the scale involved) + fast modes \\citep{CL02_PRL}. These progresses resulted in important revisions on the theory of cosmic ray transport (see review by \\citealt{LBYO} and references therein). The GS95 turbulence injected on large scales and its extensions to compressible medium is less efficient in scattering of CRs compared to the estimates made assuming that magnetic turbulence consists of plane waves moving parallel to magnetic field \\citep{Chandran00, YL02}. Fast compressible modes, on the other hand, are demonstrated as the dominant scattering agent in spite of various damping processes they are subjected to \\citep{YL02, YL04, YL08}\n\nAt the same time, one should not disregard the possibilities of generation of additional perturbations on small scales by CR themselves. For instance, the slab Alfv\\'enic perturbation can be created, e.g., via streaming instability \\citep[see][]{Wentzel74, Cesarsky80}. Instabilities induced by anisotropic distribution of CRs were also suggested as a possibility to scatter CRs \\citep[]{Lerche, Melrose74}. Particularly at shock front, studies of instabilities have been one of the major efforts since the acceleration efficiency is essentially determined by the confinement at the shock front and magnetic field amplifications. Examples of the new developments in the field include, current driven instability \\citep{Bell2004}, vorticity generation at curved shock \\citep{Giac_Jok2007}, through Baroclinic effect \\citep{Inoue09}, through precursor \\citep{BJL09}, etc. This field is rich in its own and we shall not dwell upon it in this chapter.\n\nIn fact, the small scale instabilities and large scale turbulence are not independent of each other. {\\em First} of all, the instability generated waves can be damped through nonlinear interaction with the large scale turbulence \\citep[henceforth YL02, YL04]{YL02, YL04}. In the case of anisotropic GS95 turbulence, the efficiency is reduced \\citep{FG04}. Nonetheless, owing to the non-linear damping, the instabilities can only grow in a limited range, e.g., $\\sim< 100$GeV in interstellar medium for the streaming instability \\citep{FG04, YL04}. \n{\\em Secondly}, the large scale compressible turbulence also generate small scale waves through firehose, gyroresonance instability, etc \\citep{Schek06, LB06, YL11, Santos-Lima}. \n\nPropagation of CRs perpendicular to mean magnetic field\nis another important problem for which one needs to take into account both large and small scale interactions in tested models of turbulence. Indeed, if one takes only the diffusion along the magnetic field line and field line random walk \\citep[FLRW][]{Jokipii1966, Jokipii_Parker1969, Forman1974}, compound (or subdiffusion) would arise. Whether the subdiffusion is realistic in fact depends on the models of turbulence chosen \\citep{YL08, Yan:2011valencia}. In this chapter we review current understandings to this question within the domain of numerically tested models of MHD turbulence.\n\nIn what follows, we introduce the basic mechanisms for the interactions between particles and turbulence in \\S2. We discuss the cosmic ray transport in large scale turbulence, including both analytical and numerical studies in \\S3. Applications to cosmic ray propagation is presented in \\S4. In \\S5, we consider the perpendicular transport of cosmic rays on both large and small scales. We shall also discuss the issue of super-diffusion and the applicability of sub-diffusion. In \\S6, we concentrate on the issue of self-confinement in the presence of preexisting turbulence and dwell on, in particular, the streaming instability at supernova remnant shocks and its implication for CR acceleration. \\S7, we address the issue of gyroresonance instability of CRs and its feedback on large scale compressible turbulence. Summary is provided in \\S8.\n\n\\section{Interactions between turbulence and particles}\n\\label{basics}\nBasically there are\ntwo types of resonant interactions: gyroresonance acceleration\nand transit acceleration (henceforth TTD). The resonant condition is $\\omega-k_{\\parallel}v\\mu=n\\Omega$ ($n=0, \\pm1,2...$),\nwhere $\\omega$ is the wave frequency, $\\Omega=\\Omega_{0}\/\\gamma$\nis the gyrofrequency of relativistic particle, $\\mu=\\cos\\xi$,\nwhere $\\xi$ is the pitch angle of particles. TTD formally corresponds to $n=0$ and it requires compressible perturbations. \n\nThe Fokker-Planck equation is generally used to describe\nthe evolvement of the gyrophase-averaged distribution function $f$,\n\n\\[\n\\frac{\\partial f}{\\partial t}=\\frac{\\partial}{\\partial\\mu}\\left(D_{\\mu\\mu}\\frac{\\partial f}{\\partial\\mu}+D_{\\mu p}\\frac{\\partial f}{\\partial p}\\right)+\\frac{1}{p^{2}}\\frac{\\partial}{\\partial p}\\left[p^{2}\\left(D_{\\mu p}\\frac{\\partial f}{\\partial\\mu}+D_{pp}\\frac{\\partial f}{\\partial p}\\right)\\right],\\]\n where $p$ is the particle momentum. The Fokker-Planck coefficients\n$D_{\\mu\\mu},D_{\\mu p},D_{pp}$ are the fundamental physical parameters\nfor measuring the stochastic interactions, which are determined by\nthe electromagnetic fluctuations \\citep[see][]{SchlickeiserMiller}:\n\nGyroresonance happens when the Doppler shifted wave frequency matches the Larmor frequency of a particle. In quasi-linear theory (QLT), the Fokker-Planck\ncoefficients are given by \\citep[see][]{SchlickeiserMiller, YL04}\n\n\\begin{eqnarray}\n\\left(\\begin{array}{c}\nD_{\\mu\\mu}\\\\\nD_{pp}\\end{array}\\right) = {\\frac{\\pi\\Omega^{2}(1-\\mu^{2})}{2}}\\int_{\\bf k_{min}}^{\\bf k_c}dk^3\\delta(k_{\\parallel}v_{\\parallel}-\\omega \\pm \\Omega)\n\\left[\\begin{array}{c}\n\\left(1+\\frac{\\mu V_{ph}}{v\\zeta}\\right)^{2}\\\\\nm^{2}V_{A}^{2}\\end{array}\\right]\\times\\nonumber\\\\\n\\times\\left\\{ \\left[J_{2}^{2}\\left({\\frac{k_{\\perp}v_{\\perp}}{\\Omega}}\\right)+J_{0}^{2}\\left({\\frac{k_{\\perp}v_{\\perp}}{\\Omega}}\\right)\\right]\n\\left[\\begin{array}{c}\nM_{{\\mathcal{RR}}}({\\mathbf{k}})+M_{{\\mathcal{LL}}}({\\mathbf{k}})\\\\\nK_{{\\mathcal{RR}}}({\\mathbf{k}})+K_{{\\mathcal{LL}}}({\\mathbf{k}})\\end{array}\\right]\\right.\\nonumber\\\\\n\\left.-2J_{2}\\left({\\frac{k_{\\perp}v_{\\perp}}{\\Omega}}\\right)J_{0}\\left({\\frac{k_{\\perp}v_{\\perp}}{\\Omega}}\\right)\n\\left[e^{i2\\phi}\\left[\\begin{array}{c}\nM_{{\\mathcal{RL}}}({\\mathbf{k}})\\\\\nK_{{\\mathcal{RL}}}({\\mathbf{k}})\\end{array}\\right]+e^{-i2\\phi}\\left[\\begin{array}{c}\nM_{{\\mathcal{LR}}}({\\mathbf{k}})\\\\\nK_{{\\mathcal{LR}}}({\\mathbf{k}})\\end{array}\\right]\\right]\\right\\} ,\\label{gyro}\n\\end{eqnarray}\nwhere $\\zeta=1$ for Alfv\\'{e}n modes and $\\zeta=k_{\\parallel}\/k$\nfor fast modes, $k_{min}=L^{-1}$, $k_c=\\Omega_{0}\/v_{th}$\ncorresponds to the dissipation scale, $m=\\gamma m_{H}$ is the relativistic\nmass of the proton, $v_{\\perp}$ is the particle's velocity component\nperpendicular to $\\mathbf{B}_{0}$, $\\phi=\\arctan(k_{y}\/k_{x}),$\n${\\mathcal{L}},{\\mathcal{R}}=(x\\pm iy)\/\\sqrt{2}$ represent left and\nright hand polarization. $M_{ij}$ and $K_{ij}$ are the correlation tensors of magnetic and velocity fluctuations. \n\nFrom the resonance condition, we know that the most important interaction\noccurs at $k_{\\parallel}=k_{\\parallel,res}=\\Omega\/v_{\\parallel}$.\nThis is generally true except for small $\\mu$ (or scattering near\n$90^{\\circ}$). \n\nTTD happens due to the resonant interaction with parallel magnetic mirror force. Particles can be accelerated by when they are in phase with the waves either by interacting with oscillating parallel electric field (Landau damping), or by moving magnetic mirrors (TTD). When particles are trapped by moving in the same speed with waves, an appreciable amount of interactions can occur between waves and particles. Since head-on collisions are more frequent than that trailing collisions, particles gain energies. Different from gyroresonance, the resonance function of TTD is broadened even for CRs with small pitch angles. The formal resonance peak $k_{\\parallel}\/k=V_{ph}\/v_{\\parallel}$ favors quasi-perpendicular modes. However, these quasi-perpendicular modes cannot form an effective mirror to confine CRs because the gradient of magnetic perturbations along the mean field direction $\\nabla_{\\parallel}\\mathbf{B}$ is small. As we will show later in \\S\\ref{NLT_sec}, the resonance is broadened in nonlinear theory \\citep[see][]{YL08}. \n\n\n\n\\section{Scattering of cosmic rays}\n\\label{scattering}\n\n\\subsection{Scattering by Alfv\\'{e}nic turbulence}\n\\label{Alf_scatter}\nAs we discussed in $\\S$2, Alfv\\'{e}n modes are anisotropic, eddies\nare elongated along the magnetic field, i.e., $k_{\\perp}>k_{\\parallel}$.\nThe scattering of CRs by Alfv\\'{e}n modes is suppressed first because\nmost turbulent energy goes to $k_{\\perp}$ due to the anisotropy of\nthe Alfv\\'{e}nic turbulence so that there is much less energy left\nin the resonance point $k_{\\parallel,res}=\\Omega\/v_{\\parallel}\\sim r_{L}^{-1}$.\nFurthermore, $k_{\\perp}\\gg k_{\\parallel}$ means $k_{\\perp}\\gg r_{L}^{-1}$\nso that cosmic ray particles have to be interacting with lots of eddies\nin one gyro period. This random walk substantially decreases the scattering\nefficiency. The scattering by Alfv\\'en modes was studied in YL02. In case that the pitch angle $\\xi$ not close to 0, the analytical result is \\begin{equation}\n\\left[\\begin{array}{c}\nD_{\\mu\\mu}\\\\\nD_{pp}\\end{array}\\right]=\\frac{v^{2.5}\\mu^{5.5}}{\\Omega^{1.5}L^{2.5}(1-\\mu^2)^0.5}\\Gamma[6.5,k_c^{-\\frac{2}{3}}k_{\\parallel,res}L^{\\frac{1}{3}}]\\left[\\begin{array}{c}\n1\\\\\nm^{2}V_{A}^{2}\\end{array}\\right],\\label{ana}\\end{equation}\nwhere $\\Gamma[a,z]$ is the incomplete gamma function. The presence\nof this gamma function in our solution makes our results orders of\nmagnitude larger than those%\n\\footnote{The comparison was done with the resonant term in Chandran (2000) as the nonresonant term is spurious %\n} in \\cite{Chandran00}, who employed\nGS95 ideas of anisotropy, but lacked the quantitative\ndescription of the eddies. However,\nthe scattering frequency,\n\n\\begin{equation}\n\\nu=2D_{\\mu\\mu}\/(1-\\mu^{2}),\\label{nu}\n\\label{nu}\n\\end{equation}\nare nearly $10^{10}$ times lower than the estimates for isotropic and slab model (see Fig.~\\ref{impl} {\\em left}). {\\em It is clear that for most interstellar circumstances, the scattering by Alfv\\'enic turbulence is suppressed.} As the anisotropy of the Alfv\\'{e}n modes is increasing with the\ndecrease of scales, the interaction with Alfv\\'{e}n modes becomes\nmore efficient for higher energy cosmic rays. When the Larmor radius\nof the particle becomes comparable to the injection scale, which is\nlikely to be true in the shock region as well as for very high energy cosmic\nrays in diffuse ISM, Alfv\\'{e}n modes get important.\n\n\\subsection{Cosmic ray scattering by compressible MHD turbulence}\n\nAs we mentioned earlier, numerical simulations of MHD turbulence supported the GS95 model of turbulence,\nwhich does not have the \"slab\" Alfv\\'enic modes that produced most of the scattering in the earlier models\nof CR propagation. Can the turbulence that does not appeal to CRs back-reaction (see \\S 4) produce \nefficient scattering? \n\nIn the models of ISM turbulence \\citep[]{Armstrong95, Mckee_Ostriker2007}, where the injection happens at large scale, \nfast modes were identified as a scattering agent for cosmic rays in interstellar medium \\cite[]{YL02,YL04}.\nThese works made use of the quantitative description of turbulence\nobtained in \\cite{CL02_PRL} to calculate\nthe scattering rate of cosmic rays. \n\nDifferent from Alfv\\'en and slow modes, fast modes are isotropic \\citep{CL02_PRL}. Indeed they are subject to both collisional and collisionless damping. The studies in \\cite{YL02, YL04} demonstrated, nevertheless, that the scattering by fast modes dominates in most cases in spite of the damping\\footnote{On the basis of weak turbulence theory, \\cite{Chandran2005} has argued that high-frequency \nfast waves, which move mostly parallel to magnetic field, generate Alfv\\'en waves also moving mostly parallel to magnetic field. We expect\nthat the scattering by thus generated Alfv\\'en modes to be similar to the scattering by the fast modes created by them. Therefore\nwe expect that the simplified approach adopted in \\cite{YL04} and the papers that followed to hold.} (see Fig.\\ref{impl} {\\em right}).\n\\begin{figure*} [h!t] \n{\\includegraphics[width=0.45\\textwidth]{YL_fig1a.eps} \n\\includegraphics[width=0.45\\textwidth]{comp.eps}\n} \n\\caption{\\small {\\em Left:} rate of CR scattering by\nAlfv\\'en waves versus CR energy. The lines at the top of the figure are\nthe accepted estimates obtained for Kolmogorov turbulence. The dotted\ncurve is from \\cite{Chandran00}. The analytical calculations are given\nby the solid line with our numerical calculations given by\ncrosses; {\\em Right:} the scattering by fast modes, dashed line represents the case without damping for fast modes included, the solid and dash-dot line are the results taking into account collisionless damping.}\n\\label{impl}\n\\end{figure*}\nMore recent studies of cosmic ray propagation and acceleration that explicitly appeal to the effect of\nthe fast modes include \\citet{Cassano_Brunetti, Brunetti_Laz, YL08, YLP08}.\nIncidentally, fast modes have been also identified as primary agents for the acceleration of charged dust particles \\cite{YL03,YLD04}.\n\n\n\\subsection{Nonlinear theory of diffusion}\n\\label{NLT_sec}\n\nWhile QLT allows easily to treat the CR dynamics in a local magnetic\nfield system of reference, a key assumption in QLT, that the particle's orbit is unperturbed, makes one wonder about the limitations of the approximation. Indeed, while QLT provides simple physical insights into scattering, it is known to have problems. For instance, it fails in treating $90^\\circ$ scattering \\citep[see][]{Volk:1973, Volk:1975, Jones:1973, Jones:1978, Owens:1974, Goldstein:1976, Felice90degree} and perpendicular transport \\citep[see][]{Kota_Jok2000, Matthaeus:2003}. \n\nIndeed, many attempts have been made to improve the QLT and various non-linear\n theories have been attempted (see \\citealt{Dupree:1966}, V\\\"olk 1973, 1975, \nJones, Kaiser \\& Birmingham 1973, Goldstein 1976). Currently we observe a surge\nof interest in finding way to go beyond QLT. Examples include the nonlinear guiding center theory \\citep[see][]{Matthaeus:2003}, second-order \nquasilinear theory \\citep{Shalchi_SQT, Qin_NLT, LeRoux:2007}, etc. Most of the analysis were limited to traditional 2D+slab models of MHD turbulence. An important step was taken in Yan \\& Lazarian (2008), where non-linear effect was accounted for in treating CR scattering in the type of MHD turbulence that are supported by numerical simulations. The results have been applied to both solar flares (Yan, Lazarian \\& Petrosian 2008) and grain acceleration \\citep{HLS12}. Below, we introduce the nonlinear theory and their applications to both particle transport and acceleration in incompressible and compressible turbulence based on the results from Yan \\& Lazarian (2008).\n\nThe basic assumption of the quasi-linear theory is that particles follow unperturbed orbits. In reality, particle's pitch angle varies gradually with the variation of the magnetic field due to conservation of adiabatic invariant $v_\\bot^2\/B$, where $B$ is the total strength of the magnetic field \\citep[see][]{Landau:1975}. Since B is varying in turbulent field, so are the projections of the particle speed $v_\\bot$ and $v_\\|$.\n This results in broadening of the resonance. The variation of the velocity is mainly caused by the magnetic perturbation $\\delta B_\\|$ in the parallel direction. This is true even for the incompressible turbulence we discussion in this section. For the incompressible turbulence, the parallel perturbation arises from the pseudo-Alfv\\'en modes. The perpendicular perturbation $\\delta B_\\bot$ is higher order effect, which we shall neglect here.\n\nThe propagation of a CR can be described as a combination of a motion of its guiding center and CR's motion about its guiding center. \nBecause of the dispersion of the pitch angle $\\Delta\\mu$ and therefore of the parallel speed $\\Delta v_\\|$, the guiding center is perturbed about the mean position $=v\\mu t$ as they move along the field lines. As a result, the perturbation $\\delta B({\\bf x},t)$ that the CRs view when moving along the field gets a different time dependence. The characteristic phase function $e^{ik_\\|z(t)}$ of the perturbation $\\delta B({\\bf x},t)$ deviates from that for plane waves. Assuming the guiding center has a Gaussian distribution along the field line, \\begin{equation}\nf(z)=\\frac{1}{\\sqrt{2\\pi}\\sigma_z}e^{-\\frac{(z-)^2}{2\\sigma_z^2}},\n\\label{gauss}\n\\end{equation}\none gets by integrating over z, \\begin{equation}\n\\int_{-\\infty}^{\\infty} dze^{ik_\\| z}f(z)= e^{ik_\\|}e^{-k_\\|^2\\sigma_z^2\/2}. \n\\label{phase}\n\\end{equation}\nThe first adiabatic invariant gives us \n\\begin{equation}\n\\sigma_z^2=<\\Delta v_\\|^2>t^2=\\frac{v^4}{v_\\|^2}\\left(\\frac{<\\delta B_\\parallel^2>}{B_0^2}\\right)t^2.\n\\end{equation}\n\n\nInsert the Eq.(\\ref{phase}) into the expression of $D_{\\mu\\mu}$ (see V\\\"olk 1975, \\citealt{YL04}), we obtain\n\n\\begin{eqnarray}\nD_{\\mu\\mu}&=&\\frac{\\Omega^2(1-\\mu^2)}{B_0^2}\\int d^3k\\sum_{n=0}^{\\infty}R_n(k_{\\parallel}v_{\\parallel}-\\omega\\pm n\\Omega)\\nonumber\\\\\n&&\\left[I^A({\\bf k})\\frac{n^2J_n^2(w)}{w^2}+\\frac{k_\\|^2}{k^2}J^{'2}_n(w)I^M({\\bf k})\\right],\n\\label{general}\n\\end{eqnarray} \nFollowing are the definitions of the parameters in the above equation. $\\Omega, \\mu$ are the Larmor frequency and pitch angle cosine of the CRs. $J_n$ represents Bessel function, and $w=k_\\bot v_\\bot\/\\Omega=k_\\bot LR\\sqrt{1-\\mu^2}$, where $R=v\/(\\Omega l)$ is the dimensionless rigidity of the CRs, $L$ is the injection scale of the turbulence. $k_\\bot, k_\\|$ are the components of the wave vector ${\\bf k}$ perpendicular and parallel to the mean magnetic field, $\\omega$ is the wave frequency. $I^A({\\bf k})$ is the energy spectrum of the Alfv\\'en modes and $I^M({\\bf k})$ represents the energy spectrum of magnetosonic modes. In QLT, the resonance function $R_n=\\pi\\delta(k_{\\parallel}v_{\\parallel}-\\omega\\pm n\\Omega)$. Now due to the perturbation of the orbit, it should be \n\\begin{eqnarray}\n&&R_n(k_{\\parallel}v_{\\parallel}-\\omega\\pm n\\Omega)\\nonumber\\\\\n&=&\\Re\\int_0^\\infty dt e^{i(k_\\|v_\\|+n\\Omega-\\omega) t-\\frac{1}{2}k_\\|^2<\\Delta v_\\|^2>t^2}\\nonumber\\\\\n&=&\\frac{\\sqrt{\\pi}}{|k_\\|\\Delta v_\\||}\\exp\\left[-\\frac{(k_\\|v \\mu-\\omega+n\\Omega)^2}{k_\\|^2\\Delta v_\\|^2}\\right]\\nonumber\\\\\n&\\simeq&\\frac{\\sqrt{\\pi}}{|k_\\||v_\\bot \\sqrt{M_A}}\\exp\\left[-\\frac{(k_\\|v \\mu-\\omega+n\\Omega)^2}{k_\\|^2v_\\bot^2M_A}\\right]\n\\label{resfunc}\n\\end{eqnarray}\nwhere $M_A\\equiv \\delta V\/v_A=\\delta B\/B_0$ is the Alfv\\'enic Mach number and $v_A$ is the Alfv\\'en speed. We stress that Eqs.~(\\ref{general},\\ref{resfunc}) are generic, and applicable to both incompressible and compressible medium. \n\nFor gyroresonance ($n=\\pm 1,2,...$), the result is similar to that from QLT for $\\mu\\gg \\Delta \\mu=\\Delta v_\\|\/v$. In this limit, Eq.(\\ref{general}) represents a sharp resonance and becomes equivalent to a $\\delta$-function when put into Eq.(\\ref{general}). \nIn general, the result is different from that of QLT, especially at $\\alpha\\rightarrow 90^\\circ$, the resonance peak happens at $k_{\\|,res}\\sim \\Omega\/\\Delta v$ in contrast to the QLT result \n$k_{\\|,res}\\sim\\Omega\/v_\\|\\rightarrow \\infty$. We shall\nshow below, that due to the anisotropy, the scattering coefficient $D_{\\mu\\mu}$ is still very small if the Alfv\\'en and the pseudo-Alfv\\'en modes are concerned. \n\nOn the other hand, the dispersion of the $v_\\parallel$ means that CRs with a much wider range of pitch angle can be scattered by the compressible modes through TTD \n($n=0$), which is marginally affected by the anisotropy and much more efficient than the gyroresonance. In QLT, the projected particle speed should be comparable to phase speed of the magnetic field compression according to the $\\delta$ function for the TTD resonance.. This means that only particles with a specific pitch angle \ncan be scattered. For the rest of the pitch angles, the interaction is still dominated by gyroresonance, which efficiency is negligibly small for the Alfv\\'enic anisotropic turbulence (see \\S\\ref{Alf_scatter}). With the resonance broadening, however, wider range of pitch angle can be scattered through TTD, including $90^\\circ$. \n\n\n\\subsection{Results from test particle simulations}\n\nWe live in an era when we can test various processes in astrophysics and numerical studies have become an important part of theoretical efforts. Test particle simulation has been used to study CR scattering and\ntransport \\cite{Giacalone_Jok1999, Mace2000}. The aforementioned studies, however, used synthetic\ndata for turbulent fields, which have several disadvantages.\nCreating synthetic turbulence data which has scale-dependent\nanisotropy with respect to the local magnetic field (as observed\nin \\citealt{CV00} and \\citealt{MG01}) is difficult\nand has not been realized yet. Also,\nsynthetic data normally uses Gaussian statistics and\ndelta-correlated fields, which is hardly appropriate for\ndescription of strong turbulence. \n\nUsing the results of direct numerical MHD simulations as the input data, \\cite{BYL2011} and \\cite{Xu_Yan} performed test particle simulations. Their results show good correspondence with the analytical predictions. We briefly summarize the results here. As shown in Fig.\\ref{xx_yy}, particles' motion is diffusive both along the magnetic field (x direction) and across the field (y direction). Moreover, the scattering coefficient shows the same pitch angle dependence as that predicted in \\cite{YL08}, namely the scattering is most efficient for large pitch angles due to the TTD mirror interaction (see Fig. \\ref{xx_yy} {\\em left}). \n\n\\begin{figure}\n\\includegraphics[width=0.45\\textwidth]{duu.eps}\n\\includegraphics[width=0.45\\textwidth]{ratio.jpg}\n\\caption{{\\em Left}: dimensionless CR scattering coefficient $D_{\\mu\n \\mu}\/\\Omega$ vs the pitch angle $\\mu$. It is dominated by TTD resonant mirror interaction with compressible modes; {\\em right:} Diffusive behavior of the particles displayed in the tracing\n simulations. Both the parallel and perpendicular transport are normal diffusion, and the ratio of their diffusion coefficients is $\\sim M_A^4$, consistent with the analytical prediction in \\cite{YL08} \\citep*[from][]{Xu_Yan}.}\n\\label{xx_yy}\n\\end{figure}\n\n\n\\section{Cosmic ray propagation in Galaxy}\n\\label{results}\nThe scattering by fast modes is influenced by the medium properties as the fast modes are subject to linear damping, e.g., Landau damping.\n Using the approach above we revisit the problem of the CR propagation in the selected phases of the ISM (see Table~\\ref{ch1t1} for a list of fiducial parameters appropriate for the idealized phases\\footnote{The parameters of idealized interstellar phases are a subject of debate. Recently, even the entire concept of the phase being stable\n entities has been challenged \\citep[see][and ref. therein]{Gazol:2007}. Indeed different parts\n of interstellar medium can exhibit variations of these parameters \\citep[see][and ref. therein]{Wolfire:2003}}) assuming that turbulence is injected on large scales.\n\\begin{table*}\n{\\footnotesize \\begin{tabular}{ccccccc}\n\\hline\n\\hline \n ISM&\nhalo&\n HIM&\n WIM&\n WNM&\n CNM&\n DC\\tabularnewline\n\\hline\nT(K)&\n $2\\times 10^6$&\n $1\\times10^{6}$&\n 8000&\n 6000&\n 100&\n 15\\tabularnewline\n$c_S$(km\/s)&\n130&\n91&\n8.1&\n7&\n0.91&\n0.35\\tabularnewline\nn(cm$^{-3}$)&\n $10^{-3}$&\n $4\\times10^{-3}$&\n 0.1&\n 0.4&\n 30&\n 200\\tabularnewline\n$l_{mfp}$(cm)&\n$4\\times 10^{19}$&\n$2\\times10^{18}$&\n$6\\times10^{12}$&\n$8\\times10^{11}$&\n$3\\times10^{6}$&\n$10^{4}$\\tabularnewline\nL(pc)&\n 100&\n 100&\n 50&\n 50&\n 50&\n 50\\tabularnewline\nB($\\mu$G)&\n5&\n2&\n5&\n5&\n5&\n15\\tabularnewline\n$\\beta$&\n0.28&\n3.5&\n0.11&\n0.33&\n0.42&\n0.046\\tabularnewline\ndamping&\n collisionless&\n collisional&\n collisional&\n neutral-ion&\n neutral-ion&\n neutral-ion\\tabularnewline\n\\hline\n\\hline\n\\end{tabular}\n\\caption{The parameters of idealized ISM phases and relevant damping. The\ndominant damping mechanism for turbulence is given in the last line. HIM=hot ionized medium, CNM=cold neutral medium, WNM=warm neutral\nmedium, WIM=warm ionized medium, DC=dark cloud.}}\n\\label{ch1t1}\n\\end{table*}\n\n\\subsection{Halo}\n\n\\begin{figure}\n\\includegraphics[width=0.45\\textwidth]{f5.eps}\n\\includegraphics[width=0.45\\textwidth]{f9.eps}\n\\caption{\\small {\\em Left}: The turbulence truncation scales in Galactic halo and warm ionized medium (WIM). The damping curves flattens around $90^\\circ$ due to field line wandering (dotted lines, see \\citealt*{YL04, LVC04}); For WIM, both viscous and collisionless damping are applicable; {\\em right}: The mean free paths in two different phases of ISM: halo (solid line) and WIM (dashed line). At lower energies ($\\sim<100$GeV), the different dependence in WIM is owing to the viscous damping \\citep[from][]{YL08}.}\n\\label{mfp}\n\\end{figure}\n\nIn Galactic halo (see Table~\\ref{ch1t1}), the Coulomb collisional mean free path is $\\sim 10$ pc, the plasma is thus in a collisionless regime. The cascading rate \nof the fast modes is \\citep{CL02_PRL}\n\\begin{equation}\n\\tau_k^{-1}=(k\/L)^{1\/2}\\delta V^2\/V_{ph}.\n\\label{tcasfast}\n\\end{equation}\n\nBy equating it with the collisionless damping rate \n\\begin{equation}\n\\Gamma_{c} = \\frac{\\sqrt{\\pi\\beta}\\sin^{2}\\theta}{2\\cos\\theta}kv_A\\times \\left[\\sqrt{\\frac{m_e}{m_i}}\\exp\\left(-\\frac{m_e}{\\beta m_i\\cos^2\\theta}\\right)+5\\exp\\left(-\\frac{1}{\\beta\\cos^{2}\\theta}\\right)\\right],\n\\label{Ginz}\n\\end{equation}\nwe obtain the turbulence truncation scale $k_c$:\n\\begin{equation}\nk_c L\\simeq \\frac{4M_A^4m_i\\cos^2\\theta}{\\pi m_e\\beta\\sin^4\\theta}\\exp\\left(\\frac{2m_e}{\\beta m_i\\cos^2\\theta}\\right).\n\\label{landauk}\n\\end{equation}\nwhere $\\beta=P_{gas}\/P_{mag}$.\n\nThe scale $k_c$ depends on the {\\it wave pitch angle} $\\theta$, which makes\nthe damping anisotropic. As the turbulence undergoes cascade and the waves propagate in a turbulent medium, the angle $\\theta$ is changing.\nAs discussed in YL04 the field wandering defines the spread of angles. During one cascading time, the fast modes propagate a distance \n$v\\tau_{cas} $ and see an angular deviation $\\tan \\delta \\theta \\simeq \\sqrt{\\tan^2\\delta \\theta_\\parallel+\\tan^2 \\delta\\theta_\\perp}$, which is\n\\begin{equation}\n\\tan \\delta\\theta \\simeq \\sqrt{\\frac{M_A^2\\cos\\theta}{27(kL)^{1\/2}}+\\left(\\frac{M_A^2\\sin^2\\theta}{kL}\\right)^{1\/3}}\n\\label{dthetaB}\n\\end{equation}\nAs evident, the damping scale given by Eq.(\\ref{landauk}) varies considerably especially when $\\theta\\rightarrow 0$ and $\\theta\\rightarrow 90^\\circ$. For the quasi-parallel modes, the randomization ($\\propto (kL)^{-1\/4}$) is negligible since the turbulence cascade continues to very small scales. On small scales, most energy of the fast modes is contained in these quasi-parallel modes \\citep*{YL04, Petrosian:2006}.\n\nFor the quasi-perpendicular modes, the damping rate (Eq.\\ref{Ginz}) should be averaged over the range $90^\\circ-\\delta\\theta$ to $90^\\circ$. Equating Eq.(\\ref{tcasfast}) and Eq.(\\ref{Ginz}) averaged over $\\delta\\theta$, we get the averaged damping wave number (see Fig.\\ref{mfp} {\\em left}). The field line wandering has a marginal effect on the gyroresonance, whose interaction with the quasi-perpendicular modes is negligible (YL04). However, TTD scattering rates of moderate energy CRs ($<10$TeV) will be decreased owing to the increase of the damping around the $90^\\circ$ (see Fig.\\ref{mfp} {\\em left}). For higher energy CRs, the influence of damping is marginal and so is that of field line wandering. \n\n\\begin{figure}\n\\includegraphics[width=0.45\\textwidth]{f7.eps}\n\\includegraphics[width=0.45\\textwidth]{f8.eps}\n\\caption{Pitch angle diffusion coefficients in halo and WIM. Upper lines in the plots represent the contribution from TTD and lower lines are for gyroresonance \\citep[from][]{YL08}.}\n\\label{fastcompr}\n\\end{figure}\n\nThe QLT result on gyroresonance in the range $\\mu>\\Delta \\mu$ provides a good approximation to the non-linear results \\citep{YL08}. For CRs with sufficiently small rigidities, the resonant fast modes ($k_{res}\\approx 1\/(R\\mu)$) are on small scales with a quasi-slab structure (see Fig.\\ref{mfp} {\\em left}). For the scattering by these quasi-parallel modes, the analytical result that follows from QLT approximation \\citep[see][]{YL04} for the gyroresonance is\\footnote{It can be shown that the QLT result follows from our more general results (see Eqs.\\ref{general}, \\ref{resfunc}) if we put $\\Delta \\mu \\rightarrow 0$. This justifies our use of the analytical approximation.} \n\n\\begin{equation}\n\\left[\\begin{array}{c}\nD^{G}_{\\mu\\mu}\\\\\nD^{G}_{pp}\\end{array}\\right]=\\frac{\\pi v \\mu^{0.5}(1-\\mu^{2})}{4LR^{0.5}}\\left[\\begin{array}{c}\n\\frac{1}{7}[1+(R\\mu)^2]^{-\\frac{7}{4}}-(\\tan^{2}\\theta_c+1)^{-\\frac{7}{4}}\\\\\n\\frac{m^{2}V_{A}^{2}}{3}\\left\\{[1+(R\\mu)^2]^{-\\frac{3}{4}}-(\\tan^{2}\\theta_c+1)^{-\\frac{3}{4}}\\right\\}\\end{array}\\right]\n\\label{lbgyro}\\end{equation}\nwhere $\\tan\\theta_c={k_{\\perp,c}}\/{k_{\\parallel,res}}$.\n\nOnce we know the functional form of the $D_{\\mu\\mu}$, we can obtain the corresponding mean free path \\citep{Earl:1974}:\n\\begin{equation}\n\\lambda_\\|\/L=\\frac{3}{4}\\int^1_0 d\\mu \\frac{v(1-\\mu^2)^2}{(D^T_{\\mu\\mu}+D^G_{\\mu\\mu})L},\n\\end{equation}\nwhere $D^T_{\\mu\\mu}$ is the contribution from TTD interaction and can be obtained using the nonlinear theory (see \\citealt{YL08}, and also \\S\\ref{NLT_sec}) with the inertial range of fast modes determined for the local medium (see, e.g. \\ref{landauk} in the case of collisionless damping). \n\nThe mean free path is sensitive to the scattering by gyroresonance at small pitch angles, due to the influence of damping on the fast modes on small scales. Fig.\\ref{fastcompr} shows the pitch angle diffusion of CRs with different energies due to the TTD and gyroresonance.\n \nThe weak dependence of the mean free path (see Fig.\\ref{mfp} {\\em right}) of the moderate energy (e.g$<1$TeV) CRs in halo results from the fact that gyroresonance changes marginally with the CR energy (see Fig.\\ref{fastcompr}). This is associated with the damping in collisionless medium. We expect that similar flat dependence can happen in any collisionless medium. This can be a natural explanation of the puzzling ``Palmer Concensus\" \\citep{Palmer:1982}, the same trend observed in solar wind.\n\n\\subsection{Warm Ionized Medium}\n\nIn warm ionized medium, the Coulomb collisional mean free path is $l_{mfp}=6\\times 10^{12}$ cm and the plasma $\\beta\\simeq0.11$. Suppose that the turbulence energy is injected from large scale, then the compressible turbulence is subjected to the viscous damping besides the collisionless damping. \nBy equating the viscous damping rate with the cascading rate (Eq.\\ref{tcasfast}), we obtain the following truncation scale, \n\\begin{eqnarray}\nk_{c}L=x_c\\left\\{\\begin{array}{rl}(1-\\xi^2)^{-\\frac{2}{3}} & \\beta\\ll 1\\\\\n(1-3\\xi^2)^{-\\frac{4}{3}} & \\beta\\gg 1\\end{array}\\right.\n\\end{eqnarray}\nwhere $x_c=\\left[\\frac{6\\rho\\delta V^2L}{\\eta_0V_A}\\right]^{\\frac{2}{3}}$,\n $\\eta_0$ is the longitudinal viscosity. In the low $\\beta$ regime, the motions are primarily perpendicular to the magnetic field so that $\\partial v_{x}\/\\partial x=\\dot{n}\/n\\sim\\dot{B}\/B$. The longitudinal viscosity enters here as the result of distortion of the Maxiwellian distribution \\citep[see][]{Braginskii:1965}. The transverse energy of the ions increases during compression because of the conservation of adiabatic invariant $v_{\\perp}^{2}\/B$. If the rate of compression is faster than that of collisions, the ion distribution in the momentum space is bound to be distorted from the Maxiwellian isotropic sphere to an oblate spheroid with the long axis perpendicular to the magnetic field. As a result, the transverse pressure gets greater than the longitudinal pressure, resulting in a stress $\\sim\\eta_{0}\\partial v_{x}\/\\partial x$.\nThe restoration of the equilibrium increases the entropy and causes the dissipation of energy.\n\n\nThe viscous damping scale is compared to collisionless cutoff scale (Eq.\\ref{landauk}) in Fig.\\ref{mfp} {\\it left}. \nAs shown there, both viscous damping and collisionless damping are important in WIM. Viscous damping is dominant for small $\\theta$ and\n collisionless damping takes over for large $\\theta$ except for $\\theta=90^\\circ$.\nThis is because collisionless damping increases with $\\theta$ much faster than the viscous damping. For sufficiently small wave pitch angles, the viscous damping is too small to prevent the fast modes to cascade down to scales smaller than the mean free path $l_{mfp}$. Because of the similar quasi-slab structure on small scales, \nEq.(\\ref{lbgyro}) can be also applied in WIM. The results are illustrated in Fig.\\ref{fastcompr}. Compared to the case in halo, we see that the qualitative difference stands in the gyroresonance. This is because gyroresonance is sensitive to the quasi-slab modes whose damping differs in halo and WIM. \n\n\\subsection{Other phases}\n\nIn hot ionized medium (HIM), the plasma is also in collisionless regime, but the density is higher and the plasma beta is larger than 1. The damping by protons thus becomes substantial especially at small pitch angles. The damping truncates the turbulence at much larger scales than the gyroscales of the CRs of the energy range we consider. No gyroresonance can happen and some other mechanisms are necessary to prevent CRs streaming freely along the field. The turbulence injected from small scales might play an important role (see \\S6). \n\n\nIn partially ionized gas one should take into account an additional damping that arises from ion-neutral collisions \\citep[see][]{Kulsrud_Pearce, LG01, LVC04}. In the latter work a viscosity-damped regime of turbulence was predicted at scales less the scale $k_{c, amb}^{-1}$ at which the ordinary magnetic turbulence is damped by ionic viscosity. The corresponding numerical work, e.g., \\cite{CLV_newregime} testifies that for the viscosity-damped regime the parallel scale stays equal to the scale of the ambipolar damping, \ni.e., $k_{\\|}=k_{c, amb}$, while $k_{\\bot}$ increases. In that respect, the scattering by such magnetic fluctuations is analogous to the scattering induced by the weak turbulence (see \\S 2.3, \\citealt{YL08}). The difference stems from the spectrum\nof $k_{\\bot}$ is shallower than the spectrum of the weak turbulence. The predicted values of the spectrum for the viscosity-damped turbulence $E(k_\\bot)\\sim\nk_\\bot^{-1}$ \\citep{LVC04} are in rough agreement with simulations. More detailed studies of scattering in partially ionized gas will be necessary. \n\n\\section{Perpendicular transport}\n\nIn this section we deal with the diffusion perpendicular\nto {\\it mean} magnetic field. \n\nPropagation of CRs perpendicular to the mean magnetic field is another important problem in which QLT encounters serious difficulties.\nCompound diffusion, resulting from the convolution of diffusion along the magnetic field line and diffusion of field line perpendicular to mean field direction, has been invoked to discuss transport of cosmic rays in the Milky Way \\citep*{Getmantsev, Lingenfelter:1971,Allan:1972}. The role of compound diffusion in the acceleration\n of CRs at quasi-perpendicular shocks were investigated by \\cite{Duffy:1995} and \\cite{Kirk:1996}. \n\nIndeed, the idea of CR transport in the direction perpendicular to the mean magnetic field being dominated by the field line random walk \n(FLRW, \\citealt{Jokipii1966, Jokipii_Parker1969, Forman1974}) can be easily justified\nonly in a restricted situation where the turbulence perturbations are small and CRs do not scatter backwards to retrace their trajectories. If the latter is not true, the particle motions are subdiffusive, \ni.e., the squared distance diffused growing as not as $t$ but as $t^{\\alpha}$, $\\alpha<1$, e.g., $\\alpha=1\/2$ \\citep{Kota_Jok2000, Mace2000, Qin2002}.\nIf true, this could indicate a substantial shift in the paradigm of CR transport, a shift that surely dwarfs a modification of magnetic turbulence model from the 2D+slab to a more simulation-motivated model that we deal here.\n\n \nIt was also proposed that with substantial transverse structure, {\\it i.e.}, transverse displacement of field lines, perpendicular diffusion is recovered \\citep{Qin2002}. Is it the case of the MHD turbulence models we deal with? \n\n\nHow realistic is the subdiffusion in the presence of turbulence? The answer for this question apparently depends on the models of turbulence chosen. \n\nCompound diffusion happens when particles are restricted to the magnetic field lines and perpendicular transport is solely due to the random walk of field line wandering \\citep[see][]{Kota_Jok2000}. \nIn the three-dimensional turbulence, field lines are diverging away due to shearing by the Alfv\\'en modes \\citep[see][]{LV99, Narayan_Medv, Lazarian06}.\n Since the Larmor radii of CRs are much larger than the minimum scale of eddies $l_{\\bot, min}$, field lines within the CR Larmor orbit are effectively diverging away owing to shear by the Alfv\\'enic turbulence.\nThe cross-field transport thus results from the deviations of field lines at small scales, as well as field line random walk at large scale ($>{\\rm min}[L\/M^3_A,L]$).\n\nBoth observation of Galactic CRs and solar wind indicate that the diffusion of CRs perpendicular to magnetic field is normal diffusion \\citep[]{Giacalone_Jok1999, Maclennan2001}. Why is that?\n\nMost recently the diffusion in magnetic fields was considered for thermal particles in Lazarian (2006), for cosmic rays in \\cite{YL08}. In what follows we present the results based on the studies in \\cite{YL08}.\n\n\\subsection{Perpendicular diffusion on large scale}\n\nFor perpendicular diffusion, the important issue is the reference frame. We emphasize that we consider the diffusion perpendicular to the {\\emph mean} field direction in the global reference of frame. \n\n{\\it High $M_A$ turbulence}: High $M_A$ turbulence corresponds to the field that is easily bended by\nhydrodynamic motions at the injection scale as the hydro energy at the\ninjection scale is much larger than the magnetic energy, i.e.\n$\\rho V_L^2\\gg B^2$. In this case\nmagnetic field becomes dynamically important on a much smaller scale, i.e. the \nscale $l_A=L\/M_A^3$ \\citep[see][]{Lazarian06}. If the parallel mean free path of CRs $\\lambda_\\|\\ll l_A$, the stiffness of B field is negligible so that the perpendicular diffusion coefficient is the same as the parallel one, i.e., $D_\\bot=D_\\|\\sim 1\/3 \\lambda_{\\|} v$. If $\\lambda_\\|\\gg l_A$, the\n diffusion is controlled by the straightness of the field lines, and $\nD_\\bot=D_{\\|}\\approx 1\/3l_Av.\n\\label{dbb}\n$ The diffusion is isotropic if scales larger than $l_A$ are\nconcerned. \n\n{\\it Low $M_A$ turbulence}: In the magnetically dominated case, i.e. the field that cannot be easily bended at\nthe turbulence injection scale, individual magnetic field lines are aligned\nwith the mean magnetic field. The diffusion in this case is anisotropic.\nIf turbulence is injected at scale $L$ it stays \nweak for the scales larger than $LM_A^2$ and it is \nstrong at smaller scales. Consider first the case of $\\lambda_\\|>L$.\nThe time of the individual step is $L\/v_\\|$, then $D_\\perp\\approx 1\/3Lv M_A^4.$\nThis is similar to the case discussed in the FLRW model (Jokipii 1966). However, we obtain the dependence of $M_A^4$ instead of their $M_A^2$ scaling. In the opposite case of $\\lambda_\\|1$). This can well explain the recently observed super-diffusion in solar wind \\citep{Perri2009}. Superdiffusion can have important implications for shock acceleration as discussed in details in \\cite{LY13}.\n\n\n\\subsection{Is there subdiffusion?}\nThe diffusion coefficient $D_{\\|}M_A^4$ we obtained in the case of $M_A<1$, means that the transport\nperpendicular to the dynamically strong magnetic field is a normal diffusion, rather\nthan the subdiffusion as discussed in a number of recent papers. This is also supported by test particle simulations (\\citealt*{BYL2011, Xu_Yan}, see Fig.\\ref{xx_yy} {\\it right}). Let us\nclarify this point by obtaining the necessary conditions for the subdiffusion\nto take place.\n\nThe major implicit assumption in subdiffusion (or compound diffusion) is that the particles trace back \ntheir \ntrajectories in x direction on the scale $\\delta z$. When is it possible to talk about retracing of particles? In the case of random motions at a single scale {\\it only}, the distance over \nwhich the particle\ntrajectories get uncorrelated is given by the \\cite{RR1978}\nmodel. Assuming that the damping scale of the turbulence is larger\nthat the CR Larmor radius, the \\cite{RR1978}\nmodel, when generalized to anisotropic turbulence provides \\citep{Narayan_Medv, Lazarian06} $L_{RR}=l_{\\|, min}\\ln(l_{\\bot, min}\/r_{Lar})$\nwhere $l_{\\|, min}$ is the parallel scale of the cut-off of turbulent motions, \n$l_{\\bot, min}$ is the corresponding perpendicular scale, $r_{Lar}$ is the\nCR Larmor radius. The assumption of $r_{Lar}l_{\\bot, min}$, as it is a usual case for Alfv\\'en motions in the\nphase of ISM with the ionization larger than $\\approx 93\\%$, where the\nAlfv\\'enic motions go to the thermal particle gyroradius \n\\citep[see estimates in][]{LG01, LVC04}, \nthe subdiffusion of CR is not an applicable concept for Alfv\\'enic turbulence. \nThis does\nnot preclude subdiffusion from taking place\nin particular models of magnetic perturbations,\ne.g. in the slab model considered in \\cite{Kota_Jok2000}, but we believe in the omnipresence of Alfv\\'enic turbulence in interstellar gas \\citep[see][]{Armstrong95}.\n\n\\section{Streaming Instability in the Presence of Turbulence}\n\n\\begin{table}\n\\caption{The notation we used in this section}\n\\label{notations}\n\\begin{tabular}{|c|r|}\n\\hline\nA & normalized wave amplitude $\\delta B\/B_0$\\\\\na& hardening of the CR spectrum at the shock front\\\\\n$B_0$ & mean magnetic field at the shock in the later Sedov phase\\\\\n$B_{cav}$ &inercloud magnetic field strength\\\\\n$\\delta B$& wave amplitude\\\\\nc & light speed\\\\\nd& distance of the molecular cloud from observer\\\\\nD& diffusion coefficient of CRs\\\\\nE& CR energy\\\\\n$E_{SN}$& supernova explosion\\\\\nf& distribution function of CRs\\\\\n$f_\\pi$& fraction of energy transferred from parent protons to pions\\\\\nk& wave number\\\\\nK(t) & Normalization factor of CR distribution function\\\\\nL& the injection scale of background turbulence\\\\\nm& proton rest mass\\\\\n$M_c$ & cloud mass\\\\ \nn& intercloud number density\\\\ \n$N_\\gamma$ & $\\gamma$ ray flux\\\\\np& CR's momentum\\\\\n$p_{max}$& the maximum momentum accelerated at the shock front\\\\\n$P_{CR}$ & CR pressure\\\\\nq & charge of the particle\\\\\nr& distance from SNR centre\\\\\n$R_c$& the distance of the molecular cloud from the SNR centre\\\\\n$r_g$ & Larmor radius of CRs\\\\\n$R_d$ & diffusion distance of CRs\\\\\n$R_{sh}$ & shock radius\\\\\n$R_{esp},\\,t_{esp}$& the escaping distance\/time of CRs\\\\ \ns& 1D spectrum index of CR distribution\\\\\nt& time since supernova explosion\\\\\n$t_{age}$ & the age of SNR\\\\\n$t_{sed}$ & the time at which SNR enters the Sedov phase\\\\\nU& shock speed\\\\\n$U_i$ &initial shock velocity\\\\\nv& particle speed\\\\\n$v_s$ & streaming speed of CRs\\\\\nW& wave energy\\\\\n$\\alpha$& power index of D with respect to particle momentum p\\\\\n$\\chi$&reduction factor of D with respect to $D_{ISM}$\\\\\n$\\delta$& power index of $p_{max}$ with respect to t\\\\\n $\\eta$&fraction of SN energy converted into CRs\\\\\n $\\eta_A$& a numerical factor in Eq.\\ref{pp}\\\\\n$\\Gamma_{cr}$& the growth rate of streaming instability\\\\\n$\\Gamma_d$& wave damping rate\\\\\n $ \\kappa$&ratio of diffusion length to shock radius\\\\\n $\\Omega_0$ & the Larmor frequency of non-relativistic protons\\\\\n $\\sigma_{pp}$ &cross section for pp collision\\\\\n $\\xi$& the ratio of CR pressure to fluid ram pressure\\\\\n\\hline\n\\end{tabular}\n\\end{table}\n \nWhen cosmic rays stream at a velocity much larger than Alfv\\'{e}n\nvelocity, they can excite by gyroresonance MHD modes which in turn scatter\ncosmic rays back, thus increasing the amplitude of the resonant mode. This \nrunaway process is known as streaming instability. It was claimed\nthat the instability could provide confinement for cosmic rays with\nenergy less than $\\sim 10^2$GeV \\citep{Cesarsky80}. However, this was calculated\nin an ideal regime, namely, there was no background MHD turbulence.\nIn other words, it was thought that the\nself-excited modes would not be appreciably damped in fully ionized gas\\footnote{We neglect the nonlinear Landau damping, which is suppressed in turbulence due to decrease of mean free path.}. \n\nThis is not true for turbulent medium, however. \\citet{YL02}\npointed out that the streaming instability is partially suppressed\nin the presence of background turbulence \\citep[see more in][]{LCY02}. More recently, detailed calculations of the \nstreaming instability in the presence of background Alfv\\'enic turbulence \nwere presented in \\cite{FG04}. The growth rate of the modes of wave number $k$ is \\citep{Longairbook}.\n\\begin{equation}\n\\Gamma_{cr}(k)=\\Omega_0\\frac{N(\\geq E)}{n_{p}}(-1+\\frac{v_{stream}}{V_{A}}),\n\\label{instability}\n\\end{equation}\n where $N(\\geq E)$ is the number density of cosmic rays with energy\n$\\geq E$ which resonate with the wave,\n$n_{p}$ is the number density of charged particles in the medium.\nThe number density of cosmic rays near the sun is $N(\\geq E)\\simeq 2\\times10^{-10}(E\/$GeV$)^{-1.6}$\ncm$^{-3}$sr$^{-1}$ \\citep{Wentzel74}.\n\nInteraction with fast modes was considered by \\citet{YL04}. \nSuch an interaction happens at the rate \n$\\tau_k\\sim (k\/L)^{-1\/2}V_{ph}\/V^2$. \nBy equating it with the growth rate Eq.(\\ref{instability}), we can find that the streaming instability\nis only applicable for particles with energy less than\n\\begin{equation}\n\\gamma_{max}\\simeq1.5\\times 10^{-9}[n_{p}^{-1}(V_{ph}\/V)(Lv\\Omega_0\/V^2)^{0.5}]^{1\/1.1},\n\\end{equation}\nwhich for HIM, provides $\\sim 20$GeV if taking the injection speed to be $V\\simeq 25$km\/s. Similar result was obtained with Alfv\\'en modes by \\citet{FG04}. \n\n\nMagnetic field itself is likely to be amplified through an inverse \ncascade of magnetic energy at which perturbations created at a particular\n$k$ diffuse in $k$ space to smaller $k$ thus inducing inverse cascade. \nAs the result, the magnetic perturbations at smaller $k$ get larger than the \nregular field. Consequently, even if the instability is suppressed\nfor the growth rate given by Eq. (\\ref{instability}) it gets efficient\ndue to the increase of perturbations of magnetic field stemming from the\ninverse cascade. The precise picture of the process depends on yet not completely\nclear details of the inverse cascade of magnetic field. \n\nBelow, we present the application of the current understanding of the interaction between the streaming instability and the background turbulence to the modeling of the gamma ray emission from molecular clouds near SNRs \\citep[see more details in][]{YLS12}. We shall treat the problem in a self-consistent way by comparing the streaming level that is allowed by the preexisting turbulence and the required diffusion for the CRs. \n\n\\subsection{Application to CR acceleration at the shocks }\n\\label{pmax}\nDiffusive shock acceleration of energetic CR particles relies on the crucial process of amplification of MHD turbulence so that particles can be trapped at the shock front long enough to be accelerated to the high energy observed. One of the most popular scenarios that has been adopted in the literature is the streaming instability generated by the accelerated particles. However, in the highly nonlinear regime the fluctuations of magnetic field arising from the streaming\ninstability get large and the classical treatment of the streaming instability is not applicable. We circumvent the\nproblem by proposing that the field amplification we consider does not arise from the streaming\ninstability, but is achieved earlier through other processes, e.g. the interaction of the shock precursor with density perturbations preexisting in the interstellar medium \\citep*{BJL09}. Due to the\nresonant nature of the streaming instability, the perturbations $\\delta B$ arising from it are more efficient\nin scattering CRs compared to the large scale fluctuations produced by non-resonant mechanisms, e.g.\nthe one in \\citet{BJL09}. Therefore in this chapter, we limit our discussions to the regime of $\\delta B \\sim< B_0$, where $B_0$ is the magnetic field that has already been amplified in the precursor region\\footnote{The effective $B_0$ is therefore renormalized and can be much larger than the typical field in ISM (see, e.g., \\citealt{Diamond_Makov}).}. \n\nWhen particles reach the maximum energy at a certain time, they escape and the growth of the streaming instability stops. Therefore we can obtain the maximum energy by considering the stationary state of the evolution. The steady state energy density of the turbulence $W(k)$ at the shock is determined by\n\n\\begin{equation}\n(U\\pm v_A)\\nabla W(k) = 2 (\\Gamma_{cr}-\\Gamma_d)W(k),\n\\label{wave}\n\\end{equation}\nwhere $U$ is the shock speed, and the term on the l.h.s. represents the advection of turbulence by the shock flow. $v_A\\equiv B_0\/\\sqrt{4\\pi nm}$ and $n$ are the Alfv\\'en speed and the ionized gas number density of the precursor region, respectively. The plus sign represents the forward propagating Alfv\\'en waves and the minus sign refers to the backward propagating Alfv\\'en waves. The terms on the r.h.s. describes the wave amplification by the streaming instability and damping with $\\Gamma_d$ as the corresponding damping rate of the wave. The distribution of accelerated particles at strong shocks is $f(p)\\propto p^{-4}$. If taking into account the modification of the shock structure by the accelerated particles, the CR spectrum becomes harder. Assume the distribution of CRs at the shock is $f_0(p)\\propto p^{-4+a}$. The nonlinear growth was studied by \\citet{Ptuskin:2005}. \n\nThe generalized growth rate of streaming instability is\n\n\\begin{eqnarray}\n\\Gamma_{cr}&=&\\frac{12\\pi^2 q^2v_A\\sqrt{1+A^2}}{c^2k}\\nonumber\\\\\n&\\times& \\int^\\infty_{p_{res}} dp p\\left[1-\\left(\\frac{p_{res}}{p}\\right)^2\\right]D\\left|\\frac{\\partial f}{\\partial x}\\right|, \n\\label{general_growth}\n\\end{eqnarray}\nwhere $q$ is the charge of the particle, c is the light speed, $p_{res}=ZeB_0\\sqrt{1+A^2}\/c\/k_{res}$ is the momentum of particles that resonate with the waves. $A=\\delta B\/B_0$ is wave amplitude normalized by the mean magnetic field strength $B_0$.\n\\begin{equation}\nD=\\sqrt{1+A^2}v r_g\/3\/A^2(>k_{res})\n\\label{crdiff}\n\\end{equation}\nis the diffusion coefficient of CRs, $v$ and $r_g$ are the velocity and Larmor radius of the CRs. \nIn the planar shock approximation, one gets the following growth rate of the upstream forward moving wave at x=0,\n\\begin{equation}\n\\Gamma_{cr}(k)=\\frac{C_{cr}\\xi U^2(U+v_A)k^{1-a}}{(1+A^2)^{(1-a)\/2}cv_A\\phi(p_{max})r_0^a} \n\\label{growth}\n\\end{equation}\nwhere $C_{cr}=4.5\/(4-a)\/(2-a)$, $r_0=m c^2\/q\/B_0$, where $\\xi$ measures the ratio of CR pressure at the shock and the upstream momentum flux entering the shock front, $m$ is the proton rest mass, and $p_{max}$ is the maximum momentum accelerated at the shock front. $H(p)$ is the Heaviside step function.\n\nThe linear damping is negligible since the medium should be highly ionized. In fully ionized gas, there is nonlinear Landau damping, which, however, is suppressed due to the reduction of particles' mean free path in the turbulent medium \\citep[see][]{YL11}. We therefore neglect this process here. Background turbulence itself can cause nonlinear damping to the waves \\citep{YL02}. Unlike hydrodynamical turbulence, MHD turbulence is anisotropic with eddies elongated along the magnetic field. The anisotropy increases with the decrease of the scale \\citep{GS95}. Because of the scale disparity, $k_\\| > k_\\bot \\gg k^t_\\|$, the nonlinear damping rate in MHD turbulence is less than the wave frequency $k_\\| v_A$, and it is given by \\citep{FG04, YL04}\n\n\\begin{equation}\n\\Gamma_d \\sim \\sqrt{k\/L} v_A,\n\\label{damping}\n\\end{equation}\nwhere L is the injection scale of background turbulence, and the $k$ is set by the resonance condition $k \\sim k_\\| \\sim 1\/r_L$.\n\nThere are various models for the diffusive shock acceleration. We consider here the escape-limited acceleration. In this model, particles are confined in the region near the shock where turbulence is generated. Once they propagate far upstream at a distance $l$ from the shock front, where the self-generated turbulence by CRs fades away, the particles escape and the acceleration ceases. The characteristic length that particles penetrate into the upstream is $D(p)\/U$. The maximum momentum is reached when $D(p)\/U\\simeq l\/4$\\footnote{The factor 1\/4 arises from the following reason. As pointed out by \\cite{Ostrowski:1996}, the spectrum is steepened for small l, i.e., $l U\/D(p) \\sim< 4$}. Assuming $l=\\kappa R_{sh}$, where $\\kappa<1$ is a numerical factor, one can get\n\\begin{equation}\n\\frac{p_{max}}{mc} = \\frac{3\\kappa A^2 U R_{sh}}{\\sqrt{1+A^2}v r_0}.\n\\label{gmax}\n\\end{equation}\n\nIn particular, for $A<1$\n\\begin{eqnarray}\\frac{p_{max}}{mc}&=&\\left[\\left(-v_A\\sqrt{\\frac{1}{r_0 L}}+\\sqrt{\\frac{v_A^2}{r_0 L}+\\frac{2C_{cr}a\\xi U^3(U+v_A)}{\\kappa r_0R_{sh}cv_A}}\\right) \\left(\\frac{\\kappa R_{sh}}{U}\\right)\\right]^2,\\nonumber\\\\\nA&=&\\frac{p_{max}r_0}{\\sqrt{18}\\kappa mU R_{sh}}\\sqrt{1+\\sqrt{1+36 \\left(\\frac{\\kappa mU R_{sh}}{p_{max}r_0}\\right)^2}},\n\\label{gmax_general}\n\\end{eqnarray}\n\nIn the limit of low shock velocity, \n\\begin{eqnarray\nv_A\\ll U\\ll c\\left[\\left(\\frac{v_A}{c}\\right)^3\\frac{\\kappa R_{sh}}{2 L C_{cr}a\\xi}\\right]^{1\/4},\n\\label{lowshockU}\n\\end{eqnarray}\nwe get \n\\begin{eqnarray\n\\frac{p_{max}}{mc}&=&(C_{cr}\\xi U^3)^2\\frac{a^2 L}{r_0c^2v_A^4}\n\\label{gmax_solution}\n\\end{eqnarray}\nfor the Sedov phase ($t>t_{sed}\\equiv 250(E_{51}\/(n_0U_9^5))^{1\/3}$yr), where $E_{51}=E_{SN}\/10^{51}$erg and $U_9=U_i\/10^9$cm\/s are the total energy of explosion and the initial shock velocity. In Fig.\\ref{Emax}, we plot the evolution of $p_{max}\/(mc)$ during the Sedov phase. The solid line represents the results from Eqs.(\\ref{gmax_general}). As we see, at earlier epoch when advection and streaming instability are both important, the evolution of $p_{max}$ does not follow a power law. For comparison, we also put a power law evolution in the same figure as depicted by Eq.(\\ref{gmax_solution}) (dashed line). \nOur result is also larger than that obtained by \\cite{Ptuskin:2005} since the wave dissipation rate is overestimated in their treatment.\n\n\\begin{figure}\n\\includegraphics[width=0.47\\textwidth]{Emax.eps}\n\\includegraphics[width=0.47\\textwidth]{spectrum0.eps}\n \\caption{\\small {\\em Left}: The energy of CRs that are released at the shock at time t in the Sedov phase. Our result shows that the often assumed power law solution \\citep[see][]{Gabici:2009, Ohira:2010} is only realized in asymptotic regime as described in Eqs.(\\ref{lowshockU},\\ref{gmax_solution}). It is also larger than the earlier result (dotted line) in Ptuskin \\& Zirakashvili (2005) where the damping of the waves by background turbulence is overestimated. {\\em Right}: The spectrum of CRs at a distance $r=12$ pc after 1800 (solid line), 6000 (dotted line), 12000 (dashdot line), 50000 years (cross line). The Galactic mean is plotted as a reference (dashed line). From \\cite{YLS12}.}\n\\label{Emax} \n\\end{figure}\n\n\\subsection{Enhanced scattering and streaming instability near SNRs}\n\\label{nearby}\n\nThe result from \\cite{YLS12} show that the local scattering of CRs has to be enhanced by an order of magnitude $\\chi =0.05$ in order to produce the amount of $\\gamma$ ray emission observed. A natural way to increase the scattering rate is through the streaming instability. The enhanced flux of the CRs are demonstrated to generate strong enough instability to overcome nonlinear damping by the background turbulence \\citep{YLS12}. The growth rate in the linear regime is\n\n\\begin{equation}\n\\Gamma_{gr}=\\Omega_0\\frac{N(\\geq E)}{n}\\left(\\frac{v_s}{v_A}-1\\right),\n\\end{equation}\nwhere $v_s$ is the streaming speed of CRs. The growth rate should overcome the damping rate (eq.\\ref{damping}) for the instability to operate. The condition $\\Gamma_{gr}>\\Gamma_d$ leads to\n\\begin{equation}\nv_s > v_A \\left(1+\\frac{n v_A}{N \\Omega_0\\sqrt{r_gL}}\\right)\n\\end{equation}\n\nThe spatial diffusion coefficient adopted here, $D \\approx v_s L = \\chi D_{ISM}$, satisfies this requirement. The growth and damping rates are compared in Fig.\\ref{rates} {\\em right}. We see that the streaming instability works in the energy range needed to produce the observed $\\gamma$ ray emission, proving that our results are self-consistent.\n\nNote that the case we consider here is different from the general interstellar medium discussed in \\cite{YL04} and \\cite{FG04}, namely, the local cosmic ray flux near SNRs is much enhanced (see Fig.\\ref{Emax} {\\em right}). Consequently, the growth rate of the streaming instability becomes high enough to overcome the damping rate by the preexisting turbulence in the considered \nenergy range.\n\n\\begin{figure}\n\\includegraphics[width=0.47\\textwidth]{gmray.eps}\n\\includegraphics[width=0.47\\textwidth]{stream.eps}\n \\caption{\\small {\\em Left}: The spectrum of Gamma ray emission from W28. The Fermi data are shown as dotted points \\citep{Abdo:2010}, and the H.E.S.S. data are plotted as 'x' points \\citep{Aharonian:2008} with error bars. Solid line is our result. {\\em Right}: The growth and nonlinear damping rates of streaming instability. With the locally enhanced flux, the growth rate of streaming instability becomes much larger than the mean Galactic value so that it can overcome the nonlinear damping by turbulence for a wide energy range. This is consistent with our earlier treatment in which streaming instability plays an essential role in the cosmic ray diffusion near SNRs. From \\cite{YLS12}.}\n \\label{rates}\n\\end{figure}\n\n\\begin{table}\n\\caption{Model parameters adopted}\n\\begin{tabular}{cccccc}\n\\hline\n\\hline\na&$\\chi$&$\\eta$&$ \\kappa$&$\\xi$&$\\alpha$\\\\\n\\hline\n0.1$\\sim 0.3$&$\\sim$0.05&$\\sim 0.3$&$0.04\\sim 0.1$& $0.2\\sim$0.4& 0.5\\\\\n\\hline\n\\hline\n\\end{tabular}\n\\end{table}\n\n\n\\section{Gyroresonance Instability of CRs in Compressible Turbulence}\n\n\\begin{figure}\n\\includegraphics[width=0.55\\columnwidth,\n height=0.25\\textheight]{feedback.eps}\n\\includegraphics[width=0.4\\columnwidth,\n height=0.25\\textheight]{YL_fig1c.eps}\n\\caption{{\\em Left}: The spectral energy density of slab waves that is transferred from the large scale compressible turbulence via the gyroresonance instability of CRs. In the case that the instability grows up to the maximum energy rate allowed by the turbulence cascade, large scale turbulence is truncated at $\\lambda_{fb}$ and the wave amplitude $E(k)dk\\sim \\epsilon_N^u$ is given by Eq.(\\ref{energy}). Note that the picture is different from LB06, namely, the feedback on the large scale turbulence occurs only in some cases when the scattering is not sufficient to prevent the waves from growing to the maximum values \\citep{YL11}; {\\em right}: CR scattering is dominated by compressible modes. For high energy CRs ($>\\sim$10GeV), the scattering is due to direct interaction with fast modes; For low energy CRs, the interaction is mainly due to the gyroresonance instability induced by compression of magnetic fields.}\n\\label{feedback_fig}\n\\end{figure}\n\nUntil recently, test particle approximation was assumed in most of earlier studies in which turbulence cascade is established from large scales and no feedback of CRs is included. This may not reflect the reality as we know the energy of CRs is comparable to that in turbulence and magnetic field \\citep[see][]{Kulsrudbook}. It was suggested by \\cite{LB06} that the gyroresonance instability of CRs can drain energy from the large scale turbulence and cause instability on small scales by the turbulence compression induced anisotropy on CRs (see Fig.\\ref{feedback_fig} {\\em left}). And the wave generated on the scales, in turn, provides additional scattering to CRs. In \\cite{YL11}, quantitative studies was provided based on the nonlinear theory of the growth of the instability and the feedback of the growing waves on the distributions of CRs.\n\nIn the presence of background compressible turbulence, the CR distribution is bound to be anisotropic because of the conservation of the first adiabatic invariant $\\mu\\equiv v_\\bot^2\/B$. Such anisotropic distribution is subjected to various instabilities. Waves are generated through the instabilities, enhancing the scattering rates of the particles, their distribution will be relaxed to the state of marginal state of instability even in the collisionless environment. While the hydrodynamic instability requires certain threshold, the kinetic instability can grow very fast with small deviations from isotropy. Here, we focus on the gyroresonance instability. Both the qualitative and quantitative studies in \\cite{YL11} show that the isotropization rate is roughly $\n\\tau^{-1}_{scatt} \\sim \\frac{\\Gamma_{gr}\\epsilon_N}{\\beta_{CR} A}\\label{nu_est}$, where $\\Gamma_{cr}, \\epsilon_N$ are the instability growth rate and the wave energy normalized by magnetic energy, respectively. $\\beta_{CR}$ is the ratio of CR pressure to magnetic pressure, $A$ is the degree of anisotropy of the CR momentum distribution.\n\nBy balancing the rate of decrease in anisotropy due to scattering and the growth due to compression, one can get\n\\begin{equation}\n\\epsilon_N\\sim \\frac{ \\beta_{CR}\\omega\\delta v}{\\Gamma_{gr} v_A },~~~\\lambda_{CR}=r_p\/\\epsilon_N.\n\\label{epsilon_est},\n\\end{equation}\nwhere $v_A$ is the Alfv\\'en speed, $\\omega, \\delta v$ are the wave frequency and amplitude at the scale that effectively compresses the magnetic field and create anisotropy in CRs' distribution \\citep{YL11}. Since the growth rate decreases with energy, the instability only operates for low energy CRs ($\\sim<$ 100GeV, see Fig.\\ref{feedback_fig} {\\em right}) due to the damping by the preexisting turbulence \\citep{YL11} .\n \n\\subsection{Bottle-neck for the growth of the instability and feedback on turbulence}\n\\label{feedback}\nThe creation of the slab waves through the CR resonant instability is another channel to drain the energy of large scale turbulence. This process, on one hand, can damp the turbulence. On the other hand, it means that the growth rate is limited by the turbulence cascade. The energy growth rate cannot be larger than the turbulence energy cascading rate, which is $1\/2 \\rho V_L^4\/v_A\/L$ for fast modes in low $\\beta$ medium and $\\rho v_A^3\/l_A$ for slow modes in high $\\beta$ medium. This places a constraint on the growth, thus the upper limit of wave energy is given by\n\\begin{eqnarray}\n\\epsilon^u_N=\\cases{ M_A^2 L_i\/(L A)\\gamma^{\\alpha-1},& $\\beta<1$ \\cr\n L_i\/(l_A A)\\gamma^{\\alpha-1}, & $\\beta>1$, \\cr}\n\\label{energy}\n\\end{eqnarray}\nwhere $\\gamma$ is the Lorentz factor and $L_i\\simeq 6.4\\times 10^{-7}(B\/5{\\rm \\mu G})(10^{-10}{\\rm cm}^3\/n_{cr})$ pc. The growth is induced by the compression at scales $\\sim< \\lambda_{CR}$. Therefore, in the case that $\\Gamma_{gr} \\epsilon$ reaches the energy cascading rate, fast modes are damped at the corresponding maximum turbulence pumping scale $\\lambda_{fb}=r_p\/\\epsilon_N$ (see Fig.\\ref{feedback_fig} {\\em left}). If $\\lambda_{fb}$ is larger than the original damping scale $l_c$, then there is a feedback on the large scale compressible turbulence. This shows that test particle approach is not adequate and feedback should be included in future simulations.\n\n\n\\section{Summary}\n\nIn this chapter, we reviewed recent development on cosmic ray transport theories based on modern understanding of MHD turbulence. The main conclusions from both analytical study and test particle simulations in MHD turbulence are:\n\\begin{itemize}\n\\item Compressible fast modes are most important for CR scattering. CR transport therefore varies from place to place.\n\\item Nonlinear mirror interaction is essential for pitch angle scattering (including 90 degree).\n\\item Cross field transport is diffusive on large scales and super-diffusive on small scales.\n\\item Subdiffusion does not happen in 3D turbulence.\n\\item Self - generated waves are subject to damping by preexisting turbulence \n\\item Small scale waves can be generated in compressible turbulence by gyroresonance instability. Feedback of CRs on turbulence need to be included in future simulations. \n\\end{itemize}\n\n\\bibliographystyle{apj}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzcauq b/data_all_eng_slimpj/shuffled/split2/finalzzcauq new file mode 100644 index 0000000000000000000000000000000000000000..97ccb4d4176d3bd855f6802c7d4a7b06da40edfe --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzcauq @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\n\nThe site where r-process nuclei above A=90 have been synthesized\nremains a major unsolved problem in nucleosynthesis theory\n\\citep[e.g.,][]{Arnould07}. Historically, many possibilities have\nbeen proposed \\citep[see][]{Meyer94}, but today, there are two\nprincipal contenders - neutron star mergers\n\\citep{Lattimer77,Freiburghaus99} and the NDW\n\\citep{Woosley94,Qian96,Hoffman97,Otsuki00,Thompson01}. Observations\nof ultra-metal-poor stars suggest that many r-process isotopes were\nalready quite abundant at early times in the galaxy\n\\citep{Cowan95,Sneden96,Frebel07}, suggesting both a primary origin\nfor the r-process and an association with massive stars. NDWs would\nhave accompanied the first supernovae that made neutron stars and,\ndepending upon what is assumed about their birth rate and orbital\nparameters, the first merging neutron stars could also have occurred\nquite early.\n\nBoth the merging neutron star model and the NDW have problems\nthough. In the simplest version of galactic chemical evolution,\nmerging neutron stars might be capable of providing the necessary\nintegrated yield of the r-process in the sun, but they make it too\nrarely in large doses and possibly too late to be consistent with\nobservations \\citep{Argast04}. On the other hand, making the\nr-process in NDWs requires higher entropies, shorter time-scales, or\nlower electron mole numbers, $Y_e$, than have been demonstrated in any\nrealistic, modern model for a supernova explosion \\citep[though\n see][]{Burrows06}.\n\nPrevious papers and models for nucleosynthesis in the NDW have focused\non the production of nuclei heavier than iron using either greatly\nsimplified dynamics \\citep{Beun08,Farouqi09} or nuclear\nphysics \\citep{Qian96,Otsuki00,Arcones07,Fischer09,Huedepohl09}.\nPost processing nuclear network calculations have been performed using \nthermal histories from accurate models of the dynamics \n\\citep{Hoffman97,Thompson01,Wanajo06}, but the calculations sampled only a \nlimited set of trajectories in the ejecta. No one has yet combined the \ncomplete synthesis of a realistic NDW with that of the rest of the \nsupernova.\n\nTo address this situation, and to develop a framework for testing the\nnucleosynthesis of future explosion models, we have calculated\nnucleosynthesis using neutrino luminosity histories taken from two PNS\ncalculations found in the literature \\citep{Woosley94,Huedepohl09}.\nThis was done using a modified version of the implicit one-dimensional\nhydrodynamics code Kepler, which includes an adaptive nuclear network\nof arbitrary size. This network allows for the production of both\nr-process nuclei during neutron-rich phases of the wind and production\nof light p-elements during proton-rich phases. Since the results of wind\nnucleosynthesis depend sensitively on the neutrino luminosities and\ninteraction rates \\citep{Qian96,Horowitz02}, we have included accurate\nneutrino interaction rates that contain both general relativistic and\nweak magnetism corrections. \n\nThe synthesis of all nuclei from carbon through lead is integrated\nover the history of the NDW and combined with the yield from the rest\nof the supernova, and the result is compared with a solar distribution.\nIf a nucleus produced in the NDW is greatly overproduced relative to\nthe yields of abundant elements in the rest of the supernova, there is\na problem. If it is greatly underproduced, its synthesis in the NDW is\nunimportant. If it is co-produced, the NDW may be responsible for the\ngalactic inventory of this element. An important outcome of this\nstudy are the yields expected from a ``plain vanilla'' model for the\nNDW. Are there any elements that are robustly produced and thus might\nbe used as diagnostics of the wind in an early generation of stars?\n\nIn \\S\\ref{wind_physics}, we discuss the general physics of neutrino\ndriven winds and analytically delineate the regions in neutrino\ntemperature space were different modes of nucleosynthesis occur. We\nthen discuss our numerical model in \\S\\ref{computational_method}. In\n\\S\\ref{results}, the results of the time dependent models are\npresented. We conclude with a discussion of how the NDW might affect\ngalactic chemical evolution and consider if this allows the strontium\nabundance in low metallicity halo stars to be used as a tracer of\nsupernova fallback at low metallicity. Additionally, we investigate\nif observed abundances in SN 1987A can put constraints on late time\nneutrino luminosities from PNSs. Finally, we discuss some possible\nmodifications of the basic model that might improve the r-process\nproduction. These ideas will be explored more thoroughly in \na subsequent paper.\n\n\\section{General Concepts and Relevant Physics} \\label{wind_physics}\n\nAfter collapse and bounce in a core collapse supernova, a condition of\nnear hydrostatic equilibrium exists in the vicinity of the\nneutrinospheres. The temperature of the outer layers is changing on a\ntime scale determined by the Kelvin-Helmholtz time of the PNS,\n$\\tau_{KH} \\approx 10 s$ \\citep{Burrows86,Pons99}. This is much\nlonger than the dynamical time scale of the PNS envelope, so the\nhydrostatic part of the envelope is in an approximate steady state.\nThe neutrino heating rate, which is determined by the neutrino\nluminosities from the neutrino sphere, must then balance the local\nneutrino cooling rate. Heating and cooling are dominated by the\ncharged current processes $(\\nu_e + n) \\rightleftharpoons (e^- + p)$\nand $(\\bar \\nu_e + p) \\rightleftharpoons (e^+ + n)$ \\citep{Qian96}.\nEquating these rates, while neglecting the neutron-proton mass\ndifference and weak magnetism corrections and assuming the geometry\ncan be approximated as close to plane-parallel gives the temperature\nstructure of the neutron star atmosphere as a function of radius,\n$T_{atm} \\approx 1.01 \\, \\textrm{MeV} \\, R_{\\nu,6}^{-1\/3}\nL_{\\nu,51}^{1\/6}\\epsilon_{\\nu,MeV}^{1\/3} \\left(y_\\nu\/y\\right)^{1\/3} $,\nwhere $L_{\\nu,51}$ and $\\epsilon_{\\nu,MeV}$ are the electron neutrino\nluminosity and average neutrino energy at the neutrino sphere in units\nof $10^{51} \\, \\textrm{ergs s}^{-1}$ and MeV, respectively. The\ngravitational redshift factor is $y = \\sqrt{1-2GM_{NS}\/r c^2}$ which, \nwhen evaluated at the neutrino sphere, $R_\\nu$, is $y_\\nu$. Notice that\nthe only dependence on radius is carried in the redshift factor, so that\nthe atmosphere is close to isothermal.\n \nAt the radius, $r_c$, where the pressure in the envelope becomes\nradiation dominated, the material becomes unstable to outflow\n\\citep{Salpeter81}. Since the neutrino luminosity is significantly\nlower than the neutrino Eddington luminosity, a thermally driven wind\nresults \\citep{Duncan86}. The density at which this wind begins can\nbe found approximately by equating the radiation pressure to the\nbaryonic pressure. This results in a critical density, $ \\rho_c\n\\approx \\sci{8.3}{7} \\, \\textrm{g} \\, \\textrm{cm}^{-3} \\,\nR_{\\nu,6}^{-1} L_{\\nu,51}^{1\/2}\\epsilon_{\\nu,MeV}\n\\left(y_\\nu\/y\\right), $ at which significant outflow begins and the\nkinetic equilibrium of weak interactions ceases to hold. Under these\nconditions, nuclear statistical equilibrium is maintained on a time\nscale much shorter than the dynamical time scale and, for these\ntemperatures and densities, there will be no bound nuclei present.\nSince the electron fraction is set by kinetic equilibrium, the\ncomposition of the wind does not depend on any previous nuclear\nprocessing, so any nucleosynthesis from the wind will be primary.\n\nAssuming that most neutrino heating occurs near $r_c$, the entropy is\nconstant once the temperature cools to the nucleon recombination\ntemperature, $kT \\approx 0.5$ MeV. Therefore, the final nuclear\nabundances in the wind depend mainly on the wind entropy, electron\nfraction, and the dynamical timescale at the radius where alpha \ncombination occurs \\citep{Qian96,Hoffman97}. To\ndetermine the contribution of the wind to the nucleosynthesis of the\nentire supernova, the mass loss rate must also be known. Estimates\nfor these quantities are given in the Appendix along with a discussion\nof the effect of general relativistic corrections.\n\nIntegrating the mass loss rate (equation \\ref{eq:mdot}) for a typical\nneutrino luminosity history implies that the wind will eject\napproximately $10^{-3} \\, M_\\odot$ of material. This in turn means\nthat for the wind to contribute to the integrated yields of the\nsupernova for a particular isotope, that isotope needs to overproduced\nrelative to its solar mass fraction by a factor of at least $10^5$ in\nthe wind, assuming the rest of the supernova ejects $\\sim10 \\, M_\\odot$ and has\nover production factors of its most abundant metals of order 10.\n\nAt early times in the wind, the PNS is losing lepton number\n\\citep[e.g.][]{Burrows86}. Neutrino interactions in the wind then tend to\nincrease the lepton number of the wind, and, to maintain charge\nneutrality, cause the wind to become proton rich. Under these\nconditions, the wind may synthesize some of the light p-process\nelements via the so called $\\nu$p-process \\citep{Frohlich06,Pruet06}.\n\n\\begin{figure*}\n\\begin{center}\n\\includegraphics[scale=0.7]{fig1.eps}\n\\end{center}\n\\caption{Neutrino two-color plot produced using the analytic relations\n in the Appendix. A neutron star with gravitational mass 1.4\n $M_\\odot$ \\ has been assumed with a neutrinosphere radius of 10 km.\n The total neutrino luminosity is assumed to scale as $L_{\\nu_e,tot}\n = 10^{51} (\\langle T_{\\nu} \\rangle\/ 3.5 \\, \\textrm{MeV})^4 \\,\n \\textrm{erg} \\textrm{s}^{-1}$. This luminosity is split between\n neutrinos and anti-neutrinos so as to ensure that the net\n deleptonization rate of the PNS is zero. The thick black line\n corresponds to an electron fraction of $Y_e = 0.5$. Above this\n line, neutron-rich conditions obtain and below it the matter is\n proton-rich. The white region is where there no free neutrons\n remain after charged particle reactions cease. The N = 50 (tan)\n region corresponds to final neutron-to-seed ratios between 0.01 and\n 15. The ``first peak'' (yellow) region corresponds to a neutron-to-seed\n ratio between 15 and 70, and the ``second peak'' (orange) region is where\n the neutron-to-seed ratio is greater than 70. The dashed lines\n correspond to the base ten logarithm of the mass loss rate in solar\n masses per second. }\n\\label{fig:tcp_base}\n\\end{figure*}\n\nAfter the initial deleptonization burst though, the net lepton number\ncarried by the wind will be small. Since the anti-electron\nneutrinosphere sits deeper in the PNS than the electron\nneutrinosphere, the electron neutrinos will be cooler than the\nelectron anti-neutrinos \\citep{Woosley94}. If this asymmetry is large\nenough, the wind can become neutron rich at later times. Combined\nwith an $\\alpha$-rich freeze out, this can give conditions favorable\nfor the r-process \\citep{Woosley92}.\n\nIn both cases, the resulting nucleosynthesis is characterized by the\nintegrated neutron to seed ratio after charged particle reactions\nfreeze out. For proton-rich winds, alpha-particles recombine into\n$^{12}$C by the standard triple alpha reaction and then alpha-capture\nand proton-capture reactions carry the nuclear flow up to\napproximately mass 60 \\citep{Woosley92}. The slowest reaction in this\nsequence is $^4$He($2\\alpha$,$\\gamma$)$^{12}$C, so the total number of\nseed nuclei produced is equal to the number of $^{12}$C nuclei\nproduced. When only free protons are present, the integrated neutron density is\ndetermined by the rate of anti-neutrino capture on free protons. An\nestimate for the neutron to seed ratio for proton-rich winds is given\nby equation \\ref{eq:nsp}. A neutron to seed ratio of only a few is\nrequired to bypass the few long-lived waiting points which hinder\nproduction of the light p-isotopes \\citep{Pruet06}. Still, it is\nchallenging to produce even a small neutron to seed ratio, since the\ndynamical time scale of the wind is short compared to the anti-neutrino\ncapture time scale.\n\nIn the neutron-rich case, seed nuclei are produced by a different\nreaction sequence $^4$He($\\alpha$n,$\\gamma$)$^9$Be($\\alpha$,n)$^{12}$C\n\\citep{Woosley92}. For the conditions encountered in the wind, the\nneutron catalyzed triple-alpha reaction proceeds about ten times as\nquickly as $^4$He($2\\alpha$,$\\gamma$)$^{12}$C. This increases the\nseed number compared with that obtained in a proton-rich wind with\nsimilar dynamical properties. Also, since there are free neutrons,\ncapture can proceed up to the N=50 closed shell isotopes $^{88}$Sr,\n$^{89}$Y, and $^{90}$Zr. Here, the neutron density is just determined\nby the number of free neutrons left after charged particle reactions\nfreeze out. The neutron to seed ratio in neutron-rich winds can be\napproximated using equation \\ref{eq:nsn}. Charged particle reactions\ncontinue up to the N=50 closed shell, at which point it becomes\nunfavorable to capture alpha particles due to small separation\nenergies and large coulomb barriers \\citep{Hoffman96}. If neutrons are exhausted during\nthis process, the wind will mainly produce the isotopes $^{88}$Sr,\n$^{89}$Y, and $^{90}$Zr \\citep{Hoffman97}. This happens when the\ncondition\n\\begin{equation} \n\\label{eq:n50_prdod} \n\\frac{\\bar Z}{\\bar A} \\approx 0.42-0.49 = \\frac{Y_e\n f_\\alpha}{2Y_e(f_\\alpha-1) + 1}\n\\end{equation} \nis met. Here, $f_\\alpha \\approx 14 Y_s\/Y_{\\alpha,i}$ is the fraction\nof the initial helium abundance that gets processed into heavy nuclei.\nNeutron to seed ratios of approximately 30 and 110 are required to\nproduce first and second peak r-process nucleosynthesis, respectively.\n\nUsing the analytic results for the wind dynamics and nucleosynthesis\ngiven in the Appendix (equations \\ref{eq:rcrit}, \\ref{eq:ent},\n\\ref{eq:mdot}, \\ref{eq:tau},\\ref{eq:ye}, \\ref{eq:nsp}, \\ref{eq:nsn},\nand using the neutrino interaction rates given in\n\\S\\ref{sec:neutrino_rates} to fix the thermodynamic state at $r_c$),\none can easily explore the neutrino temperature parameter space to\ndetermine the neutrino temperatures and fluxes that are most conducive\nto the r-process or the production of the light p-process. Figure\n\\ref{fig:tcp_base} is a neutrino two-color plot where it is assumed\nthat the deleptonization rate is zero and that the neutrino luminosity\nscales with the temperature to the fourth power ($L_{\\nu_e,tot} =\n10^{51} (\\langle T_{\\nu} \\rangle\/ 3.5 \\, \\textrm{MeV})^4 \\,\n\\textrm{erg} \\textrm{s}^{-1}$). The different nucleosynthetic regions\nare delineated by the final calculated neutron to seed ratio. To give\na feeling for how a particular point in parameter space might\ncontribute to the integrated nucleosynthesis of the wind, the mass\nloss rate is also shown.\n\nFor a significant amount of material to move past the N = 50 closed\nshell during neutron-rich conditions, the anti-neutrino temperature\nmust be approximately $60$\\% higher than the neutrino temperature.\nFor second peak r-process nucleosynthesis to occur, the asymmetry must\nbe greater than $100$\\%. Modern PNS cooling calculations do not give\nsuch large asymmetries \\citep{Pons99,Huedepohl09}.\n\nUnder proton-rich conditions, only a small region of the\nparameter space at high neutrino and low anti-neutrino temperature is\nfavorable for the $\\nu$p-process. There will be a small amount of\nneutron production in the white region, but it is unlikely that\nsignificant production of the light p-process elements $^{74}$Se,\n$^{78}$Kr, $^{84}$Sr, and $^{92}$Mo will occur. The region in\nneutrino temperature space where there is significant neutron\nproduction is unlikely to be reached. This region is small due to the\nshort dynamical time scale of the wind, which reduces the time over\nwhich anti-neutrinos can capture on free neutrons. One should note\nthat, very soon after shock formation in the supernova, a wind\nsolution may not be appropriate and material will be entrained closer\nto the PNS for a longer period of time. This scenario would be similar\nto the the conditions used in \\cite{Pruet06}.\n \nTherefore, based upon simple principles, it seems unlikely that the\nstandard wind scenario will produce r-process or light p-process\nisotopes in solar ratios, as is required by observations of metal poor\nhalo stars \\citep{Sneden96}. This same conclusion has been reached by\nother authors \\citep{Hoffman97,Thompson01}, but is repeated\nhere in simple terms. We will find that our numerical calculations\ngive similar results and that there is no significant r-process\nnucleosynthesis associated with the wind. Still, the wind can produce \nsome isotopes that may have an observable signature. For standard PNS \nluminosities, the wind will spend a significant amount of time in the \nregion of parameter space were N = 50 closed shell nucleosynthesis occurs. \n\n\n\n\n\\section{Computational Method}\n\\label{computational_method}\n\nTo more accurately investigate the integrated nucleosynthesis of the\nNDW, we have updated the implicit Lagrangian hydrodynamics code Kepler\n\\citep{Weaver78,Woosley02} to carry out time-dependent simulations of\nthe wind dynamics and nucleosynthesis. Kepler has been used\npreviously to study time-independent winds\\citep{Qian96}, but the weak\nand nuclear physics employed there was rudimentary and nucleosynthesis\nwas not tracked. Trajectories from Kepler were used for post-processing\ncalculations of nucleosynthesis in \\cite{Hoffman97}.\n\nKepler solves the non-relativistic hydrodynamic equations in\nLagrangian coordinates assuming spherical symmetry. First order\ngeneral relativistic corrections are included in the gravitational\nforce law (cf. \\cite{Shapiro83}). All order $v\/c$ effects are\nneglected. This is justified since the maximum wind speeds\nencountered are, at most, a few percent of the speed of light. The\nmomentum equation is then \n\\begin{equation} \n\\td{v_r}{t} = -4 \\pi r^2 \\pd{P}{m} - \\frac{Gm}{r^2}\\left( 1 +\n\\frac{P}{\\rho c^2} + \\frac{4 \\pi P r^3}{m c^2} \\right) \\left(1 -\n\\frac{2 G m}{r c^2} \\right)^{-1}\n\\end{equation}\nwhere the symbols have their standard meanings. As has been shown by\nprevious studies \\citep{Qian96,Cardall97,Otsuki00,Thompson01}, general\nrelativistic corrections to the gravitational force can have an\nappreciable effect on the entropy and dynamical time scale of the\nwind. The equation of state includes a Boltzmann gas of nucleons and\nnuclei, an arbitrarily relativistic and degenerate ideal electron gas,\nand photons.\n\n\\subsection{Weak Interaction Physics}\n\\label{sec:neutrino_rates}\n\nEnergy deposition from electron neutrino capture on nucleons, neutrino\nannihilation of all neutrino flavors, and neutrino scattering of \nall flavors on electrons is included in the\ntotal neutrino heating rate. Neutrino ``transport'' is calculated in the \nlight-bulb approximation. The energy deposition rate is dominated\nby neutrino captures on nucleons. The neutrino annihilation rates\ngiven in \\cite{Janka91} are employed. For the scattering rates, the\nrates given in \\cite{Qian96} are used, but we include general\nrelativistic corrections. Standard neutrino capture rates are\nemployed in the limit of infinitely heavy nucleons with first order\ncorrections. In this limit, the cross section is \n(Y.Z. Qian, private communication)\n\\begin{equation}\n \\sigma_{ \\nu n \\atop \\bar \\nu p} =\\frac{G_F^2 cos^2(\\theta_C)}{ \\pi\n (\\hbar c)^4}\\left[g_V^2+3g_A^2 \\right] \\left(\\epsilon_{\\nu} \\pm\n \\Delta \\right)^2\\left(1 \\pm W_{M, {\\nu \\atop {\\bar \\nu}}}\n \\epsilon_{\\nu}\\right) \n\\end{equation} \nHere, $G_F$ is the Fermi coupling constant, $\\theta_C$ is the Cabibo angle,\n$g_V$ and $g_A$ are the dimensionless vector and axial-vector coupling \nconstants for nucleons, $\\Delta$ is the proton neutron mass difference, \n$\\epsilon_\\nu$ is the neutrino energy, and $W_{M, {\\nu \\atop {\\bar \\nu}}}$ \naccounts for the weak magnetism\nand recoil corrections to the neutrino-nucleon cross section when the\nbase cross section is derived in the limit of infinitely heavy\nnucleons \\citep{Horowitz02}. This correction reduces the\nanti-neutrino cross section and increases the neutrino cross section\n(by about a total of 10\\% at the energies encountered in NDWs), which,\nfor a given incident neutrino spectrum, significantly increases the\nasymptotic electron fraction. Assuming a thermal distribution, these\ncross sections result in the neutrino energy deposition rate for\nanti-electron neutrino capture \n\\begin{equation} \n\\begin{array}{rl}\n \\dot q_{\\bar \\nu p}=&\\sci{4.2}{18} \\textrm{ergs\n s}^{-1}\\textrm{g}^{-1}\\frac{Y_p L_{\\bar \\nu,51}}{ \\langle\n \\mu\\rangle r_6^2} \\\\ \\times & \\biggl[-W_M^{\\bar\\nu\n p}\\frac{\\langle \\epsilon_{\\bar \\nu}^4 \\rangle }{\\langle\n \\epsilon_{\\bar \\nu} \\rangle } + (1 + 2W_M^{\\bar\\nu p} \\Delta)\n \\frac{\\langle \\epsilon_{\\bar \\nu}^3 \\rangle }{\\langle \\epsilon_{\\bar\\nu}\n \\rangle } \\\\ & - (2 \\Delta + W_M^{\\bar\\nu p} \\Delta^2\n )\\frac{\\langle \\epsilon_{\\bar \\nu}^2 \\rangle }{\\langle \\epsilon_{\\bar \\nu}\n \\rangle } + \\Delta^2 \\biggr]\n\\end{array}\n\\end{equation} \nand a similar expression for electron neutrino capture. The neutrino\nenergy distributions are parameterized by assuming a Fermi-Dirac\nspectrum. The neutrino energy averages, $\\avg{\\epsilon_\\nu^n}$, are\nevaluated using this distribution. The neutrino energy moments and\nluminosity are evaluated in the rest frame of the fluid. With general\nrelativistic corrections for the bending of null geodesics, the\naverage neutrino angle is given by\n\\begin{equation} \n\\langle \\mu \\rangle = \\frac{1}{2} +\n\\frac{1}{2}\\sqrt{1-\\left( \\frac{R_\\nu y_\\nu}{r y}\\right)^2}.\n\\end{equation} \nSpecial relativistic corrections are negligible in the regions where\nneutrino interactions are important.\n\nThe lepton capture rates used are calculated in the limit of\ninfinitely heavy nucleons. This results in a positron capture energy\nloss rate\n\\begin{equation}\n\\begin{array}{rl}\n\\dot q_{e^+ n} =&\\sci{6.9}{15}\\, \\textrm{ergs g}^{-1} \\, \\textrm{s}^{-1} \\, Y_n T_{10}^6 \\\\\n\\times & \\int_0^\\infty du f_{e}(u,-\\eta) \\left( u^5 + 3 \\delta u^4 + 3 \\delta^2 u^3 + \\delta^3 u^2 \\right)\n \\end{array}\n\\end{equation}\nhere $f_e(u,\\eta) = (\\exp(u-\\eta)+1)^{-1}$, $\\eta$ is the electron degeneracy parameter, \n$\\delta$ is the proton neutron mass difference divided by $k_bT$, and $Y_n$ is the neutron \nfraction. A similar rate is employed for electron capture.\n\nFor the neutrino losses, we include electron and positron capture on\nnucleons and include thermal losses as tabulated in \\cite{Itoh96}.\nThe energy loss rate in the wind is dominated by the electron\ncaptures.\n\n\\subsection{Nuclear Physics}\n\nDuring a hydrodynamic time step in Kepler, the nuclear energy generation\nrate and the changing nuclear composition are calculated\nusing a modified version of the 19-isotope network described in\n\\cite{Weaver78}. Neutrino and electron capture rates on nucleons are\ncoupled to the network, which are calculated under the same\nassumptions as the charged current energy deposition\/loss rates\ndescribed above. Therefore, non-equilibrium evolution of the electron\nfraction is accurately tracked.\n\nAlthough this network is appropriate for calculating energy generation\nthroughout the entire wind, it is not large enough to accurately track\nthe nucleosynthesis once alpha recombination begins at $ T \\approx\n0.5$ MeV. Therefore, for temperatures below $20 \\textrm{GK}$ an\nadaptive network is run alongside the hydrodynamics calculation. The\ndetails of this network can be found in \\cite{Woosley04} and \n\\cite{Rauscher02}. As a fluid\nelement passes the temperature threshold, the composition from the\n19-isotope network is mapped into the adaptive network. Typically,\nthe network contains approximately 2000 isotopes. Where available,\nexperimental nuclear reaction rates are employed, but the vast\nmajority of the rates employed in the network come from the\nstatistical model calculations of \n\\cite{Rauscher00}. In general, the nuclear physics employed in these\ncalculations is the same as that used in \\cite{Rauscher02}. The\nnucleon weak interaction rates employed in the 19-isotope network are\nalso used in the adaptive network.\n\n\n\\subsection{Problem Setup and Boundary Conditions}\n\nTo start the neutrino driven wind problem, an atmosphere of mass 0.01\n$M_\\odot$ is allowed to relax to hydrostatic equilibrium on top of a\nfixed inner boundary at the neutron stars radius. The mass enclosed\nby the inner boundary is the neutron star's mass. The photon\nluminosity from the neutron star is assumed to be nearly Eddington,\nbut we have found that the properties of the wind are insensitive to\nthe the luminosity boundary condition. Once hydrostatic equilibrium\nis achieved, the neutrino flux is turned on and a thermal wind forms.\nThis wind is allowed to relax to a quasi-steady state, and then the 19\nisotope network is turned on and the wind is, once again, allowed to\nreach a quasi-steady state. After this point, the neutrino flux is\nallowed to vary with time, and the adaptive network is turned on. \n\nAs the calculation proceeds, the mass of the envelope being followed\ndecreases and could eventually all be blown away. To prevent this,\nmass is added back to the innermost mass elements at a rate equal to\nthe mass loss rate in the wind. The mass added to a fluid element at\neach time step is a small fraction of its total mass. We find that\nmass recycling has no effect on the properties of the wind. It is\nsimply a way of treating a problem that is essentially Eulerian in a\nLagrangian code.\n\nFor most runs, a zero outer boundary pressure and temperature are \nassumed. To investigate the effect of a wind termination shock, a\ntime dependent outer boundary condition is included in some of \nthe simulations detailed below. The pressure \nof the radiation dominated region behind the supernova \nshock is approximately given by \\citep{Woosley02}\n\\begin{equation}\n\\label{eq:Pbound}\nP_{ps} \\approx \\frac{E_{sn}}{4 \\pi (v_{sn} t)^3}\n\\end{equation} \nwhere $E_{sn}$ is the explosion energy of the supernova, $v_{sn}$ is\nthe supernova shock velocity, and $t$ is the time elapsed since the \nshock was launched. As was discussed in \\cite{Arcones07}, this \nresults in a wind termination shock at a radius where the condition\n$\\rho_w v_w^2 + P_w \\approx P_{ps}$\nobtains, where $v_w$ is the wind velocity and $\\rho_w$ is the \nwind density. To avoid an accumulation of too many zones, \nmass elements are removed from the calculation once they \nexceed a radius of $10,000 \\, \\textrm{km}$. This is well \noutside the sonic point and nuclear burning has \nceased by this radius in all calculations .\n \n\\section{Numerical Results}\n\\label{results}\n\nTo survey both low and intermediate mass core collapse supernovae, \nneutrino emission\nhistories were taken from two core collapse calculations, one from a\n$20 M_\\odot$ \\citep{Woosley94} supernova calculation and the other \nfrom a $8.8 M_\\odot$ \\citep{Huedepohl09}\nsupernova calculation. Since the PNSs studied have significantly\ndifferent masses and neutrino emission characteristics, one is able to\nget a rough picture of how integrated nucleosynthesis in the NDW\nvaries with progenitor mass.\n\n\\subsection{Neutrino Driven Wind from a $20 M_\\odot$ Supernova}\n\n\\begin{figure}\n\\begin{center}\n\\leavevmode\n\\includegraphics[scale=0.45] {fig2.eps}\n\\end{center}\n \\caption{Neutrino luminosities and temperatures taken from the model\n of \\cite{Woosley94}. The top panel is the neutrino luminosities. The \n bottom panel is the average neutrino energies. The solid line corresponds \n to $\\nu_e$, the dashed line corresponds to $\\bar \\nu_e$, the dot-dashed \n line corresponds to $\\nu_{\\mu,\\tau}$.}\n \\label{fig:woos94_neut}\n\\end{figure}\n\nThe first set of neutrino luminosities and temperatures are taken from\n\\cite{Woosley94}. This calculation began with a $20 M_\\odot$ progenitor\nmeant to model the progenitor of 1987A \\citep{Woosley88}. The\nresulting neutron star had a gravitational mass of $1.4 M_\\odot$ and\nthe neutrino sphere was taken to be at 10 km. The neutrino\nluminosities and average energies as a function of time from this\nmodel are shown in figure \\ref{fig:woos94_neut}. After about 4\nseconds, the neutrino energies become constant and the large\ndifference between the electron neutrino and anti-neutrino energies\nimplies that the wind will be neutron rich. This supernova model had\nsome numerical deficiencies (Sam Dalhed, Private Communication). The\nentropy calculated for the wind in \\cite{Woosley94} ($S\/N_Ak \\approx 400$)\nwere unrealistically large due to some problems with the equation of\nstate. Here, that is not so important because the NDW is being\ncalculated separately, but this study did rely on older neutrino\ninteraction rates and did not include weak magnetism corrections (see \\S\n\\ref{sec:neutrino_rates}). Therefore, the results obtained using\nthese neutrino histories are only suggestive of what might happen in a\nmore massive star. If weak magnetism were taken into\naccount, the calculated electron and anti-electron neutrino\ntemperatures would probably be somewhat further apart.\n\n\\begin{figure}\n\\begin{center}\n\\leavevmode\n\\includegraphics[scale=0.5] {fig3.eps}\n\\end{center}\n\\caption{Wind structure after two seconds in the model using \nthe neutrino luminosities from \\cite{Woosley94}. The top panel \nshows the density in units of $10^8 \\, \\textrm{g} \\, \\textrm{cm}^{-3}$ \n(solid line) and the radial velocity in units of $10^3 \\, \\textrm{km} \\, \n\\textrm{s}^{-1}$ (dot-dashed line). The middle panel shows the net \nenergy deposition rate from weak and strong interactions in units of \n$10^{20} \\, \\textrm{erg} \\, \\textrm{g}^{-1} \\, \\textrm{s}^{-1}$ (dot-dashed line) \nand the entropy (solid line). The bottom panel shows the temperature \nin units of $\\sci{2}{9} \\, \\textrm{K}$ (solid line), the electron \nfraction (dot-dahsed line), and the fraction of material contained in \nnuclei (dotted line). }\n \\label{fig:wnd_struct}\n\\end{figure}\n\nThe calculation was run for a total of 18 seconds. During this time,\nthe mass loss rate decreased by almost three orders of magnitude while\na total mass of $\\sci{2}{-3} M_\\odot$ was lost in the wind. A\nsnapshot of the wind structure two seconds after bounce is shown in\nfigure \\ref{fig:wnd_struct}. Note that the wind velocity stays very\nsub-luminal throughout the calculation. Therefore, the neglect of\nspecial relativistic effects is reasonable. The secondary bump in the\nenergy deposition rate occurs at the same radius where nucleons and\nalpha-particles assemble into heavy nuclei. This increases the\nentropy by about 10 units. Clearly, the electron fraction is set\ninterior to were nuclei form. The radius where nuclei form is at a\nlarge enough value that the alpha effect \\citep{Fuller95} is not\nsignificant at early times in the wind. However, as the neutrino luminosity\ndecreases with time, nucleon recombination occurs at a smaller radius,\nand the alpha effect becomes increasingly important.\n\nThe time evolution of the wind as calculated by Kepler is shown in\nfigure \\ref{fig:woos94_wndprop}. The increase in asymptotic entropy is mainly\ndriven by the decrease in neutrino luminosity, since the average\nneutrino energies do not vary greatly. The analytic approximation\n(calculated using equation \\ref{eq:ent} and the neutrino interaction\nrates given in \\S\\ref{sec:neutrino_rates}) to the entropy tracks the\nentropy calculated in Kepler fairly well. This implies that the\nvariation in the neutrino luminosity with time does not significantly alter the\ndynamics from a steady state wind. In contrast to the high entropies\nreported in \\citet{Woosley94}, the entropy here never exceeds 130.\nFor the time scales and electron fractions also obtained, such a low\nvalue of entropy is not sufficient to give a strong r-process (see\nbelow).\n\nThe electron neutrino and anti-neutrino energies do move further apart\nas a function of time though, which causes the wind to evolve from\nproton-rich conditions at early times to neutron-rich conditions\nlater. A transition occurs from the synthesis of proton-rich isotopes\nvia the $\\nu p$-process at early times to the $\\alpha-$process mediated\nby the reaction sequence\n$\\alpha$($\\alpha$n,$\\gamma$)$^9$Be($\\alpha$,n)$^{12}$C later. The\nslight difference between the analytic approximation and the Kepler\ncalculation of $Y_e$ is due to the alpha effect \\citep{Fuller95}.\n\n\\begin{figure}\n\\begin{center}\n\\leavevmode\n\\includegraphics[scale=0.43] {fig4.eps}\n\\end{center}\n\\caption{Properties of the neutrino driven wind from the \n\\cite{Woosley94} supernova model as a function of time. The thick \nlines correspond to the numerical results from Kepler and the thin \nlines correspond to the predictions of the analytic estimates described \nin the appendix. The solid line is the dimensionless entropy per baryon, \nthe dashed line is the electron fraction, the dash dotted line is the \ndynamical timescale, and the dotted line is the mass loss rate. All \nof the quantities are taken extracted from where the wind temperature \nreaches 2 GK.}\n \\label{fig:woos94_wndprop}\n\\end{figure}\n\nIntegrated production factors for the wind are shown in figure\n\\ref{fig:WWsolopf}. The production factor for the species $i$ is\ndefined as\n\\begin{equation} \nP_i = \\frac{X_{i,w}M_{w}}{X_{i,\\odot}(M_{w}+M_{sn})},\n\\end{equation} \nwhere $X_{i,w}$ is the mass fraction of species $i$ in the wind\nafter all material has decayed to stable isotopes, $M_w$\nis the mass ejected in the wind, and $M_{sn}$ is the amount of mass\nejected by the entire supernova. $X_{i,\\odot}$ is the mass fraction\nof isotope $i$ in the sun for which the values of \\citet{Lodders03}\nwere used. The only isotopes that are co-produced in the wind\nalone are $^{87}$Rb, $^{88}$Sr, $^{89}$Y, and $^{90}$Zr, with\nproduction factor of $^{88}$Sr about a factor of 3 higher than the\nother two N = 50 closed shell isotopes. Before eight seconds, the\nproduction factors had been much closer. After eight seconds though,\nthe wind is dominated by $^{88}$Sr because $Y_e \\sim 0.45$ and only\n53\\% of alpha particles are free after freeze out which puts\n$\\frac{\\bar Z}{\\bar A}\\approx 0.41 $ of the heavy nuclei just\nbelow the range given in equation \\ref{eq:n50_prdod}. There are not\nenough free neutrons to make any significant amount of heavier nuclei,\nand this results in significant production of the stable N = 50 closed\nshell isotope with the lowest $\\frac{\\bar Z}{\\bar A}$.\n\n\\begin{figure}\n\\begin{center}\n\\leavevmode\n\\includegraphics[scale=0.65] {fig5.eps}\n\\end{center}\n\\caption{Isotopic production factors from the NDW model when \nthe neutrino luminosities from \\cite{Woosley94} are used. The production \nfactors are calculated assuming that $18.4 \\, M_\\odot$ of material \nwas ejected in the supernova in addition to the wind. The top dashed line\ncorresponds to the greatest production factor in the wind, the solid line is a\nfactor of two below that, and the bottom dashed line is a factor of two below the\nsolid line. These lines specify an approximate coproduction band for the wind alone.}\n \\label{fig:WWsolopf}\n\\end{figure}\n\n\\begin{figure}\n\\begin{center}\n\\leavevmode\n\\includegraphics[scale=0.42] {fig6.eps}\n\\end{center}\n \\caption{Neutrino two-color plot when the anti-neutrino luminosity \n is 1.2 times neutrino luminosity, and the total luminosity scales \n with average temperature to the fourth. Similar to figure \n \\ref{fig:tcp_base}. The red line is the neutrino temperatures \n from \\cite{Woosley94}.}\n \\label{fig:tcp_woos}\n\\end{figure}\n\n\\begin{figure}\n\\begin{center}\n\\leavevmode\n\\includegraphics[scale=0.65] {fig7.eps}\n\\end{center}\n\\caption{Isotopic production factors from the NDW model \nemploying the neutrino luminosities from \\cite{Woosley94} with \nthe anti-electron neutrino temperature reduced by $15\\%$. The \nproduction factors are calculated assuming that $18.4 \\, M_\\odot$ \nof material was ejected in the supernova in addition to the wind.\nThe horizontal lines are similar to those in figure \\ref{fig:WWsolopf}.}\n \\label{fig:WWredpf}\n\\end{figure}\n\n\\begin{figure}\n\\begin{center}\n\\leavevmode\n\\includegraphics[scale=0.65] {fig8.eps}\n\\end{center}\n\\caption{Isotopic production factors from the NDW model \nemploying the neutrino luminosities from \\cite{Woosley94} \nwith weak magnetism corrections turned off. The production \nfactors are calculated assuming that $18.4 \\, M_\\odot$ of material \nwas ejected in the supernova in addition to the wind.\nThe horizontal lines are similar to those in figure \\ref{fig:WWsolopf}.}\n \\label{fig:WWnowmpf}\n\\end{figure}\n\n\\begin{figure}\n\\begin{center}\n\\leavevmode\n\\includegraphics[scale=0.65] {fig9.eps}\n\\end{center}\n\\caption{Isotopic production factors from the NDW model when \nthe neutrino luminosities from \\cite{Woosley94} are used and an external boundary \npressure is specified as described in the text, which results in a wind termination\nshock. The production \nfactors are calculated assuming that $18.4 \\, M_\\odot$ of material \nwas ejected in the supernova in addition to the wind. The horizontal lines are \nsimilar to those in figure \\ref{fig:WWsolopf}.}\n \\label{fig:WWpbd}\n\\end{figure}\n\n\nDuring the first four seconds, the wind is proton rich and the isotopes\n$^{69}$Ga, $^{70,72}$Ge, $^{74,76}$Se, and $^{78,80,82}$Kr \nare produced by proton captures on seed nuclei produced by the triple-alpha \nreaction and subsequent ($\\alpha$,p) reactions. Although the mass \nloss rate is much higher when the wind \nis proton rich, the alpha-fraction freezes out at 98\\% of its initial value, \nwhich results in significantly decreased production of heavy nuclei. \nThe difference in final alpha fraction between the neutron- and proton-rich \nphases of the wind is due mainly to the difference in speed of the reaction \nchains $\\alpha$($2\\alpha$,$\\gamma$)$^{12}$C and\n$\\alpha$($\\alpha$n,$\\gamma$)$^9$Be($\\alpha$,n)$^{12}$C, but also \nto the decreased entropy at early times.\n\nWe can compare this with the analytic predictions for nucleosynthesis\nby plotting the neutrino temperature evolution from this model on a\nneutrino ``two-color plot'' (figure \\ref{fig:tcp_woos}). Here we have\nset $L_{\\bar \\nu_e} = 1.2 L_\\nu$ which is approximately correct at\nlate times in the calculation of \\cite{Woosley94}. The wind never\nreaches a region in which r-process nucleosynthesis is expected, but\nspends a significant amount of time making nuclei in the N = 50 closed\nshell isotones.\n\n\\subsubsection{Variations in Neutrino Properties}\nSince the neutrino temperatures from the original model were\nuncertain, several other models were calculated. One had a reduced (by $15\\%$)\nelectron antineutrino temperature; another had the weak\nmagnetism corrections to the neutrino interaction rates turned off. A\nsmaller antineutrino temperature is more in line with recent\ncalculations of PNS cooling \\citep{Pons99,Keil03}. Because the\nmodel of \\cite{Woosley94} did not include weak magnetism corrections,\nour model with weak magnetism corrections turned off is more\nconsistent with the original supernova model.\n\nThe production factors for the model with a reduced electron\nantineutrino temperature are shown in figure \\ref{fig:WWredpf}. The\nyield of $^{88}$Sr is reduced by almost a factor of ten from the base\ncase, while the production factors of $^{89}$Y and $^{90}$Zr are\nreduced by a factor of three. In this case, the wind also produces the\nproton-rich isotopes $^{74}$Se, $^{78}$Kr, and $^{84}$Sr. The\ncoproduction line for lighter elements like oxygen in a $20 M_\\odot$\nsupernova at solar metallicity is around 18, so the wind could contribute\nto the total nucleosynthesis if the antineutrino temperature was\nreduced, but its contribution would be small. \n\nThe yields when weak magnetism corrections are ignored are shown in\nfigure \\ref{fig:WWnowmpf}. Without weak magnetism, the electron\nfraction drops below 0.4 at late times when the entropy is fairly\nhigh. Equation \\ref{eq:n50_prdod} is no longer satisfied and material\nmoves past the N = 50 closed shell towards A $\\approx$ 110. Some\nr-process isotopes are produced, such as $^{96}$Zr and $^{100}$Mo, but\nnot anywhere near solar ratios, and no material reaches the first\nr-process peak.\n\n\\begin{figure*}\n\\begin{center}\n\\leavevmode\n\\includegraphics[scale=0.75] {fig10.eps}\n\\end{center}\n\\caption{Combined isotopic production factors of the neutrino \ndriven wind with unaltered neutrino temperatures and including weak magnetism\ncorrections added to those of a $20 M_\\odot$ stellar model from \\cite{Woosley95}. \nThe solid black line is the coproduction line with $^{16}$O. \nThe dashed lines are a factor of two above and below the coproduction line. The \nneutrino driven wind is responsible for the production of $^{88}$Sr, \n$^{89}$Y, and $^{90}$Zr.}\n \\label{fig:comb_pf}\n\\end{figure*}\n\n\\subsubsection{Effect of a Wind Termination Shock}\nTo investigate the possible effect of a wind termination shock on nucleosynthesis,\nanother model was run with a boundary pressure and temperature \ndetermined by equation \\ref{eq:Pbound}. An explosion energy of \n$10^{51} \\, \\textrm{erg}$ was assumed and the shock velocity was taken as\n$\\sci{2}{9} \\, \\textrm{cm s}^{-1}$. This resulted in a wind termination\nshock that was always at a radius greater than $10^3 \\, \\textrm{km}$. \nThe production factors from this model are shown in figure \n\\ref{fig:WWpbd}. Similar to the simulation without a wind termination\nshock, the N=50 closed shell elements dominate the wind's nucleosynthesis.\n\n\n\nThe main difference between the case with and without a wind termination \nshock is a shift in the mass of isotopes produced during the proton-rich\nphase. This can be seen in the increased production of Mo. \nDuring this phase, the post shock temperature varied from \n2.5 GK down to 0.8 GK and the density varied from\n$\\sci{5}{4} \\, \\textrm{g cm}^{-3}$ to $\\sci{5}{2} \\, \\textrm{g cm}^{-3}$. \nThese conditions are very favorable for \ncontinued proton capture once the long lived waiting point isotopes \n$^{56}$Ni and $^{64}$Ge are bypassed by (n,p) reactions. Because \nthese conditions persist for at least a second after a fluid element passes\nthrough the wind termination shock, significantly more proton captures \ncan occur on seed nuclei that have moved past mass $\\sim 64$ relative\nto the case with no termination shock. Still, not many more neutrons \nare produced per seed nucleus relative to the base run. Therefore, the net \nnumber of seeds that get past the long lived waiting points remains small\nand the proton-rich wind does not contribute to the integrated nucleosynthesis. \nIt should also be noted that a different treatment of the wind's \ninteraction with the supernova shock might result in a breeze solution \nwhich may supply more favorable conditions for $\\nu$p-process \nnucleosynthesis \\citep{Wanajo06}. \n\n\\subsubsection{Total Supernova Yields}\n\n\nIn figure \\ref{fig:comb_pf}, the production factors from a $20\nM_\\odot$ supernova model from \\cite{Woosley95} have been combined with\nthe production factors we calculated in the NDW with the unaltered\nneutrino histories of \\citep{Woosley94} with weak magnetism corrections\nincluded. The wind could be\nresponsible for synthesizing the isotopes $^{87}$Rb, $^{88}$Sr,\n$^{89}$Y, and $^{90}$Zr. $^{88}$Sr production is above the\nco-production band, but the rest are in agreement with the stellar\nyields. This overproduction of $^{88}$Sr is similar to the result of\n\\cite{Hoffman97}.\n\nFor the model with a reduced anti-electron neutrino temperature\ncombined with the yields from the $20 M_\\odot$ supernova model, the\nwind contributes 28\\%, 42\\%, 35\\%, 75\\%, 75\\%, and 80\\% of the total\n$^{74}$Se, $^{78}$Kr, $^{84}$Sr, $^{88}$Sr, $^{89}$Y, and $^{90}$Zr\nabundances in the supernova, respectively. This wind model does not result in any\nisotopes being overproduced relative to the rest of the yields of the\nsupernova. For the case with weak magnetism turned off, the nuclei\nproduced by the wind are overproduced relative to those made in the rest of \nthe star by factor of nearly 100, hence this\nwould need to be a very rare event if this model were realistic.\n\nClearly, weak magnetism corrections and variations in the neutrino\ntemperatures have a very significant effect on nucleosynthesis in the\nwind. Aside from the effects of an extra source of energy\n(\\ref{modifications}), the neutrino spectra are the largest current\ntheoretical uncertainty in models of the NDW.\n\n\\subsection{Neutrino Driven Wind from a $8.8 M_\\odot$ Supernova}\n\\label{eight_results}\n\n\\begin{figure}\n\\begin{center}\n\\leavevmode\n\\includegraphics[scale=0.45] {fig11.eps}\n\\end{center}\n \\caption{Neutrino luminosities and temperatures taken from the model \n of \\cite{Huedepohl09}. The line styles are the same as in figure \n \\ref{fig:woos94_neut}. }\n \\label{fig:jnk_neut}\n\\end{figure}\n\nThe second PNS model is a more modern one-dimensional calculation of an\nelectron-capture supernova \\citep{Huedepohl09} that started from an\n$8.8 M_\\odot$ progenitor model \\citep{Nomoto84}. This resulted in a\nPNS with a gravitational mass of $1.27 M_\\odot$ and a radius of 15 km.\nTogether the lower mass and increased radius imply a lower\ngravitational potential at the neutrinosphere. This work employed\nneutrino interaction rates which took weak magnetism and ``in-medium''\neffects into account. The neutrino luminosities and average energies\nas a function of time are shown in figure \\ref{fig:jnk_neut}. The\nmaximum difference between the electron and anti-electron neutrino\naverage energies is significantly less than in the model of\n\\cite{Woosley94}. This is likely due in part to both the decreased\ngravitational potential of the PNS and the more accurate neutrino\ninteraction rates in the newer model.\n\nThe calculation was run for a total of nine seconds, at which point\nthe mass loss rate had dropped by two orders of magnitude. The total\namount of mass ejected in the wind was $\\sci{3.8}{-4} \\, M_\\odot$. In\nfigure \\ref{fig:jnk_wndprop}, the properties of the NDW calculated\nusing Kepler are plotted as a function of time. Notice that the\nentropy never reaches above 100 in this model, which diminishes the\nlikelihood of significant nucleosynthesis. For comparison, we also\ninclude the analytic estimates detailed above. There is reasonable\nagreement between the analytic and the numerical calculations, but not\nnearly as good as in the $20 M_\\odot$ model.\n\n\\begin{figure}\n\\begin{center}\n\\leavevmode\n\\includegraphics[scale=0.45] {fig12.eps}\n\\end{center}\n\\caption{Properties of the neutrino driven wind from the \n\\cite{Huedepohl09} supernova model as a function of time. \nThe lines have the same meaning as in figure \\ref{fig:woos94_wndprop}.}\n \\label{fig:jnk_wndprop}\n\\end{figure}\n\n\nIn contrast to the simulation run with the neutrino luminosities of\n\\citet{Woosley94}, the electron fraction continues to increase with\ntime. The difference between the average electron neutrino energy and\nelectron anti-neutrino energy is, at most, about 3 MeV, compared to a\nmaximum of 8 MeV in the \\citet{Woosley94} calculations. Also, the\ndifference between the average neutrino energies decreases as a\nfunction of time, compared to an increase with time in\n\\citet{Woosley94}. Finally, the energies of all kinds of neutrinos\nare lower in the \\citet{Huedepohl09} calculation, so that the\nproton-neutron rest mass difference significantly suppresses the\nanti-neutrino capture rate relative to the neutrino capture rate.\nThese differences are presumably due to both the different neutron\nstar masses and neutrino interaction rates employed.\n\n\\begin{figure}\n\\begin{center}\n\\leavevmode\n\\includegraphics[scale=0.65] {fig13.eps}\n\\end{center}\n\\caption{Isotopic production factors from the NDW model employing \nthe neutrino luminosities from \\cite{Huedepohl09}. The production \nfactors are calculated assuming that $7.4 \\, M_\\odot$ of material was \nejected in the supernova in addition to the wind. \nThe horizontal lines are similar to those in figure \\ref{fig:WWsolopf}.\nNotice that none of the production factors are significantly greater \nthan one.}\n \\label{fig:jnkpf}\n\\end{figure}\n\nThe conditions in this model thus preclude {\\sl any} r-process\nnucleosynthesis, but they are potentially favorable for production of\nsome low mass p-process isotopes by the $\\nu$p-process. The\nintegrated isotopic production factors are shown in figure\n\\ref{fig:jnkpf}. The total ejected mass was take as 7.4 $M_\\odot$, as\n1.4 $M_\\odot$ neutron star is left behind in the calculation of\n\\cite{Huedepohl09}. During the calculation a maximum network size of\n988 isotopes is reached. The p-process elements $^{74}$Se and\n$^{78}$Kr are co-produced with $^{63}$Cu, $^{67}$Zn, and $^{69}$Ga,\nbut the maximum production factor for any isotope is 1 when weighted\nwith the total mass ejected in the supernova. Therefore, in this\nsimple model, the proton-rich wind from low mass neutron stars \nwill not contribute significantly to galactic chemical evolution.\n\nThe entropies encountered when the mass loss rate is high are low\n($\\sim 50$), so that there is more production of $^{56}$Ni by\ntriple-alpha and a subsequent $\\alpha$p-process. As the neutron\nabundance available for the $\\nu$p-process is given by\n\\begin{equation} \nY_n \\approx \\frac{\\lambda_\\nu Y_p}{\\rho N_A \\sum_i\nY_i \\avg{\\sigma v}_{i(n,p)j}}, \n\\end{equation} \nincreased seed production reduces the available neutron abundance and\ntherefore hinders production of the p-process elements $^{74}$Se,\n$^{78}$Kr, $^{84}$Sr, and $^{92}$Mo. Additionally, at early times,\nthe dynamical time scale is short which implies a smaller integrated neutron \nto seed ratio, $\\Delta_n$ (see the appendix). \n\nThe yields of from this model cannot be combined with the yields from \nthe rest of the supernova because they are not published. As \\cite{Nomoto84}\nhas discussed, the mass inside the helium burning shell was close to the \nmass of the neutron star that was left after the explosion. Therefore \nthe ejecta of the supernova is expected to have small production factors.\nThis implies that, even when the yields of the NDW are combined with the \nrest of the supernova, it is unlikely that these low mass core collapse\nsupernovae will contribute significantly to galactic chemical evolution.\n\n\\subsubsection{Effect of a Wind Termination Shock}\n\n\\begin{figure}\n\\begin{center}\n\\leavevmode\n\\includegraphics[scale=0.65] {fig14.eps}\n\\end{center}\n\\caption{Isotopic production factors from the NDW model employing \nthe neutrino luminosities from \\cite{Huedepohl09}, but including a time\ndependent external boundary pressure which results in a wind \ntermination shock. The production \nfactors are calculated assuming that $7.4 \\, M_\\odot$ of material was \nejected in the supernova in addition to the wind. \nThe horizontal lines are similar to those in figure \\ref{fig:WWsolopf}.\nNotice that the production factors are almost unchanged when an \nexternal boundary pressure is added.}\n \\label{fig:jnkpfpbd}\n\\end{figure}\n\n\nAs was mentioned above, it is very possible that a transonic wind solution \nmay not be appropriate this early in the supernovas evolution. \\cite{Fischer09} \nhave found that a wind termination shock is not present in a one-dimensional\nsupernova model using the progenitor from \\cite{Nomoto84}. Still, it is \ninteresting to consider the effect of a reverse shock on the wind nucleosynthesis.\n\nA second simulation was run with a time dependent boundary pressure given\nby equation \\ref{eq:Pbound}, with $E_{sn} = 10^{50} \\, \\textrm{erg}$ and \n$v_{sn} = \\sci{2}{9} \\, \\textrm{cm s}^{-1}$. This results in a wind termination\nshock at a radius of approximately $\\sci{3}{8} \\, \\textrm{cm}$ throughout the \nsimulation. Inside the wind termination shock the wind dynamics are very similar\nto those in the run with no boundary pressure. The production factors from \nthis model are shown in figure \\ref{fig:jnkpfpbd}. Clearly, there is almost no\ndifference in the nucleosynthesis in the runs with and without a wind termination\nshock.\n \nAfter $0.75 \\, \\textrm{s}$, the post shock temperature drops below 1 GK and \nthe wind termination shock has little effect on subsequent nucleosynthesis.\nBecause the post shock temperature is high for less than one second, the \nwind termination shock has very little effect on the integrated nucleosynthesis. \nA larger explosion energy would likely result in a larger effect on the nucleosynthesis, \nbut there are still very few neutrons available to bypass the long lived waiting points\nand it seems unlikely that the production factors would be increased by \nmore than a factor of a few.\n\n\\section{Discussion}\n\\label{discussion}\n\n\\subsection{Comparison with SN 1987A}\n\nSince the progenitor model used in \\cite{Woosley94} was a model for SN\n1987A, it is interesting to compare our predicted abundances with\nthose observed in the ejecta of that event. $^{88}$Sr, produced by\nthe NDW, dominates the elemental strontium yield when the results of\nthe 20 $M_\\odot$ wind model are combined with those predicted by\n\\cite{Woosley88}, who ignored the wind. For the base NDW model, [Sr\/Fe]$=\n0.8$, if weak magnetism corrections are neglected, [Sr\/Fe]$= 1.6$; and\nif the anti-neutrino temperature is reduced in the base model by 15\\%,\n[Sr\/Fe]$=0.2$.\n\n\\cite{Mazzali92} found [Sr\/Fe]$\\approx 0.3$ in the ejecta of SN 1987A\n2-3 weeks after the explosion. This observation has a substantial\nerror bar due to the uncertainty of modelling the spectrum of the\nexpanding ejecta. Of even greater concern is comparing of our models\nfor bulk yields with supernova photospheric abundances observed on a\ngiven day when the observations are probably not even probing the\ninnermost ejecta. Still, if one assumes that the [Sr\/Fe] ratio found\nby \\cite{Mazzali92} represents the value for all of the ejecta (for\ninstance by assuming that ``mixing'' was extremely efficient), a weak\nconstraint can be put on the neutrino fluxes and energies predicted by\nthe model of \\cite{Woosley94}. The NDW model with a reduced\nanti-neutrino temperature is much closer to the observed value of\n[Sr\/Fe] than the other two models. This suggests the anti-neutrino\ntemperature may have been overestimated in the original model, a\nchange that would be more consistent with more modern calculations of\nneutrino spectra formation in PNS atmospheres \\citep{Keil03}. But\nobviously, a meaningful constraint will require a more complete modeling\nof the multi-dimensional explosion and time-dependent spectrum of SN\n1987A.\n\n\n\\subsection{Strontium and Yttrium in Halo Stars} \n\nSince strontium and yttrium are abundantly produced in our models, it\nmay be that the NDW has contributed to their production throughout\ncosmic history. An interesting possibility is that the abundances of\nthese elements might trace the birth rate of neutron stars at an early\ntime. Taking a standard r-process abundance pattern from metal poor\nstars with strong r-process enhancments, \\cite{Travaglio04} find that\n8\\% and 18\\% of solar strontium and yttrium, respectively, are not\nproduced by either the ``standard'' r-process or any component of the\ns-process. It therefore seems plausible that charged particle\nreactions in the NDW could make up this ``missing'' component.\n\nAny nucleosynthesis that happens in the NDW will be primary,\ni.e. provided that the mass function of neutron stars at birth does\nnot itself scale with metallicity, similar nucleosynthesis will occur\nfor stars of any population. Below [Fe\/H]$\\sim -1.5$, no component of\nthe s-process contributes to the abundances of N = 50 closed shell\nisotopes \\citep{Serminato09}. If the NDW escapes the potential well\nof the PNS, and contributes to the galactic budget of N = 50 closed\nshell isotopes, it should provide a floor to [Sr\/Fe] and [Y\/Fe]. Based\nupon the arguments of \\cite{Travaglio04}, this floor would be at\n[Sr\/Fe]$\\approx-0.18$ and [Y\/Fe]$\\approx -0.16$. These numbers assume\nthat when the main r-process source contributes in addition to the NDW,\n[Sr\/Fe] and [Y\/Fe] approach their solar values even though the\ns-process has yet to contribute. This is consistent with\nobservations.\n\nIn defining this floor, one must assume that the abundances in a\nparticular star sample a large number of individual supernovae.\nThis is because the production of N=50 closed shell elements likely\ndepends on the PNS mass and therefore the progenitor mass. As we have\nfound, [Sr\/Fe]$=0.8$ in the $20 M_\\odot$ model with reduced\nanti-neutrino temperatures, but the $8.8 M_\\odot$ model produces no\nstrontium. Observations show that below [Fe\/H]$\\sim -3$, the spreads\nin [Sr\/Fe] and [Y\/Fe] increase significantly and the mean values\nfalloff some \\citep{Francois07,Cohen08,Lai08}. Single stars have\nvalues of [Sr\/Fe] below the predicted floor. This could be because, \nat this metallicity, the metals in a particular\nstar come from only a handful of supernovae.\n\nAnother possible explanation of this variation is that supernova fall\nback varies with metallicity. Since the NDW is the\ninnermost portion of the supernova ejecta, it will be the most\nsusceptible to fallback. It has been found that the amount of\nsupernova fallback depends strongly on the metallicity of the\nprogenitor, especially going between zero and low metallicity\n\\citep{Zhang08}. Additionally, mixing is also greatly reduced in zero\nmetallicity stars compared to solar metallicity stars due to the formers \ncompact structure \\citep{Joggerst09}. \n\nThe current understanding of supernova fallback suggests that the\nnucleosynthetic contribution of the NDW will be suppressed at very low\nmetallicity. Of course, the ejection of iron by the supernova is also\nvery susceptible to fallback, so the effect of fallback on the\nevolution of [Sr,Y\/Fe] is complicated and may require fine tuning to\ngive the observed decrease.\nA somewhat different explanation was offered by \\citet{Qian08} who\nattributed the fall off of [Sr\/Fe] at low metallicity to the evolution\nof the ``hypernova'' rate with metallicity. For their purposes,\nhypernovae were stellar explosions that contributed iron without\nmaking much strontium. \n\nGiven the sensitivity of strontium and yttrium yields to uncertain NDW\ncharacteristics, especially neutrino fluxes and temperatures, it may\nbe some time before the complex history of these elements is even\nqualitatively understood. It is likely though that their abundances in\nhalo stars will ultimately be powerful constraints upon the evolution\nof supernova physics as a function of metallicity.\n\n\\subsection{Possible Modifications of the Basic Model}\n\\label{modifications}\n\nAs is clear from figures \\ref{fig:WWsolopf} and \\ref{fig:jnkpf}, the\nsimplest case of a non-magnetic non-rotating NDW from a neutron star\nwithout additional energy deposition does not produce r-process nuclei in\nsignificant abundances. Are there extensions to this simple scenario\nthat {\\sl could} make the wind a site of the r-process?\n\nAs was pointed out by \\cite{Metzger07}, the combination of rotation\nand magnetic fields can decrease the dynamical time scale by magnetic\n``flinging''. This is not particularly effective. Adding a\nnon-thermal source of kinetic energy means that less thermal energy\nmust be put into the wind for it to escape the potential well.\nTherefore, lower entropies are achieved. It seems unlikely that this\nmechanism, by itself, will salvage the NDW as a site for the full\nr-process. If there were a way to make the rotation rate of the PNS\nhigh enough, it might be possible that there would be a centrifugally\ndriven outflow. Then the electron fraction would be determined by kinetic\nequilibrium much deeper in the PNS envelope, and the material in the\noutflow would have an electron fraction much lower than that seen in\nthe wind.\n\nTo test this possibility, we ran calculations with a centrifugal force\nterm added and corotation with the PNS enforced out to $10^3$ km.\nUnfortunately, for reasonable PNS spin rates (20 ms period), \nwe found this had little effect on the nucleosynthesis. These\ncalculations were in a regime were the electron fraction was still set\nby neutrino interactions.\n\nMany authors have discussed the possible effects of both\nmatter-enhanced\\citep{Qian95,Sigl95} and collective neutrino\n\\citep{Pastor02,Duan06} oscillations on NDW nucleosynthesis. If\nelectron antineutrinos could undergo a collective oscillation near\nthe launch radius while the electron neutrinos did not, this would\nincrease the average energy of the antineutrinos if the $\\mu$\nand $\\tau$ neutrinos have a significantly higher temperature,\nfacilitating a reduction in the electron fraction. For a normal mass hierarchy however,\nmatter enhanced neutrino oscillations would probably cause electron\nneutrino flavor conversion, which would {\\sl increase} the electron fraction\nand decrease the probability of significant r-process nucleosynthesis\n\\citep{Qian95}.\n\nCollective neutrino oscillations can cause antineutrino oscillations\nin the region were the electron fraction is set, and thereby decrease\nthe electron fraction where pure MSW oscillations would have predicted\nan increased electron fraction\\citep{Duan06}. Clearly, the main\neffect of oscillations would be on the composition of the wind, not\nthe dynamics. As can be seen in the neutrino two color plots,\noscillations would have to change the effective temperature of the\nanti-neutrinos by a very large amount to move from a region where N=50\nclose shell nucleosynthesis occurs to a region where the second\nr-process peak can be produced.\n\nThese effects are based upon the assumption that $\\mu$- and\n$\\tau$-neutrinos are significantly more energetic than the electron \nneutrinos. In the calculation\nof \\cite{Woosley94}, this is the case, as can be seen in figure\n\\ref{fig:woos94_neut}. Interestingly, the $\\mu$ and $\\tau$\ntemperatures are almost the same as the electron anti-neutrino\ntemperature in the \\cite{Huedepohl09} calculation, which can be seen\nin figure \\ref{fig:jnk_neut}. It is not clear wether this difference\nobtains because of the difference in the PNS masses or the\nsignificantly different neutrino physics employed in the calculations.\nA detailed study of neutrino transport in static backgrounds showed\nthat the inclusion of all relevant neutrino interactions brings the\naverage energies of the $\\mu$- and $\\tau$- neutrinos closer to the\ntemperature of the anti-electron neutrinos \\citep{Keil03}. Therefore\nit is uncertain wether or not neutrino oscillations could effect\nnucleosynthesis significantly. Clearly, the uncertainties here are\nnot in the wind itself but in the formation of the spectra in the PNS\nand the details of neutrino transport with neutrino oscillations.\n\nFinally, it has been suggested \\citep{Qian96,Nagataki05} that adding a\nsecondary source of volumetric energy deposition can significantly\nincrease the entropy of the wind, which results in a more alpha-rich\nfreeze out and conditions that would be more favorable for r-process\nnucleosynthesis. The addition of energy to the wind also decreases\nthe dynamical timescale. Since the important quantity to consider for\nthe r-process is $s^3\/\\tau_d$ \\citep{Hoffman97}, \nboth effects increase the chance of having a significant neutron to\nseed ratio after freeze out. If the NDW model is to be salvaged, this\nseems to us the minimal necessary extension. Of course, the physical\nprocess contributing this extra energy is very uncertain. One\npossibility is that oscillations of the PNS power sound waves\nwhich produce shocks and deposit energy in the wind, similar to the supernova\nmechanism of \\cite{Burrows06}, but smaller in magnitude. We will\nexplore this possibility in some detain in a subsequent paper.\n\nFrom a chemical evolution standpoint, it is important to consider what\neffect neutron star mergers will have on the evolution of the\nr-process abundances. It seems unavoidable that r-process nuclei will\nbe produced in the tidal tails ejected during these mergers\n\\citep{Freiburghaus99} and the amount of material ejected in these\nevents is approximately enough to account for the galactic inventory\nof r-process elements given the expected merger rate\n\\citep{Lattimer76,Rosswog99}.\n\nNeutron star mergers have been largely discounted because inferred\nmerger rates are small at low metallicity due to the long in spiral time\nand therefore they cannot account for the r-process enrichment seen in\nlow metallicity halo stars \\citep{Argast04}. Of course, the inferred\nmerger rates are very uncertain as are models for the early evolution\nof the Milky Way, so that both the conclusion that neutron star\nmergers can account for the r-process inventory of the galaxy and that\nthey are not consistent with producing the r-process at low\nmetallicity are very uncertain. Clearly, there is significant room\nfor more work in this area.\n\nTherefore, it seems reasonable that the galactic r-process abundances\ncould be accounted for by a combination of mergers and winds with an\nextra source of energy, either from an acoustically and\/or magnetically\nactive PNS. Since not every supernova will have the requisite\nconditions for an r-process, there will be significant variation in\nthe yields from single supernovae. This, along with the contribution\nfrom neutron star mergers, will give significant variation in the\n[r-process\/$\\alpha$-element] values found in single stars at low\nmetallicity but averaged over many stars these should track one\nanother, which is consistent with observations \\citep{Sneden08}.\n\n\\section{Conclusions}\n\nWe have performed calculations of the dynamics and nucleosynthesis in\ntime dependent neutrino driven winds. This was done for two sets of\nneutrino spectra calculated in one-dimensional supernova models taken\nfrom the literature. The nucleosynthesis in these models was compared\nwith supernova yields to determine if these models were consistent\nwith observations. Additionally, we compared the results of these\nnumerical models to analytic models of the neutrino driven wind and\nfound good agreement.\n\nSimilar to most of the work on the NDW after \\cite{Woosley94}, we find\nthat it is unlikely that the r-process occurs in the neutrino driven\nwind unless there is something that causes significant deviation from\na purely neutrino driven wind. Additionally, in the simplest case,\nthere is little production of p-process elements at early times in the\nwind. In our calculation that used spectra from a more massive\nneutron star, the wind only produces the N=50 closed shell elements\n$^{87}$Rb, $^{88}$Sr, $^{89}$Y, and $^{90}$Zr.\n\nThis result is sensitive to small changes in the neutrino interaction rates \n(i.e. the inclusion of weak magnetism) and changes to the neutrino temperature \nof order 10\\%. Comparing our models with the over abundance of strontium \nseen in SN 1987A suggests that the difference between the electron and \nanti-electron neutrino temperatures in the model of \\cite{Woosley94} \nmay have been to large. We also find that the effect of a wind termination\nshock on the wind nucleosynthesis is small.\n\nUsing neutrino spectra from an $8.8 M_\\odot$ supernova that drives a\nwind which is proton rich throughout its duration \\citep{Huedepohl09}, we\nfind that no significant $\\nu$p-process occurs and the wind does not\ncontribute to the yields of the supernova. The neutrino spectra from\nthis model are probably more accurate than the spectra from the model\nof \\cite{Woosley94}. We also investigated\nthe effect of an outer boundary pressure which resulted in a wind \ntermination shock. This had a negligible effect on the nucleosynthesis.\n\nHowever, one also expects that the nucleosynthesis in the NDW will\nvary considerably from event to event, especially with the mass and\npossibly the rotation rate of the PNS. The winds from more massive PNS\nhave greater entropy and might, in general, be expected to produce\nheavier elements and more of them. The neutrino spectral histories of\nPNS as a function of mass have yet to be determined over a wide \nrange of parameter space. Currently, the neutrino luminosities and \ntemperatures are the largest uncertainties in models of the NDW.\n\n\n\\begin{acknowledgements}\n\nWe would like to thank Alex Heger, David Lai, Enrico Ramirez-Ruiz,\nSanjay Reddy, and Yong-Zhong Qian for useful discussions about issues\nrelating to this work. L. R. was supported by an NNSA\/DOE Stewardship\nScience Graduate Fellowship (DE-FC52-08NA28752) and the University of\nCalifornia Office of the President (09-IR-07-117968-WOOS). S. W. was\nsupported by the US NSF (AST-0909129), the University of California\nOffice of the President (09-IR-07-117968-WOOS), and the DOE SciDAC\nProgram (DEFC-02-06ER41438). R. H. was supported by the DOE SciDAC\nProgram (DEFC-02-06ER41438) and under the auspices of the Department of \nEnergy at Lawrence Livermore National Laboratory under contract \nDE-AC52-07NA27344.\n\\end{acknowledgements}\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Results and Discussion}\n\nWe first illustrate the method in some detail for the case $\\epsilon_{BB}=1.0$, and then present the general results. We also proceed to rigorously characterize the motifs and identify them in the lattice structures.\n\nIn order to name the different phases we searched the Material Project Database\\cite{Jain2013} to find a prototype isostructural phase and name the GA calculated lattice accordingly. If no match is found, then we name the phase according to the following convention:\n\\begin{equation}\\label{Eq:naming_convention}\n{\\mbox{A}_m \\mbox{B}_n}^{\\mbox{space group}}_{\\mbox{identifier}} .\n\\end{equation}\nHere $m$ and $n$ are the number of A and B particles within the unit cell. The space group is determined using the FINDSYM package\\cite{Stokes2005}, with the tolerance for lattice and atomic positions set to $0.05$. The identifier is necessary as multiple phases with the same stoichiometry and space group, differing only in Wyckoff number and positions, are found.\n\n\n\n\\begin{figure}[t]\n\\includegraphics[width=0.48\\textwidth]{fig1.png}\n\\caption{Structures searched by GA in $\\epsilon_{BB}=1.0$ and $\\gamma = 0.8$. (a) Formation energies ($E_{form}$) of structures searched by GA as a function of stoichiometry ($x$). Each point corresponds to a structure. The color of points are assigned by the type of motifs in the corresponding structure. The black solid line is the convex hull of the system, while the black dash line is the threshold for metastable structures. (b) Structure of the FCC motif (c) Structure of the MgZn$_2$ motif, which is Frank-Kasper Z$_{16}$.}\\label{Fig:epsilon_1}\n\\end{figure}\n\n\n\\textbf{The case $\\boldsymbol{\\epsilon_{BB}=1.0}$.} Here we consider $\\epsilon_{BB} = \\epsilon_{AA} = 1.0$, while $0.3 \\le \\gamma \\le 0.9$. We first compute the energy of the ground state for the pure A and B states, which previous calculations\\cite{Stillinger2001,Travesset2014} have shown to be the hcp phase. Here, however, because of the finite cut-off of LJ potentials, the fcc phase has lower energy. The identification of equilibrium phases proceeds by comparing their energy against phase separation into pure $A$ and $B$. Then, out this list of putative binary phases that are stable against phase separation, the energies are compared to establish the resulting true phase diagram equilibrium. This is how the phase diagram Fig.~\\ref{Fig:epsilon_1} is built, where there is only one stable BNSL, the MgZn$_2$ Frank-Kasper phase at $\\gamma=0.8$. We should note that maximum of the packing fraction for this phase occurs for $\\gamma_c=\\sqrt{2\/3}=0.8165$\\cite{Travesset2017a}, which is very close.\n\nSince it is common that structures that are metastable at 0 K can be observed in experiments at finite temperatures, we also considered metastable phases defined to be those within $0.1\\epsilon$\/particle in energy above the convex hull. As shown in Fig.~\\ref{Fig:epsilon_1}, there are a number of metastable phases at $x=0.333$, which are minor variations of MgZn$_2$ as we analyze further below in the context of motifs.\n\n\\textbf{General $\\epsilon_{BB}$.} On physical grounds, it is expected that the smaller the particle the weaker the interaction, hence we consider $\\epsilon_{BB} \\le 1$. In Fig.~\\ref{Fig:epsilon_gen_energy}, we provide a typical calculation for fixed $\\gamma=0.6$ as a function of both $\\epsilon_{BB}$ and $x$. As expected, see Fig.~\\ref{Fig:epsilon_1}, the phase diagram is trivial for $\\epsilon_{BB}=1$. However, three phases TiCu$_3$, AlB$_2$ and CrB at $x=0.25, 0.333, 0.5$ are found for $\\epsilon_{BB}= 0.8$.\n\nBy repeating the calculations shown in Fig.~\\ref{Fig:epsilon_gen_energy} for the other values of $\\epsilon_{BB}$ at a fixed $\\gamma=0.6$ (see Table~\\ref{tab:my_label}), we constructed the phase diagram shown in Fig.~\\ref{Fig:epsilon_gen}. In Fig.~\\ref{Fig:epsilon_gen} we note the appearance of seven additional phases for $\\epsilon_{BB}< 0.6$ that could not be matched to any prototype: Detailed description for these and all other equilibrium phases are collected in Supporting Information Table S1. A database for all the structures is included in Supporting Information. \n\n\n\n\nSimilarly, the phase diagrams for all other values of $\\gamma$ are also presented in Supporting Information Fig. S2. Common to all these phase diagrams is the appearance of many diffusionless (martensitic), usually incongruent transformations, as a function of the energy parameter $\\epsilon_{BB}\/\\epsilon_{AA}$. In Supporting Information Fig. S3, we have also included phase diagrams for all values of $\\epsilon_{BB}\/\\epsilon_{AA}$ in $x$ and $\\gamma$.\n\n\\textbf{Motifs.} We define motifs as the polyhedron consisted of a center particle and its first-shell neighbors. The motifs are generated according to the analysis of bond length table from neighboring particles to the center (see details in Supporting Information Fig. S6). In this study, we only include motifs with the larger A-particles as the center. We will name motifs according to\n\\begin{equation}\\label{Eq:motif_name}\n \\mbox{Motif}-\\mbox{CN}-\\mbox{Identifier} \\ ,\n\\end{equation}\nwhere CN is the Coordination (the number of particles) and identifier discriminates among motifs with the same coordination number.\n\n\\onecolumngrid\n\n\n\n\\begin{figure}[b]\n \\includegraphics[width=1.0\\textwidth]{fig2.pdf}\n \\caption{Two examples of GA results for $\\gamma = 0.6$. In each figure, the solid line is the convex hull, while the dashed line is the threshold for metastable structures, see the discussion above. (a) Structures searched by GA as a function of x when $\\epsilon_{BB} = 1.0$: There are no stable binary structures between x=0 and x=1. (b) Structures searched by GA as a function of x for $\\epsilon_{BB}=0.8$. There are three stable structures which appear at $x = 0.25$ (TiCu$_3$), $x = 0.333 $ (AlB$_2$) and $x = 0.5$ (CrB).}\\label{Fig:epsilon_gen_energy}\n\\end{figure}\n\n\\clearpage\n\n\\begin{figure}[]\n\\includegraphics[width=1.0\\textwidth]{fig3.pdf}\n\\caption{Phase diagram in $x$ and $\\epsilon_{BB}\/\\epsilon_{AA}$ for $\\gamma = 0.6$.}\\label{Fig:epsilon_gen}\n\\end{figure}\n\n\\twocolumngrid\n\n\nWe identified 187 equilibrium and 102,822 metastable structures. Out of the 187 equilibrium structures, we removed redundancies by a cluster alignment algorithm\\cite{Fang2010,Sun2016a}leading to only 53 equilibrium structures. Out of these 53 structures we identified 42 motifs, which are listed in the order of increasing CN in Supporting Information Fig. S4. 416,391 motifs can be found in the 102,822 metastable structures. Among them, a vast majority (312,891) of the motifs of the metastable structures also exist in the equilibrium phases. In Tab~\\ref{tab:motif}, we list the name, CN and the percentage fraction of the ten most frequent motifs present in meta-stable structures. Note that these ten already account for more than 95\\% of the 312,891 motifs. The details about how to identify the motif from a crystal and how to identify if a crystal has the motif inside have been included in the Supporting Information.\n\n\n\n\n\\begin{table}[h]\n \\centering\n \\caption{Ten most frequent Motifs in metastable structures}\n \\begin{tabular}{c|c|c}\n \\hline\n \\hline\n Motif & CN & Frequency \\\\ \\hline\n FCC & 12 & 31.4\\% \\\\ \\hline\n HCP & 12 & 18.9\\% \\\\ \\hline\n Octahedron (Motif-6-4) & 6 & 9.8\\% \\\\ \\hline\n Half Hexagonal Prism 1 (Motif-6-2) & 6 & 9.1\\% \\\\ \\hline\n Triangular Prism (Motif-6-3) & 6 & 6.4\\% \\\\ \\hline\n Half Hexagonal Prism 2 (Motif-6-1) & 6 & 5.3\\% \\\\ \\hline\n BCC & 8 & 5.0\\% \\\\ \\hline\n Hexagonal Prism (Motif-12-3) & 12 & 4.8\\% \\\\ \\hline\n Half Truncated Cube (Motif-12-1) & 12 & 2.3\\% \\\\ \\hline\n MoB (Motif-13-1) & 13 & 2.2\\% \\\\ \\hline\n Total & & 95\\% \\\\ \\hline\n \\hline\n \\end{tabular}\n \\label{tab:motif}\n\\end{table}\n\nAs an illustrative example, we consider the case of $\\epsilon_{BB}=1$ and $\\gamma=0.8$, where in Fig.~\\ref{Fig:epsilon_1} we have shown the two relevant motifs are the FCC and the Frank-Kasper Z$_{16}$.\nBy coloring each structure according to the motif, we can confirm that the metastable phases (all in red) have motifs which are small variations of the Frank-Kasper Z$_{16}$ and that the vast majority of the structures found in other searches have motifs which are variations of either FCC or Frank-Kasper Z$_{16}$.\n\n\\onecolumngrid\n\n\\begin{figure}[]\n\\includegraphics[width=0.7\\textwidth]{fig4.png}\n\\caption{(a) Map of MgZn$_2$ in $\\gamma$ and $\\epsilon_{BB}$. The red regime indicates that the structure of MgZn$_2$ is thermodynamically stable while in blue regime it is metastable. (b) Map for the Z$_{16}$ motif with red stable, blue metastable. The red regime is where the stable structure has Z$_{16}$ motif inside. Note that the motif has a wider range of both stability and metastability, as it also appears in other Laves phase, such as the MgCu$_2$ and MgNi$_2$.}\\label{Fig:mgzn2_motif_map}\n\\end{figure}\n\n\\twocolumngrid\n\n\\onecolumngrid\n\n\\begin{figure}[]\n \\includegraphics[width=0.8\\textwidth]{fig5.png}\n \\caption{Map for first four frequent motifs in $\\gamma$ and $\\epsilon_{BB}$ excludes the general motif FCC and HCP. (a) Octahedron (b) Half hexagonal prism 1. (c) Triangular Prism (d) Half hexagonal prism 2. Red indicates stable structures and blue indicates metastable.\n }\\label{Fig:req_motif}\n\\end{figure}\n\n\\twocolumngrid\n\nIn Fig.~\\ref{Fig:mgzn2_motif_map} we show the domain of stability and metastability for the MgZn$_2$ phase and the Z$_{16}$ motif. The GA searches were performed on a mesh of $\\gamma$ and $\\epsilon_{BB}$ with an increment of 0.1. Here, to improve the resolution of the stability range, we examined the stability of all GA-found structures and motifs on a finer mesh in the $\\gamma$-$\\epsilon_{BB}$ plane with an increment of 0.02. Rather interestingly, the stability range of the Z$_{16}$ motif is larger than that of the MgZn$_2$ phase, indicating this motif is not unique to MgZn$_2$, but shared by other Laves and Frank-Kasper phases. Similar plots for the four more frequent motifs are shown in Fig.~\\ref{Fig:req_motif}.\n\n\n\n\\begin{table}[h]\n \\centering\n \\caption{Comparison between packing phases\\cite{Hopkins2012} and our study for $ 0.3 \\le \\gamma \\le 1$. (SG=Space group), The $\\ast$ indicates there are small distortions in the LJ phase, compared with the packing phase. Motifs in the LJ column indicates that they are not stable in the GA result, but they have the motif inside in the corresponding $\\gamma$ regime.}\n \\begin{tabular}{c c c c | c c}\n \\hline\n \\hline\n Phase & $\\gamma$-range & Ref & SG & LJ & Distortion \\\\ \\hline\n A$_3$B & $[0.618, 0.660]$ & \\cite{OToole2011} & 59 & TiCu$_3$ & \\\\\n AlB$_2$ & $[0.528, 0.620]$ & & 191 & AlB$_2$ & \\\\\n AuTe$_2$ & $[0.488, 0.528]$ & \\cite{Filion2009} & 12 & Motif-6-2 & \\\\\n (2-2)$^{\\ast}$ & $[0.480, 0.497]$ &\\cite{Marshall2010} & 11 & Motif-6-1 & \\\\\n (4-2) & $[0.488, 0.483]$ &\\cite{Hopkins2012} & 191 & Motif-12-3 & \\\\\n (5-2) & $[0.480, 0.483]$ &\\cite{Hopkins2012} & 44 & & \\\\\n (7-3) & $[0.468, 0.480]$ &\\cite{Hopkins2012} & 71 & Motif-12-3 & \\\\\n HgBr$_2$ & $[0.443, 0.468]$ &\\cite{Filion2009} & 36 & Motif-6-4 & \\\\\n (6-6) & $[0.414, 0.457]$ &\\cite{Hopkins2012} & 11 & Motif-6-4 & \\\\\n XY & $[0.275, 0.414]$ &\\cite{Hopkins2012} & & & \\\\\n (6,1)$_4$ & $[0.352, 0.321]$ &\\cite{Hopkins2012} & 69 & ${\\mbox{A}_2\\mbox{B}_{12}}^{(139)}_{(1)}$ & $\\ast$ \\\\\n (6,1)$_6$ & $[0.321, 0.304]$ &\\cite{Hopkins2012} & 139 & ${\\mbox{A}_2\\mbox{B}_{12}}^{(139)}_{(1)}$ & $\\ast$ \\\\\n (6,1)$_8$ & $[0.302, 0.292]$ &\\cite{Hopkins2012} & 139 & ${\\mbox{A}_2\\mbox{B}_{12}}^{(139)}_{(1)}$ & $\\ast$ \\\\\n \\hline\\hline\n \\\\\n \\end{tabular}\n\n \\label{tab:pg_lj_comp}\n\\end{table}\n\nQuite generally, the motifs are far more sensitive to $\\gamma$ than they are to $\\epsilon_{BB}\/\\epsilon_{AA}$, confirming that the particle size is more important than the actual intensity of the interactions. It is consistent with all calculations that stable structures with the same values of $\\gamma$ tend to share motifs. As found for MgZn$_2$ and Z$_{16}$, the regions for stability and metastability is wider than the corresponding structures, thus indicating that motifs define very general families of structures, like Laves phases. A classification of motifs by Renormalized Angle Sequences (RAS)\\cite{Lv2017,Lv2018} has been included in Supporting Information. \n\nThis study has identified 53 equilibrium lattices and 42 motifs (with the larger particle A as reference). We now discuss the relevance of these results for packing models\\cite{Filion2009,Hopkins2012}, their connection to the motifs reported in Quasi Frank-Kasper phases\\cite{Travesset2017} and their implications for binary superlattices.\n\n\n\\textbf{Packing Phase Diagram.} We consider the study of Hopkins \\textit{et al.}\\cite{Hopkins2012} as the reference phase diagram for packing problems, although it only includes unit cells containing up to 12 particles. Consistently with this study we concentrate on the range $0.3\\le \\gamma \\le 1$, also because for smaller $\\gamma$ there are many phases with narrow stability ranges that are less relevant in actual experimental systems.\n\n\n\nFrom Table~\\ref{tab:pg_lj_comp}, the packing of binary phase diagram contains 13 phases for the $0.3 \\le \\gamma \\le 1$ range. For large $\\gamma > 0.528$ only two phases exist; AlB$_2$ and A$_3$B, which are both found in binary LJ systems (if allowing for small differences in A$_3$B). For $0.488 < \\gamma < 0.528$, however, the AuTe$_2$ phase is reported; We did not find such phase, but we do report the Motif-6-2 as stable for the same range of $\\gamma$, see Supporting Information, which is present in the equilibrium phases at $\\gamma=0.5$ ${{\\mbox A}_4{\\mbox B}_6}^{(166)}_{(9)}$, BaCu and TePt. Some other phases, which are reported as packing phases \\cite{Hopkins2012} but not stable in the GA search, are also identified to have the motif in the corresponding $\\gamma$ regime. This indicates that these packing phases may be meta-stable in our calculation. For smaller $\\gamma$, there is also overlap if allowing for small distortions. \n\nOther phases that have large packing fractions, such as CrB and S74e\/h(KHg$_2$ in our notation)\\cite{Filion2009}, that are metastable in the packing phase diagram become equilibrium, thus showing that the LJ system augments the number of stable phases as compared with packing models.\n\n\\textbf{Motifs and Quasi Frank Kasper Phases.} In Ref.~\\cite{Travesset2017} it was shown that all experimental BNSLs could be described as disclinations of the $\\{3,3,5\\}$ polytope, thus generalizing well known four Frank-Kasper motifs Z$_{12}$,Z$_{14}$,Z$_{15}$, Z$_{16}$\\cite{Frank1958,Frank1959} to include other motifs. \n\n\\onecolumngrid\n\n\\begin{table}[]\n \\centering\n \\caption{Motifs in Quasi Frank Kasper phases\\cite{Travesset2017} compared to the ones described in this work.} \n \\begin{tabular}{c | c c c c c c}\n \\hline\\hline\n QFK\\cite{Travesset2017} & $\\mbox{Z}_6$ & $\\mbox{Z}_{12}^{\\prime\\prime}$ & $\\mbox{Z}_{14}^{\\prime}$ & $\\mbox{Z}_{16}$ & $\\mbox{Z}_{18}^{\\prime\\prime}$ & $\\mbox{Z}_{24}$ \\\\\n \\hline\n This work & Motif-6-4 & Motif-12-2 & Motif-14-1 & Motif-16-2 & Motif-18-3 & Motif-24-1 or \\\\\n & & & & & & Motif-24-3 \\\\\n \\hline\n \\hline\n\n \\end{tabular}\n \\label{tab:qfk_lj_comp}\n\\end{table}\n\n\\twocolumngrid\n\nIn Table~\\ref{tab:qfk_lj_comp} we show the equivalence between Quasi Frank Kasper motifs and the ones obtained in this work, which only include those with the A-particle as reference. It should be pointed that the motifs are not completely the same, as in Ref.~\\cite{Travesset2017} the motifs were defined by the Voronoi cell and its corresponding neighbors, which is a slightly different definition than the one used in this paper. \n\n\\textbf{Experimental Results.} The list of experimentally reported BNSLs is taken from Ref.~\\cite{Travesset2017a}, where we have excluded two dimensional superlattices and those where nanocrystals cannot be approximated as spherical, see Ref.~\\cite{Boles2016}. The comparison between the results obtained in this paper and experimental BNSLs is provided in Table~\\ref{tab:exp_lj_comp}.\n\n\\begin{table}[]\n \\centering\n \\caption{Experimentally determined structures. NA: Phase not available in this study. NF: Phase not found in this study. The DDQC\/AT is a quasicrystal phase. The bccAB$_6$ phase is also known as C$_{60}$K$_6$ and is denoted as ${\\mbox{A}\\mbox{B}_6}^{(229)}_{(1)}$ in this paper.} \n \\begin{tabular}{c c | c c c}\n \\hline \\hline\n \\multicolumn{2}{c|}{Experiment} & \\multicolumn{3}{c}{Binary LJ}\\\\ \\hline\n BNSL & $\\gamma$-range & & $\\gamma$-range & $\\varepsilon_{BB}$-range \\\\\n NaCl & $[0.41,0.60]$ & & $[0.2,0.5]$ & $[0.1,0.8]$ \\\\\n CsCl & $[0.71,0.90]$ & & NF & \\\\\n AuCu & $[0.58,0.71]$ & & NF & \\\\\n DDQC\/AT& $[0.41,0.43]$ & & NA & \\\\\n AlB$_2$ & $[0.45,0.70]$ & & $[0.4,0.7]$ & $[0.1,0.9]$ \\\\\n MgZn$_2$ & $[0.60,0.81]$ & & $[0.7,1.0]$ & $[0.1,1.0]$ \\\\\n AuCu$_3$ & $[0.40,0.60]$ & & NF & \\\\\n Li$_3$Bi & $[0.53,0.56]$ & & NF & \\\\\n Fe$_4$C & $[0.55,0.65]$ & & NF & \\\\\n CaCu$_5$ & $[0.60,0.80]$ & & $[0.6,0.8]$ & $[0.1,0.9]$ \\\\\n CaB$_6$ & $[0.43,0.47]$ & & $[0.3,0.5]$ & $[0.1,0.8]$ \\\\\n bccAB$_6$ & $[0.45,0.50]$ & & $[0.4,0.6]$ & $[0.1,0.5]$ \\\\\n cubAB$_{13}$ & $[0.55,0.60]$ & & NF & \\\\\n NaZn$_{13}$ & $[0.47,0.70]$ & & $[0.6]$ & $[0.1,0.6]$ \\\\\n \\hline \\hline\n \\end{tabular}\n \\label{tab:exp_lj_comp}\n\\end{table}\n\nSeven of the experimentally reported BNSLs, namely NaCl, AlB$_2$, MgZn$_2$, CaCu$_5$, CaB$_6$, bccAB$_6$ and NaZn$_{13}$ are found as equilibrium phases in the LJ system essentially for the same range of $\\gamma$. The fact that in our results the stability is roughly independent of $\\varepsilon_{BB}$ in certain regions provides some support for the idea that microscopic details of the potential are unimportant in this region (``universality''). Further making this point is that the same phases are stable for soft repulsive potentials in the same $\\gamma$-range \\cite{Travessetpnas2015,HorstTravesset2016,LaCour2019}. \n\nWe now analyze the phases reported in experiments that are not equilibrium in our study. One of them is beyond the scope of our calculation; DDQC\/AT, which is a quasicrystal. The Li$_3$Bi and also the AuCu$_3$ are stabilized by large deformations of the ligands, i.e. vortices\\cite{Travesset2017}, and therefore are not possible to obtain from a quasi HS approximation. The Fe$_4$C phase was observed in 2006\\cite{Shevchenko2006}, and since then, it has not been reported in any further study, which may suggest is metastable, and furthermore, it can only be stabilized by vortices\\cite{Travesset2017a}. The CsCl phase has a very narrow range of stability around $\\gamma_c = \\sqrt{3}-1=0.732$\\cite{Travesset2017a}, which is likely missed by the discretization of $\\gamma$ values in our study.\nFinally, AuCu occurs when there is ligand loss\\cite{Travesset2017,Boles2019} and is stabilized through a different mechanism involving the non-spherical shape of the nanocrystal. We therefore conclude that the binary LJ model successfully predicts those experimentally reported phases that can be described as quasi-hard spheres. This is in contrast to packing models, where MgZn$_{2}$ or CaCu$_5$ phases, widely reported in experiments are not equilibrium phases (maximum of the packing fractions). See Fig.~\\ref{Fig:summary} for a visual summary of this discussion.\n\n\\begin{figure}[]\n \\includegraphics[width=0.48\\textwidth]{fig6.pdf}\n \\caption{Summary of the main results of the paper: The experimental phases are classified according to: Hard sphere, OTM\/hard sphere (exist when NCs are modeled as hard spheres but are stabilized by vortices)\\cite{Travesset2017}, pure OTM(only stable with vortices), and other (observed in special cases, such as ligand detachment\\cite{Boles2019}). See also Table~\\ref{tab:exp_lj_comp}. Consistent with the LJ assumptions, only the hard sphere phases are found in our work. The Experiment Pred includes those strong candidates to be found experimentally, as discussed below.}\\label{Fig:summary}\n\\end{figure}\n\n\n\n\\section{Conclusions}\n\nBy the use of Genetic Algorithm (GA), we have been able to predict stable structures under different sizes of particles and strengths of interaction ($\\gamma \\in$ [0.3 to 0.9], $\\epsilon_{BB} \\in$ [0.1 to 1.0]). We report 53 stable phases, which cover a significant part of currently reported structures. Besides that, we also predict 35 stable structures which are not in Material Project database. We find that the type of stable structures strongly depends on $\\gamma$, but weakly on $\\epsilon_{BB} < 1$, providing evidence that the stability of the lattices has a weak dependence on the potential details (universality). By comparing our results with other theoretical and experimental works, it is shown that regardless of potential details, the same $\\gamma$ regime has the same stable structure, which reinforce that the stable structure has a weak dependence on the potential details.\n\n\nThere are two aspects about the limitations of the hard sphere description: The first is that it does \\textit{not} provide a free energy: the observed phases are not the ones with maximum packing fraction\\cite{Hopkins2012}, but rather, ones where the packing fractions is maximum for the particular structure. This is where the Binary LJ becomes important: the stable phases are the ones that minimize the free energy (modeled as the LJ potential). The second limitation is that it does not model large deformations of the ligand shell: these cases go beyond the LJ model and is evident from Fig.~\\ref{Fig:summary}, showing that these phases are absent.\n\nThe crystalline motifs are employed to describe the large amount of metastable structures. We find that metastable structures mostly can be described from the motifs present in equilibrium structures, thus suggesting the possibility of building superlattices by patching all motifs that can tile the 3D space, as similarly done in the more restricted case of Frank-Kasper phases\\cite{DutourSikiric2010}. It also raises the possibility of motifs being present within the liquid\\cite{Damasceno2012} as a way to anticipate the emergent crystalline structure.\n\nComparing with available experimental results, see Table~\\ref{tab:exp_lj_comp} and Fig.~\\ref{Fig:summary}, the binary LJ model captures all the equilibrium phases where nanocrystals can be faithfully described as quasi hard spheres: NaCl, AlB$_2$, MgZn$_2$, CaCu$_5$, CaB$_6$, bccAB$_6$ and NaZn$_{13}$. The other phases reported in experiments either require the presence of vortices, as predicted by the OTM\\cite{Travesset2017,Travesset2017a}, or are stable over a very narrow range of $\\gamma$ values, likely missed by the necessary discrete number considered in our study.\n\nPacking phase diagram models reported 14 equilibrium phases in the interval $\\gamma \\in [0.3,1)$, see Table~\\ref{tab:pg_lj_comp}, while our study reports 53, thus showing that binary LJ have a more complex phase diagram. Rather interestingly, phases such as MgZn$_2$ or CaCu$_5$, which are very common in experiments, are absent in the packing phase diagram; Although very useful in identifying at which $\\gamma$ values a phase is likely to appear, packing models give very poor predictions on which, among all possible phases, will actually be observed.\n\nThe two guiding principles for stability of BNSLs in experiments are high packing fraction (or low Lennard-Jones Energy) and tendency towards icosahedral order, as reflected in the motifs\\cite{Travesset2017a,Coropceanu2019}. Therefore, we expect that those equilibrium Lennard-Jones phases with Quasi Frank-Kasper motifs, \nfor example, the BNSLs ${\\mbox{A}_{2}\\mbox{B}_{4}}^{(227)}_{(1)}$ and\n${\\mbox{A}_{2}\\mbox{B}_{12}}^{(139)}_{(1)}$\n(Motif-16-2), or $\\mbox{Zr}_{2}\\mbox{Cu}^{(139)}_{(1)}$ (Motif-14-1), will be excellent candidates to search for BNSLs, see Fig.~\\ref{Fig:summary}. Definitely, these ideas will be developed further in the near future, where the 53 stable lattices will be studied with more realistic nanocrystal models described at the atomic level. \n\nIn this work we focused on spherically symmetric potentials with additive interactions, as described by relations like\n\\begin{equation}\n\\epsilon_{AB}=\\frac{1}{2}(\\epsilon_{AA}+\\epsilon_{BB}) \\ .\n\\end{equation}\nIt is of interest to consider more general models, where these restrictions are lifted. This, however, will be the subject of another study.\n\n\\section{Methods}\nThe crystal structure searches with GA were only constrained by stoichiometry, without any assumption on the Bravais lattice type, symmetry, atom basis or unit cell dimensions (up to a maximum of particles per unit cell). During the GA search, energy was used as the only criteria for optimizing the candidate pool. At each GA generation, 64 structures are generated from the parent structure pool \\textit{via} the mating procedure described in Ref.~\\cite{Deaven1995,Oganov2006,Ji2010}. The mating process was based on real-space \"cut-and-paste\" operations that was first introduced to optimize cluster structures~\\cite{Deaven1995}. This process was extended to predict low-energy crystal structures by Oganov~\\cite{Oganov2006} and reviewed in Ref.~\\cite{Ji2010}. Here, we follow the same procedure that was described in detail in Ref.~\\cite{Ji2010} and was implemented in the Adaptive Genetic Algorithm (AGA) software.\n\nWith a given set of LJ parameters, we performed three GA searches independently, with each GA search running for 1000 generations. The maximum number of particles per unit cell used in each search was 20, and thus, phases with large unit cells, the most relevant being NaZn$_{13}$, could not be included. Therefore, we include NaZn$_{13}$ into our calculation manually. All energy calculations and structure minimizations were performed by the LAMMPS code \\cite{Plimpton1995} with some cross checks using HOOMD-Blue\\cite{AndersonMe2008a} with FIRE minimization\\cite{Bitzek2006}. The database of binary lattices in HOODLT\\cite{Travesset2014} was also used.\n\n\n\\section{Supporting Information}\nSupporting information contains: \nList and maps of structures searched by genetic algorithm;\nphase diagrams of equilibrium structures; equilibrium motif database; maps of motifs; algorithms for motif identification and renormalized angle sequence\n\n\\section{acknowledgement}\n\nA.T acknowledges discussions with I. Coropceanu and D. Talapin. We also thank Prof. Torquato for facilitating the data of his group packing studies. Work at Ames Laboratory was supported by the US Department of Energy, Basic Energy Sciences, Materials Science and Engineering Division, under Contract No. DE-AC02-07CH11358, including a grant of computer time at the National Energy Research Supercomputing Center (NERSC) in Berkeley, CA. The Laboratory Directed Research and Development (LDRD) program of Ames Laboratory supported the use of GPU-accelerated computing. Y. S. was partially supported by National Science Foundation award EAR-1918134 and EAR-1918126.\n\n\n\\bibliographystyle{apsrev4-1}\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\\label{sec:intro}\n\\setcounter{page}{2}\n\n\nAn interesting application of gauge\/gravity duality to condensed matter physics arises in the study of \nmomentum relaxation. This is so mainly because the resulting zero frequency conductivities are finite, \nallowing us to study transport in a more realistic way.\nTo this end, one must find gravitational solutions which break translational \ninvariance along the boundary directions due to the presence of one or more spatially dependent sources. \nGenerically this involves numerically solving the Einstein equations which give an elliptic PDE problem in this context.\nSome studies focus on configurations which describe a lattice in the dual field theory, for instance when the chemical potential is a periodic function \nof a spatial direction on the boundary.\nGravitational solutions of this kind have been successfully constructed \n\\cite{Horowitz:2012ky, Horowitz:2012gs, Horowitz:2013jaa, Donos:2014yya, Rangamani:2015hka}\nand they indeed reproduce the expected\nlow frequency dynamics, i.e.\\ the zero frequency delta functions in the conductivities are resolved into finite width\nDrude peaks. \n\nA considerable technical simplification arises if a certain global symmetry is present, say, in the matter sector \nof the bulk theory. Then, translational invariance can be broken along that symmetry direction while \npreserving homogeneity of the geometry, which in turn \nimplies that the construction of such solutions only requires solving ODEs. Examples of this kind include\n\\cite{Donos:2012js, Donos:2013eha}, which have been shown to yield finite DC conductivities as well as to possess a rich \nstructure which displays transitions between different metallic and \ninsulating regimes.\\footnote{A simplification of this type arises in holography with massive gravity in the bulk \\cite{Vegh:2013sk}.}\n\n\nWe can simplify the problem even further by arranging the bulk matter content in such a way that the \nblack branes of interest are not only homogeneous but also isotropic. This fact was exploited in \\cite{Andrade:2013gsa} \n(see also \\cite{Taylor:2014tka}), which considered a particular configuration of a set of massless scalars, termed linear axions since they \nare shift-symmetric, that allows for an analytical black brane \nsolution at non-zero chemical potential in an arbitrary number of dimensions. As expected, the DC \nelectric and thermal conductivities are finite \\cite{Andrade:2013gsa, Gouteraux:2014hca,Davison:2014lua,Donos:2014cya}, and they can be evaluated \nanalytically. However, the conductivities at non-zero frequency need to be computed numerically since this \ninvolves solving coupled fluctuation equations around the background solution. Interestingly, it has been noted that \ndeviations from Drude physics are present when the strength of momentum relaxation is large \n\\cite{Kim:2014bza, Davison:2014lua, Davison:2015bea}. \n\n\nRecently, it has been noted that the equations of General Relativity simplify considerably in the limit in \nwhich the number of spacetime dimensions, $D$, is taken to be large, which provides an efficient tool to \napproximate finite $D$ results, as a perturbative calculation in $1\/D$ \\cite{Emparan:2013moa}. \nThe main ingredient of the construction is the fact that when the number of spacetime dimensions is large, \nthe gravitational potential becomes very steep near the horizon which yields to a natural separation of the \ndynamics that localise near the horizon and the ones that probe regions far away from it. \nIn particular, this implies that the quasi-normal mode (QNM) spectrum splits into near-horizon {\\it decoupled} modes,\nand {\\it coupled} modes that are delocalised \\cite{Emparan:2014cia, Emparan:2014aba, Emparan:2015rva}. \nAs explained in these references, while the coupled modes are quite generic, i.e.\\ \nshared by many black holes, the decoupled modes are sensitive to the particularities of different \nsolutions. \n\nIn this paper we initiate the study of holographic inhomogeneities and the resulting momentum relaxation using large $D$ techniques.\nOur first goal is to study the decoupled quasi-normal modes which control the characteristic decay rate of the \nelectric and thermal conductivities in the linear axion model at non-zero chemical potential. \nWe find that momentum conservation is restored unless we scale the strength of the axions with $D$, i.e.\\ the decoupled QNM frequency vanishes to leading order. \nTherefore we scale the axion strength appropriately and obtain a QNM capturing the momentum decay rate at leading order in large $D$. We calculate corrections to this in $1\/D$.\nWe see that in certain regimes the QNM frequencies are well-described using the large $D$ approximation. \nSecond, we will compute the AC thermal conductivity for zero chemical potential, which at leading order is exactly of Drude form. These results are consistent with the corresponding QNM calculation. To our knowledge, this is the first \nanalytical realisation of Drude behaviour outside of the hydrodynamic regime in the context of holography.\nWith these results at hand, we will comment on the signature of the transition from coherent to incoherent regimes, \ni.e.\\ the breakdown of Drude physics, in the large $D$ approximation. \n\nThis paper is organised as follows. In section \\ref{sec:model} we review the axion model, its transport properties and the appropriate master fields for the conductivity calculation. In section \\ref{sec:QNM} we compute the large $D$ decoupled QNMs which control momentum relaxation, giving analytical expressions for their frequencies. In section \\ref{sec:conductivity} we compute the AC thermal conductivity as an expansion in $1\/D$. We conclude in section \\ref{sec:conclusions}. \n\n\n\\section{Momentum relaxation in arbitrary D}\n\\label{sec:model}\n\n\nIn this section we review the holographic model of momentum relaxation proposed in~\\cite{Andrade:2013gsa}. \nWe will discuss the main properties of the background solution and describe the computation of the two-point \nfunctions using a gauge invariant master field formalism. \n\n\n\\subsection{Linear axion background}\n\\label{sec:axion}\n\n\nThe holographic model of momentum relaxation in $D = n+3$ bulk dimensions which we consider throughout this \npaper is given by the action \\cite{Andrade:2013gsa}\n\\begin{equation}\\label{S0}\n\tS_0 = \\int d^{n+3} x \\sqrt{- g} \\left( R + (n+1)(n+2) \\ell^{-2} - \\frac{1}{4} F^2 - \\frac{1}{2} \\sum_{I=1}^{n+1} (\\partial \\psi_I)^2 \\right)\n\\end{equation}\n\\noindent where $F = dA$ is the field strength of a $U(1)$ gauge field, $\\psi_I$ are $(n+1)$ massless scalar fields \nand $\\ell$ is the AdS radius which we set to one henceforth. \n\nThis model admits the following analytical black brane solution\\footnote{This solution was previously derived in \\cite{Bardoux:2012aw} \nin a different context.}\n\\begin{equation}\\label{NL ansatz}\n\tds^2 = - f(r) dt^2 + \\frac{dr^2}{f(r)} + r^2 \\delta_{a b} dx^a dx^b, \\qquad A = A_t(r) dt, \\qquad \\psi_I = \\delta_{I a} x^a\n\\end{equation}\n\\noindent where $a$ labels the $(n+1)$ boundary spatial directions $x^a$ and\n\\begin{align}\n\tf(r) &= r^2 - \\frac{\\alpha^2}{2 n} - \\frac{m_0}{r^n} + \\frac{n \\mu^2}{2(n+1)} \\frac{r_0^{2n}}{r^{2n}}\\label{axion soln} \\\\\n\tA_t(r) &= \\mu \\left(1 - \\frac{r_0^n}{r^n} \\right)\\label{axion gauge field}\n\\end{align}\nHere $r_0$ is the horizon of the brane, $\\mu$ is the chemical potential in the dual theory and $m_0$ is related to the total energy\nof the solution. \nThe Hawking temperature is given by \n\\begin{equation}\n\tT = \\frac{f'(r_0)}{4 \\pi} = \\frac{1}{4 \\pi} \\left( (n+2) r_0 - \\frac{\\alpha^2}{2 r_0} - \\frac{n^2 \\mu^2}{2 (n+1) r_0} \\right).\n\\end{equation}\nNote that, despite the fact that the geometry is isotropic and homogeneous, the solution manifestly breaks translational\ninvariance due to the explicit dependence of $\\psi_I$ on $x^a$. This feature is reflected in the its thermoelectric DC conductivities, \nwith the $\\delta$-function at zero frequency present in the Reissner-Nordstr\\\"om solution removed due to the breaking of translational invariance. \n\n\\subsection{Transport}\n\\label{sec:Trans}\nConductivities can be computed in terms of two-point functions, which are given in AdS\/CFT by studying linear fluctuations\naround the black holes under consideration. Here we are interested in the electric and thermal conductivities at zero spatial \nmomentum, which can be obtained in terms of the retarded two-point functions\n\\begin{equation}\\label{2pt fns}\n\tG_{JJ} (\\omega) = \\langle J^1 J^1 \\rangle (\\omega), \\quad G_{QJ} (\\omega) = \\langle Q^1J^1 \\rangle (\\omega) \\quad G_{QQ} (\\omega) = \\langle Q^1Q^1 \\rangle (\\omega)\n\\end{equation}\n\\noindent where $Q^i = T^{ti} - \\mu J^i$ and $T^{ij}$ and $J^i$ are the the stress tensor and $U(1)$ current of the field theory, respectively. \nHere we have chosen to compute the conductivities along the axis $x^1$. Because the black holes of interest are isotropic, this does not \nresult in loss of generality. \nWe can then express the electric conductivity $\\sigma(\\omega)$, the thermo-electric conductivity $\\beta(\\omega)$ and the thermal conductivity $\\kappa(\\omega)$ \nin terms of the two-point functions \\eqref{2pt fns} by means of the Kubo formulae:\n\\begin{align}\n\\nonumber\n\t\\sigma(\\omega) &= \\frac{i}{\\omega} (G_{JJ} (\\omega) - G_{JJ} (0)) , \\\\\n\\nonumber\n\t\\beta(\\omega) &= \\frac{i}{\\omega T} (G_{QJ} (\\omega) - G_{QJ} (0)) , \\\\ \n\\label{conductivityDefs}\n\t\\kappa(\\omega) &= \\frac{i}{\\omega T} ( G_{QQ} (\\omega) - G_{QQ} (0) ) \n\\end{align}\n\nAnalytical traction may be gained in the DC limit, where these conductivities can be computed. \nAs shown in \\cite{Andrade:2013gsa} the DC electrical conductivity is given by\n\\begin{equation}\n\t\\sigma(0) = r_0^{n-1} \\left(1 + n^2\\frac{\\mu^2}{\\alpha^2} \\right)\n\\end{equation}\nwhilst the thermal and thermo-electric conductivities for general $n$ are given in \\cite{Donos:2014cya}\n\\begin{equation}\n\t\\kappa(0) = r_0^{n+1}\\frac{(4\\pi)^2 T}{\\alpha^2}, \\qquad \\beta(0) = r_0^{n}\\frac{4\\pi \\mu}{\\alpha^2}. \\label{kappaDC} \n\\end{equation}\n\nSeparately, the conductivities may be approximated analytically for small $\\alpha$ by the Drude formula. \nFor instance, at $n=1$ the thermal conductivity is given by \n\\begin{equation}\\label{drude}\n\t\\kappa(\\omega) = \\frac{\\kappa(0)}{1 - i \\omega \\tau}, \\qquad \\omega \\ll T\n\\end{equation}\nwhere $\\tau$ is the characteristic time of momentum relaxation, set by $\\alpha$. Since there is only \none characteristic time scale, we say that transport is {\\it coherent} in this regime.\\footnote{See \\cite{Hartnoll:2014lpa} for a discussion on this terminology.}\nIncreasing $\\alpha$, the deviations from \\eqref{drude} become large, driving the system into an {\\it incoherent} \nphase. This transition was first observed in this holographic system by a numerical analysis in $n=1$ \\cite{Kim:2014bza}, \nand later on also noticed in the presence of a charged scalar condensate in \\cite{Andrade:2014xca}. A closely related coherent\/incoherent\ntransition has been reported for the thermal conductivity at zero chemical potential for $n=1$ in \\cite{Davison:2014lua}, which \nfocussed on an explanation in terms of QNM: the system behaves coherently when there is an isolated, long-lived, \npurely dissipative excitation in the spectrum. Moreover, this analysis was extended in perturbation theory to include \nchemical potential \\cite{Davison:2015bea}, with qualitatively similar results. \n \n\n\\subsection{Master fields}\n\\label{sec:MF}\nA general approach to computing the conductivities in the background \\eqref{NL ansatz}-\\eqref{axion gauge field} utilises a minimal, consistent set of perturbations,\n\\begin{equation}\\label{linear perts}\n \\delta A = e^{- i \\omega t} a(r) dx^1, \\qquad \t\\delta (ds^2) = 2 e^{- i \\omega t} r^2 h(r) dt dx^1 , \n \\qquad \\delta \\psi_1 = e^{- i \\omega t} \\alpha^{-1} \\chi(r).\n\\end{equation}\nThe linearised equations of motion which govern the perturbations \\eqref{linear perts} can be written as\n\\begin{align}\n\ta'' + \\left[ \\frac{f'}{f} + \\frac{(n-1)}{r} \\right] a' + \\frac{\\omega^2}{f^2} a + \\frac{\\mu n}{f} \\frac{r_0^n}{r^{n-1}} h' &= 0 \\\\\n\t\\chi'' + \\left[ \\frac{f'}{f} + \\frac{(n+1)}{r} \\right] \\chi' + \\frac{\\omega^2}{f^2} \\chi - \\frac{i \\omega \\alpha^2}{f^2} h &=0 \\\\\n\t\\frac{i \\omega r^2}{f} h' + \\frac{i \\omega n \\mu}{f} \\frac{r_0^n}{r^{n+1}} a - \\chi' &=0 \n\\end{align}\nwhere primes denote derivatives with respect to $r$. For odd $n$, the near boundary expansions for the physical fields are given by \n\\begin{align}\n\\label{UV phys1}\n\th &= h^{(0)} + \\ldots + \\frac{h^{(n+2)}}{r^{n+2}} \t + \\ldots\\\\\n\ta &= a^{(0)} + \\ldots + \\frac{a^{(n)}}{r^n} + \\ldots \\\\\n\\label{UV phys3}\n\t\\chi &= \\chi^{(0)} + \\frac{\\chi^{(1)}}{r} + \\frac{\\chi^{(2)}}{r^2} + \\ldots\n\\end{align}\nFor even $n$, the expansions \\eqref{UV phys1}-\\eqref{UV phys3} contain logarithms, as a result of the Weyl anomaly \npresent in even boundary dimensions \\cite{Henningson:1998gx}. These terms will play no role in the following, so we \nshall omit them. \nThe terms $\\chi^{(1)} $, $\\chi^{(2)} $ are fixed by the equations of motion as\n\\begin{equation}\\label{chi 1 2}\n\t\\chi^{(1)} = 0 , \\qquad \\chi^{(2)} = \\frac{\\omega( \\omega \\chi^{(0)} - i \\alpha^2 h^{(0)})}{2 n}\n\\end{equation}\nThe gauge invariant sources for the electric and thermal conductivity are $a^{(0)}$ and \n$ s^{(0)} =\\omega \\chi^{(0)} - i \\alpha^2 h^{(0)} $, respectively (see e.g.\\ \\cite{Donos:2013eha}).\n\nAs shown in \\cite{Andrade:2013gsa}, the perturbation equations can be decoupled in terms of two gauge invariant master \nfields $\\Phi_\\pm$, given by \n\\begin{equation}\\label{MF def}\n f r \\chi' = \\frac{\\omega}{\\mu} ( \\tilde c_+ \\Phi_+ + \\tilde c_- \\Phi_- ), \\qquad\n\ta = - i (\\Phi_+ + \\Phi_-)\n\\end{equation}\n\\noindent where\n\\begin{equation}\\label{cpm}\n\t\\tilde c_\\pm = \\frac{1}{2 r_0^n} \\left\\{ (n+2) m_0 \\pm [ (n+2)^2 m_0^2 + 4 r_0^{2n} \\mu^2 \\alpha^2 ]^{1\/2} \\right \\}.\n\\end{equation}\n\n\\noindent The master fields are governed by the equations \n\\begin{equation}\\label{MF eqs pm}\n\tr^{3-n} ( f r^{n-1} \\Phi_\\pm' )' + \\left( \\frac{r^2 \\omega^2}{f} - \\frac{n^2 \\mu^2 r_0^{2n}}{r^{2n}} + \n\tn \\tilde c_\\pm \\frac{r_0^n}{r^n} \\right) \\Phi_\\pm = 0.\n\\end{equation}\n\\noindent As shown in \\cite{Son:2002sd}, in order to obtain the retarded correlators the fluctuations must satisfy ingoing boundary conditions \nat the black hole horizon. These can be implemented by simply imposing the ingoing condition on the master field \n\\cite{Berti:2009kk}, which amounts to\n\\begin{equation}\\label{ingoing bc}\n\t\\Phi_\\pm(r) = (r - r_0)^{- i \\omega\/(4 \\pi T)} ( \\Phi_\\pm^H + \\ldots ), \\qquad {\\rm near } \\; r = r_0\n\\end{equation}\n\\noindent where $\\Phi_\\pm^H$ are arbitrary constants and the ellipsis denotes regular subleading terms. \n\nThe near boundary asymptotics of the master fields are given by \n\\begin{equation}\\label{UV MF}\n\t\\Phi_\\pm = \\Phi^{(0)}_\\pm + \\ldots + \\frac{1}{r^{n}} \\Phi^{(n)}_\\pm + \\ldots\n\\end{equation}\nFrom \\eqref{UV phys1}-\\eqref{UV phys3} and \\eqref{MF def}, we learn that the asymptotic data in \\eqref{UV MF} \nis related to the physical asymptotic data as\n\\begin{align}\n\t\\Phi^{(0)}_\\pm &= \\pm \\frac{1}{\\omega(\\tilde c_- - \\tilde c_+)} ( 2 \\mu \\chi^{(2)} + i \\omega \\tilde c_- a^{(0)} ) \\\\\n\t\\Phi^{(n)}_\\pm &= \\pm \\frac{i}{\\omega^2(\\tilde c_- - \\tilde c_+)} ( - \\alpha^2 (n+2) \\mu h^{(n+2)} + \\omega^2 \\tilde c_- a^{(n)} ) \n\\end{align}\n\\noindent where $\\chi^{(2)}$ is related to the gauge invariant source for the stress tensor by \\eqref{chi 1 2}.\nIn order to compute the two-point functions at $\\mu\\neq 0$, a detailed computation of the on-shell action is needed due to the \nnon-trivial interplay between the physical sources and vevs in $\\Phi_\\pm$\\footnote{This computation was carried out\nfor $n=1$ in \\cite{Davison:2015bea}.}. However, it is easy to see that in order to obtain the poles in such correlators \nit suffices to solve for the spectra of $\\Phi_\\pm$ with Dirichlet boundary conditions $\\Phi^{(0)}_\\pm =0$. \n\n\n\n\n\\subsubsection{The neutral case}\n\\label{neutral master}\n\nFor $\\mu = 0$, all the gauge invariant information is contained in the thermal conductivity. To compute it, the \nrelevant fluctuations are \\eqref{linear perts} with $a(r) = 0$. The physical boundary data satisfies \\eqref{chi 1 2} \nand the gauge invariant source for the stress tensor is again $ s^{(0)}$. \nVia simple manipulations of the equations of motion, we can derive the master field equation\n\\begin{equation}\\label{MF eq neutral}\n\tr^{3-n} ( f r^{n-1} \\Phi' )' + \\left( \\frac{r^2 \\omega^2}{f} + n (n+2) \\frac{m_0}{r^n} \\right) \\Phi = 0 \n\\end{equation}\n\\noindent where the master field $\\Phi$ is given by\n\\begin{equation}\\label{Phi def}\n\t\\Phi = \\frac{f r \\chi'}{i\\omega}\n\\end{equation}\nNote that \\eqref{MF eq neutral} is the $\\mu \\to 0$ limit of the equation for $\\Phi_+$ \\eqref{MF eqs pm} with $m_0 \\geq 0$.\nEquation \\eqref{MF eq neutral} has been previously derived\nfor $n=1$ in \\cite{Davison:2014lua}.\nAs in the $\\mu \\neq 0$ case, the UV asymptotics for $\\Phi$ can be written as\n\\begin{equation}\\label{UV Psi}\n\t\\Phi = \\Phi^{(0)} + \\ldots + \\frac{\\Phi^{(n)}}{r^{n}} + \\ldots\n\\end{equation}\n\\noindent where once again we are not writing down the terms involving $ \\log r $ which are present for \neven $n$. The independent coefficients in \\eqref{UV Psi} are related to the boundary data \\eqref{UV phys1} and \\eqref{UV phys3}\nby\n\\begin{equation}\\label{Phi UV data}\n\t\\Phi^{(0)} = \\frac{ i\\omega \\chi^{(0)} + \\alpha^2 h^{(0)}}{n}, \\qquad \n\t\\Phi^{(n)} = \\frac{ (n+2) \\alpha^2}{\\omega^2} h^{(n+2)}\n\\end{equation}\n\nUp to an overall $\\omega$-independent factor, $\\xi$, which we will fix later using the DC results, the two-point function $G_{QQ}$ can be written as \n\\begin{equation}\\label{G2 neutral}\n\tG_{QQ} = \\xi\\frac{\\Phi^{(n)}}{\\Phi^{(0)}} \n\\end{equation}\nHere we have chosen a renormalization scheme in which all local contributions to \\eqref{G2 neutral} are removed by \ncounterterms \\cite{deHaro:2000vlm}. \n\n\n\\section{QNM frequencies}\n\\label{sec:QNM}\n\n\nFinding analytical solutions to the master field equations \\eqref{MF eqs pm} and \\eqref{MF eq neutral} for general \nvalues of the parameters seems out of reach. Closely following \\cite{Emparan:2013moa, Emparan:2014cia, Emparan:2014aba, \nEmparan:2015rva}, we obtain perturbative \nsolutions using $1\/n$ as the expansion parameter. \nIn this section we will find expressions for the decoupled QNM for $\\mu \\neq 0$ \nto order $n^{-1}$ and for $\\mu = 0 $ to order $n^{-3}$, finding good agreement with numerical calculations at finite $n$\nin a certain region of parameter space. \nIn section~\\ref{sec:conductivity} we will carry out the computation of the AC thermal conductivity to order $n^{-2}$, obtaining a result consistent with our \nQNM calculation. \n\n\nAs explained in \\cite{Emparan:2014cia, Emparan:2014aba, Emparan:2015rva}, the spectrum of QNM in the large $n$ limit splits into decoupled \nmodes, which are normalisable in the near horizon geometry, and non-decoupled modes, which are not. The latter \nare shared by many black holes so we do not expect to obtain information about the conductivities in this set of \nmodes, since, in particular, they are part of the spectra of black holes which are translationally invariant along \nthe boundary directions.\nWe focus on the decoupled modes and find that they indeed correspond to `Drude poles', i.e.\\ they are the purely \nimaginary modes which control the relaxation time of the system. As stated in \\cite{Emparan:2014cia, Emparan:2014aba, Emparan:2015rva}, \na necessary condition for the existence of decoupled QNMs is the presence of negative minima in the effective potential $V_{\\pm}$ defined by recasting the master field \nequation as\n\\begin{equation}\n\t\\left( \\frac{d^2}{d r_*^2} + \\omega^2 - V_{\\pm} \\right) \\Psi_\\pm = 0\n\\end{equation}\n\\noindent where $d r_* = dr \/f(r)$ is the tortoise coordinate. This form can be achieved by letting $ \\Phi_\\pm(r) = r^{(1-n)\/2} \\Psi_\\pm(r)$ \nin the master field equations \\eqref{MF eqs pm} and \\eqref{MF eq neutral}. By examining $V_{-}$, we conclude that there are \nno decoupled QNMs for $\\Phi_-$. \n\nWhen taking the $n \\to \\infty$ limit, it is important to assign the scaling with $n$ of different physical \nquantities. Our goal is to capture the effects of momentum relaxation, so we will rescale quantities as appropriate so that $\\alpha$ appears at infinite $n$. More concretely, we will \ntake the $n \\to \\infty$ limit holding $r_0$, $\\mu$ and $\\hat \\alpha$ fixed, where\n\\begin{equation}\n \t\\hat \\alpha = \\frac{\\alpha}{\\sqrt{n}}.\n\\end{equation} \nThis scaling mirrors the scaling of momenta required in \\cite{Emparan:2015rva}.\nIt is convenient to define the radial variable $\\rho$ by\n\\begin{equation}\n \t\\rho = \\left( \\frac{r}{r_0} \\right)^n.\n\\end{equation} \nHere we work with $r_0=1$. We will keep $\\rho$ finite as $n \\to \\infty$, performing expansions of\n\\begin{equation}\nr = \\rho^{1\/n} = 1 + \\frac{1}{n}\\log{\\rho} + \\ldots\n\\end{equation} \nwhich takes us into the horizon region.\nIn order to obtain the perturbative solution we are after, we postulate the following expansions for the fields and the \nQNM frequency $\\omega = \\omega_\\pm$,\n\\begin{equation}\\label{n expansions}\n\t\\Phi_\\pm(\\rho) = \\sum_{i = 0} \\frac{\\Phi_{\\pm,i}(\\rho)}{n^i}, \\qquad \\omega_\\pm = \\sum_{i = 0} \\frac{\\omega_{\\pm,i}}{n^i}\n\\end{equation}\nOur boundary conditions are normalisability in the near horizon, i.e.\\ \n\\begin{equation}\n\t\\Phi_{\\pm,i}(\\rho) \\to 0 , \\qquad {\\rm at} \\, \\, \\rho \\to \\infty \n\\end{equation}\n\\noindent and ingoing boundary conditions at the horizon. These can be written as boundary conditions for the $\\Phi_{\\pm,i}$\nin \\eqref{n expansions} by expanding \\eqref{ingoing bc} in powers of $1\/n$. \nThe remainder of the computation of the decoupled QNM proceeds in close parallel to the one described in \n\\cite{Emparan:2015rva}, and we shall simply quote our results.\n\nFor $\\Phi_+$, we find decoupled QNMs with frequencies\n\\begin{align}\n\\omega_{+} &= -i \\hat\\alpha ^2\\left\\{\\frac{ \\left(2-\\hat\\alpha ^2\\right)}{2-\\hat\\alpha ^2+\\mu ^2} - \\frac{1}{n}\\left[\\frac{2 \\hat\\alpha^2}{\\left(2-\\hat\\alpha ^2+\\mu ^2\\right) } \\, \\log \\left(\\frac{2-\\hat\\alpha ^2}{2-\\hat\\alpha ^2-\\mu ^2}\\right) \\right.\\right. \\nonumber\\\\\n &\\phantom{=\\ }\\left.\\left. +\\frac{2 \\left(2 - \\hat\\alpha^2\\right)^3 + \\left(12 - \n 8 \\hat\\alpha^2 + \\hat\\alpha^4\\right) \\mu^2 + \\left(2 - 3 \\hat\\alpha^2\\right) \\mu^4 }{\\left(2-\\hat\\alpha ^2+\\mu ^2\\right)^3 } \\right]+O\\left(n^{-2}\\right)\\right\\}. \\label{eq:QNMcharged}\n\\end{align}\nWe find that it is impossible to satisfy the boundary conditions for $\\Phi_-$, so we conclude that there are no decoupled QNMs for this field, as argued above. \nIn the $\\mu=0$ case we are able to obtain two higher orders in the expansion for the $\\Phi$ frequency:\\footnote{Interestingly, these QNM frequencies can be obtained directly from the QNMs of black branes in AdS without momentum relaxation \\cite{Emparan:2015rva} by mapping the momenta $\\hat{q}^2 = \\hat\\alpha^2\/2$ and the spatial metric curvature parameter $K=-\\hat\\alpha^2\/2$.}\n\\begin{align}\n\\omega &= -i \\hat{\\alpha}^2 \\left\\{1 - \\frac{2}{n} - \\frac{2\\left(12+(\\pi^2-6)\\hat{\\alpha}^2\\right)}{3\\left(\\hat{\\alpha}^2-2\\right)n^2} \\right. \\nonumber\\\\\n &\\phantom{=\\ }\\left.+\\frac{8\\left[-12+\\hat{\\alpha}^2\\left((\\hat{\\alpha}^2 -4)(\\pi^2-3)-3(\\hat{\\alpha}^2 +2)\\zeta(3)\\right)\\right]}{3\\left(\\hat{\\alpha}^2-2\\right)^2 n^3}+O\\left(n^{-4}\\right)\\right\\} \\label{eq:QNMneutral}.\n\\end{align} \n\nA notable feature of the frequencies \\eqref{eq:QNMcharged} and \\eqref{eq:QNMneutral} is a breakdown of the expansion when $\\hat\\alpha^2+\\mu^2 =2$. In fact this behaviour could have been predicted by examining a large~$n$ expansion of the DC thermal conductivity, \\eqref{kappaDC},\n\\begin{equation}\n\\kappa(0) = \\kappa(0)|_{n\\to\\infty} \\left(1 + \\frac{4 + \\mu^2}{(2-\\hat\\alpha^2 - \\mu^2)n}+ O\\left(n^{-2}\\right)\\right).\n\\end{equation}\nThis breakdown can be traced back to a change in the way that the temperature scales with $n$ at large $n$:\n\\begin{equation}\nT = \\frac{(2 - \\hat\\alpha^2 -\\mu^2)n}{8 \\pi} + O\\left(n^{0}\\right).\n\\end{equation}\nConsequently, in order to examine the point $\\hat\\alpha^2+\\mu^2 =2$ we must repeat our large $n$ analysis there. For $\\mu=0$ and $\\hat\\alpha^2 =2$ the master field equation \\eqref{MF eq neutral} can be solved exactly for any $n$. This generalises the analysis performed at $n=1$ in \\cite{Davison:2014lua}. The additional divergence in the $\\mu\\neq 0$ case \\eqref{eq:QNMcharged} at $2+\\mu^2 = \\hat\\alpha^2$ coincides with the change of $n$ scaling of the mass parameter of the background solution, and occurs at a higher value of $\\hat\\alpha^2$ than the divergence discussed above. \n\nMoving on, we would like to compare these large $n$ analytical expressions \\eqref{eq:QNMcharged} and \\eqref{eq:QNMneutral} with finite $n$ numerics. For clarity we focus on $\\mu=0$, for which the comparison is presented in figure~\\ref{QNMplot} for values $n=1,11$ and $101$. The $n=1$ case was previously analysed numerically in \\cite{Davison:2014lua} wherein it was noted that a pole collision occurred as $\\alpha$ was dialled. In this figure~\\ref{QNMplot}, we demonstrate that there are numerous such pole collisions at $n=1$, indicated by each extremum of the curve. For $n>1$ we find only one pole collision. Interestingly, the oscillations are centred on the critical value $\\hat\\alpha = \\sqrt{2}$ discussed above, and the locations where the crossings occur coincide with the existence of analytic regular, normalisable modes whose frequencies have integer imaginary part as discussed in appendix \\ref{appendixCritical}. We discuss these collisions in the context of a transition from coherent to incoherent behaviour in the conclusions, section \\ref{sec:conclusions}.\n\nFinally, we note that there is excellent agreement at sufficiently large finite $n$ between the numerical results and the large $n$ expansion, which interestingly includes the breakdown near $\\hat\\alpha = \\sqrt{2}$.\n\n\\begin{figure}[h!]\n\\begin{center}\n\\includegraphics[width=0.9\\textwidth]{qnmTripA}\\\\\n\\includegraphics[width=0.45\\textwidth]{qnmTripB}\\\\\n\\begin{picture}(0.1,0.1)(0,0)\n\\put(-160,240){\\makebox(0,0){$n=1$}}\n\\put(45,240){\\makebox(0,0){$n=11$}}\n\\put(-55,120){\\makebox(0,0){$n=101$}}\n\\put(-215,225){\\makebox(0,0){$-\\Im(\\omega)$}}\n\\put(-115,100){\\makebox(0,0){$-\\Im(\\omega)$}}\n\\put(0,10){\\makebox(0,0){$\\hat\\alpha$}}\n\\put(-105,135){\\makebox(0,0){$\\hat\\alpha$}}\n\\put(105,135){\\makebox(0,0){$\\hat\\alpha$}}\n\\end{picture}\n\\caption{Purely imaginary quasi-normal mode frequencies of the linear axion black brane at $\\mu=0$ as a function of $\\hat\\alpha = \\alpha\/\\sqrt{n}$ computed numerically for various $n$ as labelled (solid curves). The dashed lines show the large $n$ analytical counterpart \\eqref{eq:QNMneutral} computed to order $n^{-2}$. The vertical dotted line is the `critical' value $\\hat\\alpha = \\sqrt{2}$, where the master field equation can be solved analytically for any $n$, giving the integer crossing frequencies. The $n=1$ case shows several pole collisions, indicated by each turn over of the curve, whilst for $n>1$ we see only one collision for this range. Units are given by $r_0=1$.\\label{QNMplot}}\n\\end{center}\n\\end{figure}\n\n\n\\section{AC conductivity}\n\\label{sec:conductivity}\n\nIn this section we compute the AC thermal conductivity for the neutral theory to order $n^{-2}$, and compare the resulting expressions with numerics. Specifically we look at the frequency range which captures the decoupled mode describing the essential momentum relaxation physics, i.e.\\ we take $\\omega = O\\left(n^0\\right)$.\n\nThe computation begins with the approach outlined in \\cite{Emparan:2013moa}. The basic structure of the calculation is a matched asymptotic expansion made possible by the new small scale $r_0\/n$, corresponding to the localisation of gradients near the horizon. This new scale allows us to separate the bulk geometry into a near and far zone, defined as follows:\n\\begin{eqnarray}\n\\text{near zone:}\\quad & r-r_0\\ll r_0, & \\quad \\log \\rho \\ll n\\\\\n\\text{far zone:}\\quad & r-r_0\\gg \\frac{r_0}{n}, &\\quad \\log \\rho \\gg 1.\n\\end{eqnarray}\nAs we have previously, we shall take $r_0=1$. Note that these zones overlap: in particular, the overlap zone is described by $\\log \\rho \\gg 1$ in the near zone, and $\\log \\rho \\ll n$ in the far zone. Thus the calculation proceeds by solving in both zones and matching at the overlap; the near zone will allow us to imprint the ingoing horizon boundary conditions on the solution, whilst the far zone will enable us to read off the normalisable and non-normalisable data and allow the computation of the two-point function. \n\n\\subsection{Near zone}\nThe near zone is reached by taking the large $n$ limit whilst working at fixed $\\rho$. The calculation proceeds similarly to the QNM calculation and so we will be brief. A key difference is that we do not wish to impose normalisablity, and so the frequencies are not quantised. As before, we expand $\\Phi(\\rho) = \\sum_{i = 0} \\frac{\\Phi_i(\\rho)}{n^i}$, but we do not expand $\\omega$. After imposing the ingoing boundary conditions we obtain, \n\\begin{eqnarray}\n\\Phi_{0} &=& \\frac{a_0}{\\rho}\\\\\n\\Phi_{1} &=& \\frac{2 i a_0 \\omega}{(\\hat\\alpha^2-2)}\\frac{\\log{(\\rho-1)}}{\\rho} - \\frac{2 a_0(\\hat\\alpha^2 - i \\omega)}{\\hat\\alpha^2 -2}\\frac{\\rho-1}{\\rho}\n\\end{eqnarray}\ntogether with explicit expressions for $\\Phi_{2}$ and $\\Phi_{3}$ which we have omitted here. $a_0$ is an unconstrained integration constant. Finally, in the overlap region, we have\n\\begin{eqnarray}\n\\Phi_{0} &=& \\frac{a_0}{\\rho}\\\\\n\\Phi_{1} &=& -2a_0\\frac{(\\hat\\alpha^2-i\\omega)}{\\hat\\alpha^2 -2} \\left(1-\\frac{1}{\\rho}\\right) + 2 a_0 \\frac{i \\omega}{\\hat\\alpha^2 -2}\\frac{\\log\\rho}{\\rho} - a_0 \\frac{2 i \\omega}{\\hat\\alpha^2-2}\\frac{1}{\\rho^2}.\n\\end{eqnarray}\nwhere again we have evaluated the overlap expressions for $\\Phi_{2}$ and $\\Phi_{3}$ but we omit them here in the interest of keeping the presentation concise.\n\n\\subsection{Far zone and matching}\nAt leading order the far zone equations can be obtained by removing any terms which decay exponentially fast with $n$ \\cite{Emparan:2013moa}. More generally, an expansion can be formed by counting powers of $r^{-n}$ in the equations of motion after inserting\n\\begin{equation}\n\\Phi = \\phi + r^{-n} \\psi + \\ldots.\n\\end{equation}\nLet us introduce a counting parameter $\\lambda$ for this purpose, i.e. we count $\\phi$ as order $\\lambda^0$. In order to obtain the conductivity we need the coefficient of $r^{-n}$, and so we need to go to order $\\lambda^1$.\nAt order $\\lambda^0$ the master field equation becomes\n\\begin{equation}\n{\\cal D}\\phi = 0,\\qquad {\\cal D} \\equiv \\partial_r^2 - \\frac{(n-1)\\hat{\\alpha}^2 - 2(n+1) r^2}{2r^3 - \\hat{\\alpha}^2 r} \\partial_r + \\frac{4 \\omega^2}{(\\alpha^2-2r^2)^2}. \\label{lambda0}\n\\end{equation}\nThis equation can be solved explicitly in terms of Gauss hypergeometric functions,\n\\begin{eqnarray}\n\\phi &=& \\left(1-\\frac{\\hat{\\alpha}^2}{2 r^2}\\right)^{-\\frac{i \\omega}{\\sqrt{2}\\hat{\\alpha}}} \\Bigg(A\\; _2F_1\\left(-\\frac{i \\omega}{\\sqrt{2}\\hat{\\alpha}}, 1- \\frac{n}{2} - \\frac{i \\omega}{\\sqrt{2}\\hat{\\alpha}}, 1-\\frac{n}{2}, \\frac{\\hat{\\alpha}^2}{2r^2}\\right)\\nonumber\\\\\n&& + r^{-n} B\\; _2F_1\\left(1-\\frac{i \\omega}{\\sqrt{2}\\hat{\\alpha}}, \\frac{n}{2} - \\frac{i \\omega}{\\sqrt{2}\\hat{\\alpha}}, 1+\\frac{n}{2}, \\frac{\\hat{\\alpha}^2}{2r^2}\\right) \\Bigg)\\label{phifar}\n\\end{eqnarray}\nwhere $A$ and $B$ are integration constants; $A$ will contribute to the non-normalisable part of $\\Phi$ at infinity, $\\Phi^{(0)}$, whilst $B$ will contribute to the normalisable part, $\\Phi^{(n)}$. To find this in the overlap region we need to use expressions for the Gauss hypergeometric functions for large parameters. Expanding $\\phi$ in powers of $n$ and similarly for the integration constants $A$ and $B$, we find, \n\\begin{eqnarray}\n\\phi_0 &=& A_0 - \\frac{2B_0}{(\\hat\\alpha^2 - 2)\\rho}\\label{far0overlap}\\\\\n\\phi_1 &=& A_1 - \\frac{A_0\\omega^2}{\\hat\\alpha^2-2} + \\frac{1}{\\rho} \\left(-\\frac{2B_1}{\\hat\\alpha^2-2} -2 B_0\\frac{2\\hat\\alpha^2 + \\omega^2}{(\\hat\\alpha^2-2)^2} -4 B_0\\frac{\\hat\\alpha^2}{(\\hat\\alpha^2-2)^2} \\log\\rho\\right)\\label{far0overlapB}\n\\end{eqnarray}\ntogether with similar expressions for $\\phi_2$ and $\\phi_3$. Subscripts denote the power of $1\/n$ for which it is a coefficient.\n\nAt next order in $\\lambda$, the equation for $\\psi$ is sourced by $\\phi$:\n\\begin{eqnarray}\nr^n{\\cal D} \\left(\\frac{\\psi}{r^n} \\right) &=& {\\cal S} \\label{DS}\n\\end{eqnarray}\nwhere the operator ${\\cal D}$ is defined in \\eqref{lambda0} and where\n\\begin{align}\n{\\cal S} &= r^{-1} \\frac{(2-\\hat\\alpha^2) \\left(n \\hat\\alpha^2 - 2(2+n) r^2\\right)}{(\\hat\\alpha^2-2r^2)^2} \\phi' \\nonumber\\\\\n&\\phantom{=\\ }+ r^{-2} \\frac{2-\\hat\\alpha^2}{2r^2-\\hat\\alpha^2}\\left(-n(2+n) - \\frac{8 \\omega^2 r^2}{(2r^2-\\hat\\alpha^2)^2}\\right)\\phi.\n\\end{align}\nUnlike for $\\phi$ we have not directly integrated this equation. However, we can do so order-by-order in a large $n$ expansion at fixed $r$ provided we include the correct non-perturbative contributions. To the order of $n$ considered these turn out to be,\n\\begin{eqnarray}\n\\psi &=& \\left(\\psi_{B,0}(r) + \\frac{\\psi_{B,1}(r)}{n} + \\frac{\\psi_{B,2}(r)}{n^2} + \\frac{\\psi_{B,3}(r)}{n^3} +O\\left(n^{-4}\\right) \\right)\\nonumber\\\\\n&&+ r^{-n}\\left(\\psi_{C,0}(r) + \\frac{\\psi_{C,1}(r)}{n} + \\frac{\\psi_{C,2}(r)}{n^2} + \\frac{\\psi_{C,3}(r)}{n^3} + O\\left(n^{-4}\\right) \\right). \\label{nonpert}\n\\end{eqnarray}\nWe can solve for each $\\psi_{B,i}$ and $\\psi_{C,i}$ provided $A_0=0$, which as we shall see shortly is consistent with the required value from the matching calculation. Each term is required for the matching calculation to work and is straightforward to obtain. This method is more efficient than solving \\eqref{DS} at arbitrary $n$ and then expanding, as in \\cite{Emparan:2013moa}. Applied to $\\phi$ above, this method gives the same result as the expansion of \\eqref{phifar}.\n\nExpressing \\eqref{nonpert} in the overlap region and combining with \\eqref{far0overlap} and \\eqref{far0overlapB} gives us $\\Phi$ in the overlap zone, which can be matched with the expression coming from the near zone calculation. This fixes the coefficients appearing in \\eqref{far0overlap},\\eqref{far0overlapB} together with additional integration constants which arise in each of the $\\psi_{B,i}$. For example, \n\\begin{align}\nA_0 &= 0,\\\\\nA_1 &= \\frac{2 a_0(\\hat\\alpha^2-i\\omega)}{2-\\hat\\alpha^2}\\\\\nA_2 &= -\\frac{2 a_0(-i \\omega^3 + \\hat\\alpha^2(4-2i\\omega+\\omega^2))}{(2-\\hat\\alpha^2)^2}\\\\\n\\nonumber\nA_3 &= \\frac{a_0}{3\\left(\\hat{\\alpha }^2-2\\right)^3} \n\\bigg\\{ \\hat{\\alpha }^4 \\left(-6 \\omega ^2-4 i \\pi ^2 \\omega \\right) \\\\\n &-\\hat{\\alpha }^2 \\left[ 3 \\left(\\omega ^4-6 i \\omega\n ^3+8 \\omega ^2+32\\right)+4 \\pi ^2 \\omega (\\omega +2 i)\\right] + i \\omega ^2 \\left(3 \\omega ^3-4 \\pi ^2 \\omega +48 i\\right) \\bigg\\}\n\\end{align}\nThe $B_i$ coefficients are given in relation to the coefficients appearing in $\\psi_{B,i}$. These coefficients determine $\\Phi^{(0)}$ and $\\Phi^{(n)}$ to order $n^{-3}$. Note that since $\\psi$ does not contribute to $\\Phi^{(0)}$, and $A_0=0$, the non-normalisable data $\\Phi^{(0)}$ vanishes to leading order in $n$ and so the Green's function will grow with $n$.\n\n\\subsection{Results}\nCombining the asymptotic results for $\\phi$ and $\\psi$ discussed above brings us to the main result of this section --- the thermal conductivity \\eqref{conductivityDefs} to order $n^{-2}$: \n\\begin{eqnarray}\n\\nonumber\n&& \\kappa(\\omega) = 2\\pi \\frac{2-\\hat\\alpha^2}{\\hat\\alpha^2-i \\omega} \\\\\n\\nonumber\n&&+\\frac{4 \\pi \\left(\\hat\\alpha^4 (2+\\omega (\\omega -i))-i \\hat\\alpha^2 \\omega ^3+2 i \\omega \\left(\\hat\\alpha^2-i\n \\omega \\right)^2 \\log \\left(2-\\hat\\alpha^2\\right)-2 i \\omega \\log (2) \\left(\\hat\\alpha^2-i \\omega \\right)^2\\right)}{n\n \\left(\\hat\\alpha^3-i \\hat\\alpha \\omega \\right)^2}\\nonumber\\\\\n\\nonumber\n&&-\\frac{4 \\pi \\omega }{3 n^2 \\hat{\\alpha }^4 \\left(\\hat{\\alpha }^2-2\\right)^2 \\left(\\omega +i \\hat{\\alpha }^2\\right)^3}\\bigg\\{\\\\\n\\nonumber\n&& +\\hat{\\alpha }^6 \\bigg[ \\hat{\\alpha }^6 (-(6 \\log (2-\\hat{\\alpha }^2 )+\\pi ^2+6-6 \\log (2) )) \\\\\n\\nonumber\n&& +4 \\hat{\\alpha }^2 (6 \\log (2-\\hat{\\alpha }^2 )+\\pi ^2+12-6 \\log (2) )-48 \\bigg]\\\\\n\\nonumber\n&& +i \\hat{\\alpha }^6 \\bigg[ (\\hat{\\alpha }^2-2 ) (3 (\\hat{\\alpha }^2+\\pi ^2+4 ) \\hat{\\alpha }^2+2 (\\pi ^2-6)) \\\\\n\\nonumber\n&&+18 (\\hat{\\alpha }^4-4 ) \\log (2-\\hat{\\alpha }^2 ) -18 (\\hat{\\alpha }^4-4 ) \\log (2) \\bigg] \\omega\\\\\n\\nonumber\n&& +\\hat{\\alpha}^4 \\bigg[ -12 \\hat{\\alpha }^6 (1+\\log (2))+2 \\hat{\\alpha }^4 (\\pi ^2+15 (1+\\log (2))) \\\\\n\\nonumber\n&& -4 \\hat{\\alpha }^2 (3+\\pi ^2+\\log (4096))\n +6 (\\hat{\\alpha }^2-2) (2 \\hat{\\alpha }^4-\\hat{\\alpha }^2+6) \\log (2-\\hat{\\alpha }^2 )+72 \\log (2) \\bigg] \\omega ^2\\\\\n\\nonumber\n&& -3 i \\hat{\\alpha }^2 \\bigg[ \\hat{\\alpha }^8-3 \\hat{\\alpha }^6 (5+\\log (16))+\\hat{\\alpha }^4 (28+46 \\log (2))-4 \\hat{\\alpha }^2\n (1+\\log (4096)) \\\\\n \\nonumber\n&& +2 (\\hat{\\alpha }^2-2) (6 \\hat{\\alpha }^4-11 \\hat{\\alpha }^2+2) \\log (2-\\hat{\\alpha }^2)+\\log (256) \\bigg] \\omega ^3\\\\\n\\nonumber\n&& +6 \\hat{\\alpha }^2 \\left(\\hat{\\alpha }^2-2\\right) \\bigg[-\\hat{\\alpha }^4+6 \\hat{\\alpha }^2 (1+\\log (2))-6 \\left(\\hat{\\alpha }^2-2\\right) \\log \\left(2-\\hat{\\alpha }^2\\right)-12 \\log (2)\\bigg] \\omega ^4\\\\\n\\nonumber\n&& +3 i \\bigg[ \\hat{\\alpha }^6-2 \\hat{\\alpha }^4 (3+\\log (4))+8 \\hat{\\alpha }^2 (1+\\log (4)) \\\\\n&& +4(\\hat{\\alpha }^2-2)^2 \\log (2-\\hat{\\alpha }^2 )-16 \\log (2) \\bigg ] \\omega ^5 \\bigg\\} +O\\left(n^{-3}\\right). \\label{conductivityfinal}\n\\end{eqnarray}\nWe have fixed the overall normalisation given by $\\xi$ in \\eqref{G2 neutral} by comparing with the DC value \\eqref{kappaDC}:\n\\begin{equation}\n\\kappa(0) = 2\\pi\\left(\\frac{2}{\\hat\\alpha^2}-1\\right) \\left(1+ \\frac{4}{(2-\\hat\\alpha^2)n}\\right). \\label{kappaDCNetural} \n\\end{equation}\nWe find $\\xi = -\\hat\\alpha^2+ O(n)^{-3}$ . Interestingly, at $\\mu=0$, the expansion for $\\kappa(0)$ truncates at order $1\/n$.\n\n \nWe note that at leading order the result takes Drude form. A comparison with numerical integration for finite $n=1,3, 11,101$ is given in figure \\ref{kappaplot}, truncating at orders $n^0$, $n^{-1}$ and $n^{-2}$.\n\nLet us start with the $n^0$ approximation (black dash in figure \\ref{kappaplot}). Even at this leading order the broad features of the conductivity are well captured by the large $n$ expansion. We note that the agreement at larger frequencies is excellent. The width of the peak, which is related to the relaxation timescale, is also very good, in agreement with the approximation of the QNMs by the large $n$ expansion. The only notable discrepancy is the height of the peak. This can be easily understood: $\\kappa(0)$ receives a $1\/n$ correction \\eqref{kappaDCNetural} and no further corrections, thus the DC limit is not expected to agree at this order. By $n=101$ the agreement is good everywhere.\n\nAt order $n^{-1}$ (blue dots in figure \\ref{kappaplot}) the DC limit now agrees, as anticipated. Overall the approximation is good even at $n=1$, but now we see even for modest values of $n$ the analytical result is in excellent agreement with the numerical result, e.g.\\ at $n=11$. \n\nAt order $n^{-2}$ (red dash in figure \\ref{kappaplot}) the $n=11$ and $n=101$ results are not visibly affected. However for the lower values of $n=1,3$ the agreement becomes worse than at order $n^{-1}$. This is similar to the large $D$ approximation applied to the Gregory-Laflamme instability \\cite{Emparan:2015rva}, where it was argued that the series is asymptotic due to the existence of non-perturbative contributions.\n\n\\begin{figure}[h!]\n\\begin{center}\n\\includegraphics[width=0.9\\textwidth]{kappaTripA}\\\\\n\\includegraphics[width=0.9\\textwidth]{kappaTripB}\\\\\n\\begin{picture}(0.1,0.1)(0,0)\n\\put(-40,250){\\makebox(0,0){$n=1$}}\n\\put(155,250){\\makebox(0,0){$n=3$}}\n\\put(-40,120){\\makebox(0,0){$n=11$}}\n\\put(155,120){\\makebox(0,0){$n=101$}}\n\\put(-210,230){\\makebox(0,0){$\\Re(\\kappa)$}}\n\\put(-210,100){\\makebox(0,0){$\\Re(\\kappa)$}}\n\\put(-105,10){\\makebox(0,0){$\\omega$}}\n\\put(105,10){\\makebox(0,0){$\\omega$}}\n\\end{picture}\n\\caption{The AC thermal conductivity at $\\mu=0$ computed analytically to orders $n^0$ (black dashed), $n^{-1}$ (blue dotted) and $n^{-2}$ (red dashed) as given in \\eqref{conductivityfinal}, compared to the finite $n$ numerical result (solid) for $\\hat\\alpha = 1\/2$. Units are given by $r_0=1$.\\label{kappaplot}}\n\\end{center}\n\\end{figure}\n\n\n\\section{Conclusions}\n\\label{sec:conclusions}\n\n\nWe have studied the linear axion model defined via \\eqref{S0} when the number of spacetime dimensions\nis large, focusing on the quasi-normal modes governing momentum relaxation and the AC thermal conductivity. We have kept the horizon radius $r_0$ and the chemical potential $\\mu$ fixed in this limit. We \nfound that the influence of the momentum relaxation parameter, $\\alpha$, vanished at large $n$, but could be restored by scaling $\\alpha$ \nsuch that $\\hat\\alpha \\equiv \\alpha\/ \\sqrt{n}$ is held fixed. This places the physics of momentum relaxation at leading order in the large $D$ expansion.\n\nAn important technical point which simplifies our analysis is the existence of \nmaster field equations in arbitrary dimensions \\eqref{MF eqs pm}, \\eqref{MF eq neutral}. These decoupled wave equations contain \nall the gauge invariant information required to compute the two-point functions of interest, reducing our problem \nto calculations closely related to those already carried out in the context of General Relativity in large $D$. \n\nWe obtain analytical expressions for the QNM which control the electric and heat transport as a power series in $1\/D$. \nIn the language of \\cite{Emparan:2014aba}, these are of the decoupled kind, meaning that they are normalisable \nin the near horizon geometry. \nFor $\\mu \\neq 0$, we have computed the QNM which controls the electric conductivity for \nup to order $n^{-1}$, while for $\\mu = 0$ we obtain the thermal QNM to order $n^{-3}$. Furthermore, \nwe calculate the AC thermal conductivity for $\\mu = 0$ to order $n^{-2}$. At leading order it takes Drude form, \nand at order $n^{-1}$ it provides a good approximation even for small values of $n$, illustrating the practicality of this technique for such systems.\n\nInterestingly, our perturbative series for the QNM breaks down due to the growth of the \ncoefficients as $\\hat\\alpha \\to \\hat \\alpha_{c} \\equiv \\sqrt{2 r_0^2-\\mu^2}$. \nNumerically, we observe that the structure of lowest lying QNM \nchanges significantly as we approach $\\hat \\alpha_{c}$. For very small values of $\\hat \\alpha$ there exists an isolated, \npurely dissipative excitation which governs transport, i.e.\\ the system is in a coherent regime. Increasing $\\alpha$ towards \n$\\hat \\alpha_{c}$, the characteristic time scale of this mode decreases and it mixes with the rest of the QNM in the spectrum, so \nthat we enter an incoherent phase.\\footnote{In this regime there can be numerous pole collisions at finite $n$, including a collision between the Drude mode and a higher lying excitation.}\nWe thus interpret the breakdown of the perturbative expansion as a large $D$ signature \nof the coherent\/incoherent transition. It would be interesting to revisit our analysis with transverse wavevector $k \\neq 0$ and investigate the interplay of these QNMs with diffusion.\n\nMore generally, it is interesting to observe that in the case where we do not scale the sources of the axions with $D$, momentum conservation is restored at infinite $D$. This suggests that the large $D$ expansion may be used to improve analytical control over more generic setups incorporating inhomogeneity. We leave this possibility for future work.\n\n\\acknowledgments\n\nWe are pleased to thank Marco Caldarelli, Richard Davison, Roberto Emparan, Blaise Gout\\'eraux and Kostas Skenderis for valuable comments. \nT.A.\\ is supported by the European Research Council under the European Union's Seventh Framework Programme\n(ERC Grant agreement 307955). He also thanks the Institute of Physics at University of Amsterdam for their hospitality \nduring the completion of this work. \nS.A.G.\\ is supported by National Science Foundation grant PHY-13-13986.\nB.W.\\ is supported by European Research Council grant ERC-2014-StG639022-NewNGR. He also thanks the Department of Physics at the University of Oxford for their hospitality during the completion of this work.\nWe are grateful to Centro de Ciencias Pedro Pascual, Benasque where this work was initiated.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nOne important topic in many branches of biology is to understand evolutionary events and forces leading to current biological systems, such as a group of species or strains of a virus. To this end, evolutionary relationships among the biological system under investigation are typically represented by \na phylogenetic tree, that is, a binary tree whose leaves are labelled by the taxon units in the system. As these events and forces, such as rates of speciation and expansion, are often not directly observable~\\cite{mooers2007some,heath2008taxon}, one popular approach is to compare empirical shape indices computed from trees inferred from real datasets with those predicted by a null tree growth model\n~\\cite{blum2006random,hagen2015age}. Furthermore, topological tree shapes are also closely related to several fundamental statistics in population genetics~\\cite{ferretti2017decomposing,arbisser2018joint} and certain important parameters in \nthe dynamics of virus evolution and propagation~\\cite{colijn2014phylogenetic}. \n\n\n\nOne important family of tree shapes are balance indices, such as Colless' index, Sackin's index and the number of subtrees (see, e.g.~\\cite{fischer2021tree} and the references therein). Various properties concerning these statistics have been established in the past decades on the following two fundamental random phylogenetic tree models: \nthe Yule model (aka the Yule-Harding-Kingman (YHK) model)~\\cite{rosenberg06a,disanto2013exact,Janson2014} and the uniform model (aka the proportional to distinguishable arrangements (PDA) model)~\\cite{McKenzie2000,chang2010limit, WuChoi16,CTW19}.\nHowever, for phylogenetic trees inferred from real datasets, the Yule or uniform model may not always be a good fit~\\cite{blum2006random}, and several general classes of random trees have been proposed for modelling and analysing the observed data,\ntwo popular ones being Ford's alpha model~\\cite{Ford2006} and Aldous' beta model~\\cite{aldous96a}. \n\n\nIn this paper, we confine ourselves to Ford's alpha model, a one-parameter family of random tree growth models introduced by Daniel J. Ford in his PhD thesis \\cite{Ford2006}. \n More precisely, under the Ford model with a fixed parameter $0\\le \\alpha \\le 1$, a random tree of a given number of leaves is generated such that at any step in which a tree $T_n$ with $n$ leaves has been constructed from previous steps, a new leaf attaches to an internal edge of $T_n$ with probability $\\frac{\\alpha}{n-\\alpha}$ and to a leaf edge in $T_n$ with probability $\\frac{1-\\alpha}{n-\\alpha}$. The resulting random tree model will be referred to as the Ford model (with parameter $\\alpha$) in this paper, which is also known as the alpha tree model~(see, e.g.~\\cite{coronado2019balance}). Note that the Ford model is a family of random tree models which includes the Yule model with $\\alpha=0$, the uniform model with $\\alpha=1\/2$, and the Comb model with $\\alpha=1$. \n\nThe tree shape indices studied in this paper are the number of cherries and that of pitchforks. Here a cherry is a subtree with precisely two leaves and a pitchfork a subtree with three leaves. \nThe asymptotic properties of the number of cherries was first studied by McKenzie and Steel \\cite{McKenzie2000}, who showed that the number of cherries is asymptotically normal for the Yule and the uniform models as the number of leaves tends to infinity. Later, similar properties of the number of cherries are extended to the Ford model~\\cite[Theorem 57]{Ford2006} and to the Crump-Mode-Jagers branching process~\\cite{plazzotta2016asymptotic}. \nFor the number of pitchforks, Rosenberg~\\cite{rosenberg06a} obtained its mean and variance and Chang and Fuchs~\\cite{chang2010limit} proved that the number of pitchforks is also asymptotically normal for the Yule and the uniform models. For the joint distributions, Holmgren and Janson showed that~\\cite{Janson2014} the joint distribution is asymptotically normal for the Yule model. This was recently extended by us to the uniform model based on a uniform version of the extended urn models in which negative entries are permitted for their replacement matrices~\\cite{Paper1}. \n\n\nIn this paper, we establish the strong law of large numbers and the central limit theorem \nfor the joint distribution of cherries and pitchforks under the Ford model (Theorem~\\ref{Thm:Convg-ChPh}) \nby considering an associated nonuniform urn model (Theorem~\\ref{thm:urn:edge}). These results are presented in Section~\\ref{sec:limiting}, following Section~\\ref{sec:preliminary} in which we collect background concerning the Ford model and limiting theorems on uniform urn models.\nFurthermore, we derive a recurrence formula for computing the exact joint distribution under the Ford model~(Theorem~\\ref{jointpmf}) in Section~\\ref{sec:exact}, generalizing the results in~\\cite{WuChoi16,CTW19} for the Yule and the uniform model. This recurrence formula enables us to obtain exact expressions for the mean and variance of the number of cherries and that of pitchforks and their covariance under the Ford model. This, in-particular, generalises the exact expressions of mean and variance for the number of cherries and that of pitchforks for the Yule and the uniform models~\\cite{McKenzie2000,rosenberg06a,chang2010limit} and the number of cherries for the Ford model~\\cite[Theorem 60]{Ford2006}. \nAs an application, in Section~\\ref{sec:expansion} we obtain higher order expansions of the first and second moments of the joint distributions. \n\n\n\n\n\\section{Ford Model and Urn Model}\n\\label{sec:preliminary}\nIn this section, we first introduce the Ford model, which is a one-parameter family of random phylogenetic tree models. Next we present a nonuniform version of the extended urn models associated with the Ford tree model. Finally, we recall certain conditions on the related uniform version of the extended urn model under which the strong law of large numbers and the central limit theorem are obtained.\n\n\\subsection{Ford model}\n\nA rooted binary tree is a finite connected simple graph without cycles that contains a unique vertex of degree 1 designated as the root and all the remaining vertices are of degree 3 (interior vertices) or 1 (leaves). A phylogenetic tree with $n$ leaves is a rooted binary tree whose leaves are bijectively labelled by the elements in $\\{1,\\dots,n\\}$. Edges incident with leaves are referred to as pendant edges. \n\nUnder the Ford model with parameter $0\\le \\alpha \\le 1$, a random phylogenetic tree $T_n$ with $n$ leaves is constructed recursively by adding one leaf at a time as follows. Fix a random permutation $(x_1,\\dots,x_n)$ of $\\{1,\\dots,n\\}$. The initial tree $T_2$ contains precisely two leaves (e.g. one cherry) which are labelled as $x_1$ and $x_2$. For the recursive step, given a tree $T_m$ with $m$ leaves constructed so far, choose a random edge in $T_m$ according to the distribution that assigns weight $1-\\alpha$ to each pendant edge (i.e., those incident with a leaf) and weight $\\alpha$ to each of the other edges. The new leaf labelled $x_{m+1}$ bifurcates the selected edge and joins in the middle. \nEvery single addition of a leaf in the tree results into a replacement of the selected edge with two new edges.\nFinally, we let $A_n$ and $C_n$ denote the numbers of pitchforks and cherries in tree $T_n$, respectively.\n\n \\begin{figure}[ht]\n\t\\begin{center}\n\t\t{\\includegraphics[width=0.9\\textwidth]{1_Fig_example.pdf}}\n\t\\end{center}\n\t\\caption{A sample path of the Ford model and the associated trajectory under the urn model. (i) A sample path of the Ford model evolving from $T_2$ with two leaves to $T_6$ with six leaves. The labels of the leaves are omitted for simplicity. The type of an edge is indicated by the circled number next to it. For $2\\le i \\le 5$, the edge selected in $T_i$ to generate $T_{i+1}$ is highlighted in bold and the associated edge type is indicated in the circled number above the arrow. (ii) The associated urn model with six colours, derived from the types of pendants edges in the trees. \n\t\tIn vector form, $U_0=(0,2,0,0,1,0), U_1=(2,0,1,0,0,2), U_2=(0,4,,0,0,2,1), U_3=(2,2,1,0,1,3)$, and $U_4=(2,2,1,1,1,4)$. \t\n\t}\n\t\\label{fig:example}\n\\end{figure}\n\n\n\n\\subsection{An urn model associated with trees }\n\\label{subsect:urn:tree}\n\nConsider an urn containing balls of $d$ different colours where colours are denoted by integers $\\{1,2,\\dots, d\\}$. Let $U_n=(U_{n,1},\\dots, U_{n,d})$ be the configuration vector of length $d$ such that the $i$-th element of $U_n$ is the number of balls of colour $i$ at time $n$. Let $U_0$ be the initial vector of colour configuration, then at every time $n\\geq 1$, a ball is selected uniformly at random from the urn and if the colour of the selected ball is $i$ then the ball is replaced along with $R_{i,j}$ many balls of colour $j$, for every $1 \\leq j \\leq d$. The dynamics of the urn configuration depends on its initial configuration $U_0$ and the $d \\times d$ replacement matrix $R = (R_{i,j})_{1\\leq i,j\\leq d}$.\n\n\nWe study the limiting properties of the numbers of cherries and pitchforks via an equivalent urn process. Towards this, we use six different colours and assign one colour to each type of edges of the tree in the following scheme introduced in~\\cite{Paper1}: colour $1$ for all pendant edges of a cherry in a pitchfork; colour $2$ for pendant edges of a cherry not contained in a pitchfork; colour $3$ for pendant edges in a pitchfork but not in any cherry; colour $4$ for pendant edges in neither a cherry nor a pitchfork; colour $5$ for internal edges adjacent to a cherry but not in a pitchfork (i.e., those adjacent to colour $2$ edges), and colour $6$ for all other (necessarily internal) edges (including the one incident with the root). See Fig.~\\ref{fig:example} for an illustration of the scheme. \n\n\nConsider an urn with colour configuration at time $n$ as\n$U_n = (U_{n,1},\\dots, U_{n,6})$, where $U_{n,i}$ denotes the number of edges of colour $i$ in the tree at time $n$, which has precisely $n+2$ leaves. Then $U_0 = (0,2,0,0,1,0)$, since at the initial time step $(n=0)$ there is one internal edge and one essential cherry in a rooted tree; see $T_2$ in Fig.~\\ref{fig:example}. Based on the colouring scheme of the edges, at any time $n\\geq 0$, we have\n\\begin{equation}\\label{urn-pf-ch}\n(A_{n+2}, C_{n+2}) = \\frac{1}{2} \\left(U_{n,1}, U_{n,1}+U_{n,2}\\right),\n\\end{equation}\nwhere $A_{n+2}$ and $C_{n+2}$ are the numbers of pitchforks and cherries in $T_{n+2}$, respectively.\nUnder the alpha tree model, the dynamics of the corresponding urn process evolves according to the following replacement matrix\n\\[ R = \\begin{bmatrix}\n0&0&0&1&0&1\\\\\n2&-2&1&0&-1&2\\\\\n-2&4&-1&0&2&-1\\\\\n0&2&0&-1&1&0\\\\\n2&-2&1&0&-1&2\\\\\n0&0&0&1&0&1\n\\end{bmatrix}. \\]\nLet $e_i$, $1\\le i \\le 6$, denote a $6$-vector in which the $i$-th component is $1$ and $0$ elsewhere; and $\\chi_n$ the random vector taking value $e_i$ if, at time $n$, speciation happens at an edge with type $i$.\nThus, we have the following recursion\n\\[U_n= U_{n-1} +\\chi_n R, \\qquad n\\geq 1, \\]\nwhere\n\\begin{equation}\\label{SelecProb}\nP(\\chi_n =e_i|\\mbox{${\\mathcal F}$}_{n-1}) \\propto \\begin{cases}\n(1-\\alpha) U_{n-1,i}, &\\text{ for } i \\in\\{1,2,3,4\\}, \\\\[1ex]\n\\alpha \\,U_{n-1,i}, & \\text{ for } i\\in \\{5,6\\}.\n\\end{cases}\n\\end{equation}\nObserve that the process $(U_n)_{n \\ge 0}$, which describes the dynamics of the numbers of cherries and pitchfork,\nis a {\\em nonuniform urn model} since the balls are not selected uniformly at random from the urn, which is different from the classical {\\em uniform} urn models in which the balls are selected uniformly at random from the urn (see, e.g.~\\cite[Chapter 7]{hofri2019algorithmics}).\n\n\\subsection{Limiting theorems on uniform urn models}\n\\label{limit:uniform:urn}\nIn this subsection, we recall the strong laws of large numbers and the central limit theorems on a version of uniform urn models developed in~\\cite{Paper1}, which will be related to the nonuniform urn process in Subsection~\\ref{subsect:urn:tree} later using the urn coupling idea in \\cite{Kaur2018}. \n\n\nFor the classical uniform urn models, it has been shown (see \\cite{BaiHu2005}) that the random process $U_n\/n$ converges almost surely to the left eigenvector of $R$ corresponding to the maximal eigenvalue and the asymptotic normality holds with a known limiting variance matrix under certain assumptions on $R$. Standard assumptions made in the urn model theory are that the replacement matrix is irreducible with a constant row sum and all the off-diagonal elements are non-negative~(see, e.g.~\\cite{Hosam2009}). In \\cite{Paper1}, we extend this to the case when off-diagonal elements of a replacement matrix can be negative satisfying the following set of assumptions {\\bf (A1)--(A4)}, which was slightly rephrased from~\\cite{Paper1}. Let $\\text{diag}(a_1, \\dots , a_d)$ denote the diagonal matrix whose diagonal elements are $a_1,\\dots , a_d$. \n\n\\noindent\n{\\bf (A1):} {\\em Tenable:} It is always possible to draw balls and follow the replacement rule.\n\\\\\n{\\bf (A2):} {\\em Small:} \nAll eigenvalues of $R$ are real; the maximal eigenvalue $\\lambda_1$, called the {\\em principal eigenvalue} is positive with $\\lambda_1>2\\lambda$ holds for all other eigenvalues $\\lambda$ of $R$. \\\\\n{\\bf (A3):} {\\em Strictly balanced:} \tThe column vector $\\mathbf{u}_1=(1,1,\\dots,1)^\\top$, is a right eigenvector \nof $R$ corresponding to $\\lambda_1$; and it has a principal left eigenvector $\\bf{v}_1$ (i.e., the \nleft eigenvectors corresponding to $\\lambda_1$) that is also a probability vector.\n\\\\\n{\\bf (A4):} {\\em Diagonalisable:} There exists an invertible matrix $V$ with real entries whose first row is $\\bf{v}_1$ such that the first column of $V^{-1}$ is $\\mathbf{u}_1$ and \n\\begin{equation}\n\\label{eq:R:diagonal}\nVRV^{-1} = \\text{diag}(\\lambda_1, \\lambda_2,\\dots, \\lambda_d) =: \\Lambda,\n\\end{equation}\nwhere $\\lambda_1> \\lambda_2 \\ge \\dots \\ge \\lambda_d$ are eigenvalues of $R$. \n\n\n\\medskip\n\nLet $\\mathcal{N}(\\mathbf{0}, \\Sigma)$ be the multivariate normal distribution with mean vector $\\mathbf{0}=(0,\\dots,0)$ and covariance matrix $\\Sigma$. Then we have the following result from~\\cite[Theorems 1 \\& 2]{Paper1}, which can also be alternatively derived from~\\cite[Theorems 3.21 \\& 3.22 and Remark 4.2]{Janson2004}. \n\n\\begin{theorem}\n\t\\label{thm1&2:paper1}\n\tUnder assumptions {\\em \\bf{(A1)--(A4)}}, we have\n\t\\begin{equation}\n\t\\label{eq:asconv:urn}\n\t(n\\pev)^{-1} U_n \\ \\stackrel{\\mbox{a.s.}}{\\longrightarrow} \\ \\mathbf{v}_1\n\t~\\quad~\\mbox{and}~\\quad~ \n\tn^{-1\/2} (U_n - n\\pev \\mathbf{v}_1) \\xrightarrow{~d~} \\mbox{${\\mathcal N}$}(\\mathbf{0}, \\Sigma),\n\t\\end{equation}\n\twhere $\\pev$ is the principal eigenvalue and $\\mathbf{v}_1$ is the principal left eigenvector of $R$, and \n\t\\begin{equation}\n\t\\Sigma = \\sum_{i,j=2}^d \\frac{\\pev \\lambda_i \\lambda _j {\\mathbf u}_i^\\top \\mbox{\\em diag}(\\mathbf{v}_1) {\\mathbf u}_j }{\\pev-\\lambda_i -\\lambda_j} \\mathbf{v}_i^\\top \\mathbf{v}_j,\n\t\\end{equation}\n\twhere ${\\mathbf v}_j$ is the $j$-th row of $V$ and ${\\mathbf u}_j$ the $j$-th column of $V^{-1}$ for $2\\le j \\le d$.\n\\end{theorem}\n\n\n\n\\section{Limit Theorems for the Joint Distribution}\n\\label{sec:limiting}\n\nIn this section, we present the strong laws of large numbers and the central limit theorems on the joint distribution of the number of cherries and that of pitchforks under the Ford model.\n\n\n\\subsection{Main convergence results}\n\n\nFor later use, we consider the following polynomials in $\\alpha$: \n\\begin{equation}\n\\label{eq:main:cov:poly}\n\\begin{matrix*}[l]\n\\gp_1 = 8\\alpha^3-32\\alpha^2+45\\alpha-23, & \\quad\\quad\\quad \\gp_4 = 8\\alpha^3-40\\alpha^2+37\\alpha+13, \\\\\n\\gp_2 = 40\\alpha^3-164\\alpha^2+221\\alpha-97, & \\quad\\quad\\quad \\gp_5 = 40\\alpha^3-112\\alpha^2-31\\alpha+181, \\\\\n\\gp_3 = 56\\alpha^3-248\\alpha^2+367\\alpha-181, & \\quad\\quad\\quad \\gp_6 = 8\\alpha^3+4\\alpha^2-71\\alpha+71; \n\\end{matrix*}\n\\end{equation}\nand for simplicity of notation, we do not indicate the $\\phi_i$'s as functions of $\\alpha$. Moreover, it can be verified directly that $\\phi_1, \\phi_2, \\phi_3 <0$ and $\\phi_4, \\phi_5, \\phi_6 > 0$ for $\\alpha \\in (0, 1).$\nThen, we have the following result on the joint asymptotic properties of the urn model process associated with the $\\alpha$-tree model.\n\n\n\\begin{theorem}\n\t\\label{thm:urn:edge}\n\tSuppose $(U_n)_{n\\geq 0}$ is the urn process associated with the Ford model with parameter $\\alpha\\in(0,1)$. \n\tThen, \n\t\\begin{equation}\n\t\\label{eq:conv:urn}\n\t\\frac{U_n}{n} \\ \\stackrel{\\mbox{a.s.}}{\\longrightarrow} \\ \n\t\\mathbf{v}\t\n\t~\\quad~\\mbox{and}~\\quad~\n\t\\frac{ U_n- n\\mathbf{v} }{\\sqrt{n}} \\xrightarrow{~d~} \\mathcal{N}\\left (\\mathbf{0},\\Sigma \\right),\n\t\\end{equation}\n\tas $n \\to \\infty$, where \n\t\\begin{equation}\\label{main:Leftev1}\n\t{\\mathbf v}= \\frac{1}{2(3-2\\alpha)} \\left(2(1-\\alpha), \\,2(1-\\alpha),\\, (1-\\alpha),\\, 1+\\alpha,\\, 1-\\alpha, \\,5-3\\alpha \\right)\n\t\\end{equation}\nand with the polynomials $\\gp_1,\\dots,\\gp_6$ defined in~\\eqref{eq:main:cov:poly},\n\\begin{equation}\n\\Sigma=\\frac{1-\\alpha}{4(3-2\\alpha)^2(5-4\\alpha)(7-4\\alpha)} \n\\begin{bmatrix*}[r]\n-12\\gp_1 & 4\\gp_2 & -6\\gp_1 & -2\\gp_4 & 2 \\gp_2 & -2 \\gp_2 \\\\\n4\\gp_2 & -4\\gp_3 & 2\\gp_2 & -2\\gp_6 & -2 \\gp_3 & 2 \\gp_3 \\\\\n-6\\gp_1 & 2\\gp_2 & -3\\gp_1 & -\\gp_4 & \\gp_2 & - \\gp_2 \\\\\n-2\\gp_4 & -2\\gp_6 & -\\gp_4 & \\gp_5 & - \\gp_6 & \\gp_6 \\\\\n2 \\gp_2 & -2 \\gp_3 & \\gp_2& - \\gp_6 & - \\gp_3 & \\gp_3 \\\\\n-2 \\gp_2 & 2 \\gp_3 & - \\gp_2 & \\gp_6 & \\gp_3 & -\\gp_3 \n\\end{bmatrix*}.\n\\end{equation}\n\\end{theorem}\nThe proof of Theorem~\\ref{thm:urn:edge} is given at the end of this section.\n\n\n\\begin{remark} \n\\label{rem3.1} For later use, here we present the limiting results on the urn model using a scaling factor relating to the time $n$ (which is motivated by noting that the number of leaves in the tree at time $n$ is $n+2$). However, the results can be readily rephrased using the proportion of color balls in the urn process.\n\n\\end{remark}\n\n\\begin{remark} \n\tUsing the approach outlined in~\\cite{Paper1}, Theorem \\ref{thm:urn:edge} \tcontinues to hold for the unrooted $\\alpha$-tree models. \n\\end{remark}\n\nWith Theorem~\\ref{thm:urn:edge}, we are ready to present one of our main results in this paper \nconcerning limit theorems on the joint distribution of the number of cherries $C_n$ and the number of pitchforks $A_n$\nunder the Ford model. \n\n\n\\begin{theorem} \\label{Thm:Convg-ChPh}\nUnder the Ford model with parameter $\\alpha \\in [0,1]$, we have\n\t\\[\\frac{1}{n} (A_n,C_n) \\ \\stackrel{\\mbox{a.s.}}{\\longrightarrow} \\ (\\nu, \\mu) := \\frac{1-\\alpha}{2(3-2\\alpha)} (1,2),\\]\n\tand\n\t\\[ \\frac{(A_n, C_n) -n (\\nu, \\mu) }{\\sqrt{n}} \\xrightarrow{~d~} \\mathcal{N}\\big((0,0), S \\big), \\]\n\twhere \\begin{equation}\n\tS= \\begin{bmatrix} \\tau^2 & \\rho \\\\ \\rho & \\si^2 \\\\ \\end{bmatrix} =\n\t\\frac{1-\\alpha}{(3- 2\\alpha)^2 (5-4\\alpha) } \\begin{bmatrix}\n\t\\frac{-24 \\alpha^3 +96\\alpha^2 -135\\alpha +69 }{4(7-4\\alpha)} & \\frac{-(2-\\alpha) (1-2\\alpha)}{2} \\\\[1ex]\n\t\\frac{-(2-\\alpha) (1-2\\alpha)}{2} & 2-\\alpha\n\t\\end{bmatrix}.\n\t\\end{equation}\n\\end{theorem}\n\\medskip\n \\begin{remark} \nWe consider special cases of $\\alpha$-tree model, which are commonly studied in phylogenetics. The first two have been established in ~\\cite{Paper1}.\n\n\\begin{enumerate}\n\t\\item The uniform model corresponds to $\\alpha=1\/2$, where all edges, internal or leaf, are selected with equal weight and the limit results hold with\n\t\\[(\\nu, \\mu) = \\frac{1}{8}(1, 2)\n\t\\quad \\text{and } \\quad \\begin{bmatrix} \\tau^2 & \\rho \\\\ \\rho & \\si^2 \\\\ \\end{bmatrix} =\n\t\\frac{1}{64}\\begin{bmatrix} 3&0\\\\0&4\\end{bmatrix}. \\]\n\t\\item The Yule model corresponds to $\\alpha =0$, where only leaf edges are selected with equal weight and the limit results hold with\n\t\\[ (\\nu, \\mu) = \\frac{1}{6} (1,2)\n\t\\quad \\text{and } \\quad \\begin{bmatrix}\n\t\\tau^2 & \\rho \\\\\n\t\\rho & \\si^2 \\\\\n\t\\end{bmatrix} = \\frac{1}{45} \\begin{bmatrix} 69\/28 &-1 \\\\ -1 & 2 \\end{bmatrix}. \\]\n\t\n\\item The Comb model corresponds to $\\alpha =1$, a degenerate case. It is easy to see that $(\\nu, \\mu) = (0,0) $ and $\\tau^2= \\rho = \\si^2 = 0$.\n\t\n\\end{enumerate}\n \\end{remark}\n\n\n\\begin{proof}[Proof of Theorem~\\ref{Thm:Convg-ChPh}]\n\tFirst note that the case $\\alpha=1$ reduces to a degenerate case of Comb model and therefore we only consider $\\alpha\\in[0,1)$. The limiting results for the case $\\alpha=0$ has been obtained in \\cite{Paper1}, which agree with the above results when $\\alpha=0$. Thus, it is enough to prove the result for $\\alpha\\in (0,1)$.\n\t\n\t By~\\eqref{urn-pf-ch}, we have \t \n\t$ (A_n, C_n) = U_n Q $\n\twith\n\t\\begin{equation}\\label{Def-T}\n\tQ^\\top = \\frac{1}{2}\\begin{bmatrix} 1 &0&0&0&0&0\\\\\n\t1&1&0&0&0&0\n\t\\end{bmatrix}.\n\t\\end{equation}\t\t\nSince\n\t\\begin{equation}\n\t\\frac{U_n }{n} \\ \\stackrel{\\mbox{a.s.}}{\\longrightarrow} \\ {\\mathbf v} = \\frac{1}{2(3-2\\alpha)} \\big(2(1-\\alpha), 2(1-\\alpha), 1-\\alpha, 1+\\alpha, 1-\\alpha, 5-3\\alpha \\big),\n\t\\end{equation}\n\tusing the relation from equation \\eqref{urn-pf-ch} we get\n\t\\[\\frac{1}{n} (A_n,C_n) =\\left(\\frac{U_n}{n} \\right) Q \\ \\stackrel{\\mbox{a.s.}}{\\longrightarrow} \\ {\\mathbf v}\\,Q = \\frac{1-\\alpha}{2(3-2\\alpha)} (1,2). \\]\n\tThis concludes the proof of the almost sure convergence. We now prove the central limit theorem and obtain the expression for the limiting variance matrix.\n\t\n\t\n\tDenoting covariance matrix $\\Sigma$ by $(\\sigma_{i,j})$ for $1\\le i,j\\le 6$, we consider the matrix \n\t\\begin{align*}\n\tS&= Q^\\top \\Sigma Q\n\t=\\frac{1}{4} \\begin{bmatrix}\n\t\\sigma_{1,1} & \\sigma_{1,1}+\\sigma_{1,2}\\\\\n\t\\sigma_{1,1}+\\sigma_{2,1}& \\sigma_{1,1}+\\sigma_{2,1}+\\sigma_{1,2}+\\sigma_{2,2}\n\t\\end{bmatrix} \\\\[1ex]\n\t&=\\frac{1-\\alpha}{16(3-2\\alpha)^2(5-4\\alpha)(7-4\\alpha)} \n\t\\begin{bmatrix}\n\t-12\\gp_1 & -12\\gp_1+4\\gp_2\\\\\n\t-12\\gp_1+4\\gp_2 & -12\\gp_1+8\\gp_2-4\\gp_3\n\t\\end{bmatrix} \\\\[1ex]\n\t\t&=\\frac{1-\\alpha}{(3- 2\\alpha)^2 (5-4\\alpha) } \\begin{bmatrix}\n\t\\frac{-24 \\alpha^3 +96\\alpha^2 -135\\alpha +69 }{4(7-4\\alpha)} & \\frac{-(2-\\alpha) (1-2\\alpha)}{2} \\\\[2ex]\n\t\\frac{-(2-\\alpha) (1-2\\alpha)}{2} & 2-\\alpha\n\t\\end{bmatrix}.\n\t\\end{align*}\nSince $(A_n, C_n) = U_n Q$, where $Q$ is as defined in \\eqref{Def-T}, we get\n\t\\[ \\frac{(A_n, C_n) -n (\\nu, \\mu) }{\\sqrt{n}} = \\frac{1}{\\sqrt{n}} \\left(U_n -n {\\mathbf v} \\right)Q \\xrightarrow{~d~} \\mathcal{N}\\left(\\mathbf{0}, Q^\\top \\Sigma Q \\right)\n\t=\\mathcal{N}\\left(\\mathbf{0}, S \\right)\n\t.\\]\n This completes the proof.\n\\end{proof}\n\n\nWe end this subsection with the following results on the behaviour of the first and second moments of the limiting joint distribution of cherries and pitchforks in the parameter region, as indicated by their plots in Figure~\\ref{fig:limit:cov}.\n\n\\begin{corollary}\n\t\\begin{enumerate}[\\normalfont(i)]\n\t\t\\item For $0< \\alpha <1$, $A_n\/C_n \\ \\stackrel{\\mbox{a.s.}}{\\longrightarrow} \\ 1\/2$ as $n\\to \\infty$. That is, the number of pitchforks is asymptotically equal to the number of essential cherries.\n\t\t\n\t\t\\item $ A_n\/n \\ \\stackrel{\\mbox{a.s.}}{\\longrightarrow} \\ \\frac{1-\\alpha}{2(3-2\\alpha)}, $ which decreases strictly from $1\/6$ to $0$, as $\\alpha$ increases from $0$ to $1$.\n\t\t\n\t\t\n\t\t\\item The limiting variance of $A_n\/\\sqrt{n}$, $\\tau^2$, decreases strictly from $23\/420$ to $0$, as $\\alpha$ increases from $0$ to $1$.\n\t\t\\item The limiting variance of $C_n\/\\sqrt{n} $, $\\si^2$, increases strictly from $2\/45$\n\t\tto $0.0695$ over $(0, a_0)$ and decreases from $0.0695$ to $0$ over $(a_0, 1)$, where $a_0 =0.7339$,\n\t\t the unique root of $19-48\\alpha+36\\alpha^2-8\\alpha^3 =0$ in $(0,1)$.\n\t\t\n\t\t\\item The limiting covariance of $A_n\/\\sqrt{n}$ and $C_n\/\\sqrt{n}$ changes sign from negative to positive at $\\alpha =1\/2$. Specifically, it increases from $-1\/45$ \n\t\tto $0.0225$ \n\t\tover $(0, a_1)$ and decreases from $0.0225$\n\t\tover $(a_1, 1)$, where $a_1=0.8688$,\n\t\tthe unique root of $-24\\alpha^4+160\\alpha^3 -370\\alpha^2 +358\\alpha -123=0$ in $(0,1)$.\n\t\\end{enumerate}\n\\end{corollary}\n\n\\begin{figure}[H] \n\t\\label{fig:limit:cov}\n\t\\centering\n\t\\includegraphics[width=0.8\\linewidth,height=0.38\\textheight]{Rplot_New.png}\n\t\\caption{Plot of the limiting covariances of the joint distribution of cherries and pitchforks with respect to the parameter $\\alpha$ under the Ford model.}\n\\end{figure}\n\n\\subsection{A uniform urn model derived from $U_n$}\n\nFor $\\alpha\\in (0,1)$, consider the diagonal $6\\times 6$ matrix\n$\nT_\\alpha=\\text{diag}(1-\\alpha,1-\\alpha,1-\\alpha,1-\\alpha,\\alpha,\\alpha)\n$ \nand \\[ \\widetilde{U}_n := U_nT_\\alpha = \\left((1-\\alpha) U_{n,1}, \\dots, (1-\\alpha) U_{n,4}, \\alpha U_{n,5}, \\alpha U_{n,6}\\right). \\]\nClearly, there is a one to one correspondence between $U_n$ and $\\widetilde{U}_n= U_nT_\\alpha$ for $\\alpha\\in (0,1)$ and therefore it is sufficient to obtain the limiting results for the urn process $\\widetilde{U}_n$. Note that the off-diagonal elements of the replacement matrix $R_\\alpha$ are not all non-negative, therefore we will use the limit results from \\cite{Paper1} to obtain the convergence results for the urn process $\\widetilde{U}_n$.\n\n\n\\begin{theorem}\\label{Thm:Urn}\nSuppose $\\alpha\\in(0,1)$. Then \t$(\\widetilde{U}_n)_{n\\geq 0}$ is an uniform urn process with replacement matrix $ R_\\alpha = RT_\\alpha$ and\n\\begin{equation}\n\\label{eq:cas:nonuniform}\n\\frac{\\widetilde{U}_n }{n} \\ \\stackrel{\\mbox{a.s.}}{\\longrightarrow} \\ \\widetilde{{\\mathbf v}}_1,\n\\end{equation}\nwhere \\begin{equation}\\label{Leftev1}\n\\widetilde{{\\mathbf v}}_1= \\frac{1}{2(3-2\\alpha)} \\big(2(1-\\alpha)^2, 2(1-\\alpha)^2, (1-\\alpha)^2, 1-\\alpha^2, \\alpha(1-\\alpha), \\alpha(5-3\\alpha) \\big)\n\\end{equation}\nis the normalized left eigenvector of $R_\\alpha$ corresponding to the largest eigenvalue $\\lambda_1=1$. Furthermore,\n\\begin{equation}\n\\label{eq:cweak:nonuniform}\n\\frac{\\widetilde{U}_n -n \\widetilde{{\\mathbf v}}_1}{\\sqrt{n}} \\xrightarrow{~d~} \\mathcal{N}(\\mathbf{0}, \\widetilde{\\Sigma}),\n\\end{equation}\nwith the polynomials $\\gp_1,\\dots,\\gp_6$ defined in~\\eqref{eq:main:cov:poly} and $\\beta=1-\\alpha$,\n\\begin{equation}\n\\label{eq:covariance:nonuniform}\n\\widetilde{\\Sigma}=\\frac{\\beta}{4(3-2\\alpha)^2(5-4\\alpha)(7-4\\alpha)} \n\\begin{bmatrix*}[r]\n-12\\beta^2\\gp_1 & 4\\beta^2\\gp_2 & -6\\beta^2\\gp_1 & -2\\beta^2\\gp_4 & 2\\alpha\\beta \\gp_2 & -2\\alpha\\beta \\gp_2 \\\\\n4\\beta^2\\gp_2 & -4\\beta^2\\gp_3 & 2\\beta^2\\gp_2 & -2\\beta^2\\gp_6 & -2\\alpha\\beta \\gp_3 & 2\\alpha\\beta \\gp_3 \\\\\n-6\\beta^2\\gp_1 & 2\\beta^2\\gp_2 & -3\\beta^2\\gp_1 & -\\beta^2\\gp_4 & \\alpha\\beta \\gp_2 & -\\alpha\\beta \\gp_2 \\\\\n-2\\beta^2\\gp_4 & -2\\beta^2\\gp_6 & -\\beta^2\\gp_4 & \\beta^2\\gp_5 & -\\alpha\\beta \\gp_6 & \\alpha\\beta \\gp_6 \\\\\n2\\alpha\\beta \\gp_2 & -2\\alpha\\beta \\gp_3 & \\alpha\\beta \\gp_2& -\\alpha\\beta \\gp_6 & -\\alpha^2 \\gp_3 & \\alpha^2 \\gp_3 \\\\\n-2\\alpha\\beta \\gp_2 & 2\\alpha\\beta \\gp_3 & -\\alpha\\beta \\gp_2 & \\alpha\\beta \\gp_6 & \\alpha^2 \\gp_3 & -\\alpha^2 \\gp_3 \n\\end{bmatrix*}.\n\\end{equation}\n\n\\end{theorem}\n\\begin{proof}[Proof of Theorem \\ref{Thm:Urn}]\n\tFirst, observe that at any time $n$, there are $n+2$ pendant edges and $n+1$ internal edges in a rooted tree. That is, \n\\[ U_{n,1}+ U_{n,2}+ U_{n,3}+ U_{n,4}= n+2 \\quad \\text{and } \\quad U_{n,5}+ U_{n,6} = n+1.\\]\nThis gives\n\\begin{align*}\n\\|\\widetilde{U}_n\\|_1\n= (1-\\alpha) \\sum_{j=1}^4 U_{n,j}+ \\alpha \\sum_{j=5}^6 U_{n,j} \n= (1-\\alpha) (n+2) + \\alpha (n+1) = n+2 -\\alpha.\n\\end{align*}\nTherefore, from \\eqref{SelecProb} we get,\n\\[ \\mbox{${\\mathbb E}$}[\\chi_{n}| \\mbox{${\\mathcal F}$}_{n-1}] = \\dfrac{U_{n-1} T_{\\alpha}}{\\|U_{n-1}T_{\\alpha}\\|_1}= \\dfrac{U_{n-1} T_{\\alpha}}{ n+1 -\\alpha},\n\\]\nand\n\\begin{align*}\n\\mbox{${\\mathbb E}$}[U_{n}|\\mbox{${\\mathcal F}$}_{n-1}]\n= U_{n-1} + \\mbox{${\\mathbb E}$}[\\chi_{n}|\\mbox{${\\mathcal F}$}_{n-1}] R\n= U_{n-1} + \\dfrac{1}{n+1 -\\alpha} U_{n-1} T_\\alpha R.\n\\end{align*}\nMultiplying both sides by $T_\\alpha$, we get\n\n\\[\\mbox{${\\mathbb E}$}[\\widetilde{U}_{n}|\\mbox{${\\mathcal F}$}_{n-1}] = \\widetilde{U}_{n-1} + \\left(\\dfrac{1}{\\|\\widetilde{U}_{n-1} \\|_1} \\widetilde{U}_{n-1} \\right) R T_\\alpha. \\]\nHence, $(\\widetilde{U}_n)_{n\\geq 0}$ is a classical uniform urn model with replacement matrix $ R_\\alpha = RT_\\alpha$.\n\n\nNote that {\\bf (A1)} holds because the general Ford's dynamics on a rooted tree is well defined at every time $n$, thus the corresponding urn model satisfies the assumption of tenability. That is, it is always possible to draw balls without getting stuck with the replacement rule.\nNote that $R_\\alpha$ is diagonalisable as \n\\[V R_\\alpha V^{-1}=\\Lambda \\]\nholds with $ \\Lambda = \\text{diag} \\big(1,0,0,0, -2(1-\\alpha),-(3-2\\alpha)\\big)$,\n\\begin{equation} \\label{Reigen:AalphaR}\nV^{-1}= \\begin{bmatrix}\n1& \\frac{1}{\\beta}&0&0&1& 1-\\alpha\\\\[1ex]\n1& 0&\\frac{1}{\\beta}&0&1& 3-\\alpha \\\\[1ex]\n1& \\frac{-2}{\\beta}&0& \\frac{3}{\\beta}&\\frac{-(2-\\alpha)}{\\beta}&-5+\\alpha \\\\[1ex]\n1 &0&0&\\frac{1}{\\beta}&\\frac{-(2-\\alpha)}{\\beta}&-3+\\alpha \\\\[1ex]\n1 & 0&\\frac{-2}{\\alpha}&\\frac{1}{\\alpha}&1&3-\\alpha \\\\[1ex]\n1 & 0&0&\\frac{-1}{\\alpha} & 1& 1-\\alpha\n\\end{bmatrix}\n\\end{equation}\n\nand \n\t\\setlength{\\arraycolsep}{2.5pt}\n\\medmuskip = 1mu\n\\begin{align}\nV =\\frac{1}{2(3-2\\alpha) }\n\\begin{bmatrix}\\label{Leigen:AalphaR:1}\n2\\beta^2 & 2\\beta^2 & \\beta^2 & (1+\\alpha)\\beta & \\alpha\\beta& \\alpha(5-3\\alpha)\\\\[1ex]\n2\\beta(1+\\alpha-\\alpha^2) & 2\\beta^3 &-(2-\\alpha)\\beta^2 & (2-\\alpha)\\beta^2 & -\\alpha\\beta^2& -\\alpha\\beta(5-3\\alpha) \\\\[1ex]\n2\\alpha\\beta^2 & 2\\alpha(2-\\alpha)\\beta & \\alpha\\beta^2 &-\\alpha\\beta^2 & -\\alpha(3- \\alpha)\\beta & -3\\alpha\\beta^2 \\\\[1ex]\n2\\alpha(2-\\alpha)\\beta & 2\\alpha\\beta^2 & \\alpha(2-\\alpha)\\beta &-\\alpha(2-\\alpha)\\beta & \\alpha^2 \\beta & -3\\alpha(2-\\alpha)\\beta \\\\[1ex]\n2(2-\\alpha)\\beta & -2\\beta^2 & (2-\\alpha)\\beta & -(4-\\alpha)\\beta & -\\alpha\\beta &\\alpha\\beta\\\\[1ex]\n-2\\beta & 2\\beta & -\\beta & \\beta & \\alpha & -\\alpha\n\\end{bmatrix}.\n\\end{align}\nTherefore, $R$ satisfies condition {\\bf (A4)}. Next, {\\bf (A2)} holds because $R_\\alpha$ has eigenvalues \n$$1,\\quad 0,\\quad 0, \\quad 0, \\quad -2(1-\\alpha),\\quad -(3-2\\alpha)$$ which are all real. The maximal eigenvalue $\\lambda_1=1$ is positive with $\\lambda_1>2\\lambda$ holds for all other eigenvalues $\\lambda$ of $R_\\alpha$.\nFurthermore, put $\\mathbf{u}_i={V^{-1}}\\mathbf{e}^\\top_i$ and $\\mathbf{v}_i=\\mathbf{e}_iV$ for $1\\le i \\le 4$. Then {\\bf (A3)} follows by noting that $\\mathbf{u}_1=(1,1,1,1,1,1)^\\top$ is the principal right eigenvector, and\n\\[\\widetilde{{\\mathbf v}}_1= \\frac{1}{2(3-2\\alpha)} \\big(2(1-\\alpha)^2, 2(1-\\alpha)^2, (1-\\alpha)^2, 1-\\alpha^2, \\alpha(1-\\alpha), \\alpha(5-3\\alpha) \\big)\\] is the principal left eigenvector. \n\n\nSince all the assumptions {\\bf (A1)--(A4)} are satisfied by the replacement matrix $R_\\alpha$, by Theorem~\\ref{thm1&2:paper1},\n\\eqref{eq:cas:nonuniform} holds. Furthermore, since\n\\begin{equation} \n\\widetilde{\\Sigma} = \\sum_{i,j=2}^6 \\frac{ \\lambda_i \\lambda _j {\\mathbf u}_i^\\top \\mbox{diag}(\\mathbf{v}_1) {\\mathbf u}_j }{1-\\lambda_i -\\lambda_j} \\mathbf{v}_i^\\top \\mathbf{v}_j,\n\\end{equation}\nby~\\eqref{eq:cas:nonuniform} it follows that~\\eqref{eq:cweak:nonuniform} holds.\n\\end{proof}\n\n\n\n\\subsection{Proof of Theorem \\ref{thm:urn:edge}}\n\n\n\n\n\\begin{proof}\n\t\\noindent\n\tObserve that $\\sum_{i=1}^6U_{n,i} = 3+2n$ (since $2$ balls are added into the urn at every time point), thus the vector of color proportions is $U_n \/(3+2n)$.\n\n\tSince $\\alpha \\in (0,1)$, it follows that $T_\\alpha$ is invertible and its inverse is \n\t$$\n\tT_\\alpha^{-1}=\\frac{1}{\\alpha(1-\\alpha)}\\mbox{diag}(\\alpha,\\alpha,\\alpha,\\alpha,1-\\alpha,1-\\alpha),\n\t$$\n\twhich is also a diagonal matrix, and so $(T_\\alpha^{-1})^\\top=T_\\alpha^{-1}$.\n\tNote that we have $U_n = \\widetilde{U}_n T_\\alpha^{-1}$ and consider \n\t$$\n\t{{\\mathbf v}} = \\widetilde{{\\mathbf v}}_1 (T_\\alpha)^{-1} = \\frac{1}{2(3-2\\alpha)} \\big(2(1-\\alpha), 2(1-\\alpha), 1-\\alpha, 1+\\alpha, 1-\\alpha, 5-3\\alpha \\big).\n\t$$\n\tSince $ \\dfrac{\\widetilde{U}_n }{n} \\ \\stackrel{\\mbox{a.s.}}{\\longrightarrow} \\ \\widetilde{{\\mathbf v}}_1$ holds in view of~\\eqref{eq:cas:nonuniform} in Theorem \\ref{Thm:Urn}, \n\n\t\\begin{equation}\n\t\\frac{U_n }{n} \\ \\stackrel{\\mbox{a.s.}}{\\longrightarrow} \\ {{\\mathbf v}},\n\t\\end{equation}\n\twhich concludes the proof of the almost sure convergence in~\\eqref{eq:conv:urn}. \n\t\n\tConsider the covariance matrix $\\widetilde{\\Sigma}$ for $ \\widetilde{U}_n$ as stated in~\\eqref{eq:covariance:nonuniform}, then by straightforward calculation we have \n\t$$\n\t\\Sigma=(T_\\alpha^{-1})^\\top \\widetilde{\\Sigma} T_\\alpha^{-1}=T_\\alpha^{-1} \\widetilde{\\Sigma} T_\\alpha^{-1}. \n\t$$\n\tTherefore, since\n\t\\[\\frac{\\widetilde{U}_n -n \\widetilde{{\\mathbf v}}_1}{\\sqrt{n}} \\xrightarrow{~d~} \\mathcal{N} (\\mathbf{0}, \\widetilde{\\Sigma} ) \\]\n\tin view of Theorem \\ref{Thm:Urn}, we get\n\t\\[\\frac{U_n -n {\\mathbf v} }{\\sqrt{n}} \\xrightarrow{~d~} \\mathcal{N} \\big(\\mathbf{0}, (T_\\alpha^{-1})^\\top \\widetilde{\\Sigma}\\, T_\\alpha^{-1}\\big)=\\mathcal{N} (\\mathbf{0}, \\Sigma). \\]\n\tThis completes the proof.\n\\end{proof}\n\n\n\n\\section{Exact Distributions }\n\\label{sec:exact}\n\nIn this section, we present recursion formulas for exact computation of the joint distributions of cherries and pitchforks, their means, variances and covariance for fixed $n$ under the Ford model.\n\n\n\nWe begin with the following notation. Given a phylogenetic tree $T$, let $E_{1}(T)$ be the set of pendant edges that are contained in a pitchfork but not a cherry; $E_{2}(T)$ the set of edges in $T$\nthat are contained in a cherry but not in a pitchfork (note that in our notation a cherry contains three leaves);\n$E_{3}(T)$ the set of pendant edges that are contained in neither a cherry nor a pitchfork; and $E_{4}(T)=E(T)\\setminus (E_{1}(T)\\cup E_{2}(T) \\cup E_{3}(T))$.\nIn addition, $E(T)$ can be decomposed into the disjoint union of these four sets of edges.\ni.e., $E(T)=E_1(T)\\sqcup E_2(T) \\sqcup E_3(T) \\sqcup E_4(T)$, where $\\sqcup$ denotes disjoint union.\nLet $C(T), A(T)$ be the number of cherries and pitchforks in a tree $T$. The following result presented in \\cite{WuChoi16} will be useful later. \n\n\\begin{lemma}\t\\label{lem:edge-set}\n\tSuppose that $T$ is a phylogenetic tree with $n$ leaves. Then we have\n\t\\begin{equation}\n\t\\label{eq:edge:dec}\n\tE(T)=E_{1}(T)\\,\\sqcup\\,E_{2}(T)\\,\\sqcup\\,E_{3}(T)\\,\\sqcup\\,E_{4}(T).\n\t\\end{equation}\n\tIn addition, we have\n\n\t$|E_{1}(T)|=A(T)$,\n\t$|E_{2}(T)|=3(C(T)-A(T))$,\n\t$|E_{3}(T)|=n-A(T)-2C(T)$,\n\tand\n\t$|E_{4}(T)|=n-1+3A(T)-C(T)$.\nFurthermore, suppose that $e$ is an edge in $T$ and $T'=T[e]$. Then\n\t\twe have\n\t\t\\small{\n\t\t\t\\begin{equation*}\n\t\t\tA(T') = \\begin{cases}\n\t\t\tA(T) & \\text{if } e\\in E_3(T)\\cup E_4(T), \\\\\n\t\t\tA(T)-1 & \\text{if } e\\in E_1(T),\\\\\n\t\t\tA(T)+1 & \\text{if } e\\in E_2(T);\n\t\t\t\\end{cases}\n\t\t\t\\mbox{and} \\quad\n\t\t\tC(T') = \\begin{cases}\n\t\t\tC(T) & \\text{if } e \\in E_2(T)\\cup E_4(T), \\\\\n\t\t\t{} & \\\\\n\t\t\tC(T)+1 & \\text{if } e \\in E_1(T)\\cup E_3(T).\n\t\t\t\\end{cases}\n\t\t\t\\end{equation*}\n\t\t}\n\n\\end{lemma}\n\nWe start with the following result on the exact computation of the joint probability mass function (pmf) of $A_n$ and $C_n$, \nwhich can be regarded as a generalization of the previous results on the Yule model (e.g. when $\\alpha=0$~\\cite[Theorem 1]{WuChoi16}) and the uniform model (e.g. $\\alpha=1\/2$~\\cite[Theorem 4]{WuChoi16}).\nA related result for unrooted trees is presented in~\\cite{CTW19}.\n\n\\begin{theorem} \\label{jointpmf}\nFor $n \\ge 3$, $0 \\le a\\le n\/3$ and $1\\le b\\le n\/2$, under the Ford model with parameter $\\alpha\\in [0,1]$ we have\n\\begin{eqnarray*}\n&& \\mathbb{P}(A_{n+1}=a, C_{n+1}=b) \\\\&=& \\frac{2a+ \\alpha(n-a-b-1)}{n-\\alpha} \\mathbb{P}(A_n=a, C_n=b)\n+ \\frac{(1-\\alpha)(a+1)}{n-\\alpha} \\mathbb{P}(A_n=a+1, C_n=b-1) \\\\\n&& \n\\quad\n+ \\frac{(2-\\alpha)(b-a+1)}{n-\\alpha} \\mathbb{P}(A_n=a-1, C_n=b) + \\frac{(1-\\alpha)(n-a-2b+2)}{n-\\alpha} \\mathbb{P}(A_n=a, C_n=b-1).\n\\end{eqnarray*}\n\\end{theorem}\n\\begin{proof}[Proof of Theorem \\ref {jointpmf}]\nFix $n> 3$, and let $T_2,\\dots,T_n,T_{n+1}$ be a sequence of random trees generated by the Ford process, that is, $T_2$ contains two leaves and $T_{i+1}=T_i[e_i]$ for a random edge $e_i$ in $T_i$ chosen according to the Ford model for $2\\leq i \\leq n$. \n\tThen we have\n\t\\begin{align}\n\t\\label{eq:total:yule}\n\t\\mathbb{P}(A_{n+1}=a, C_{n+1}=b) &=\\mathbb{P}(A(T_{n+1})=a, C(T_{n+1})=b) \\notag \\\\\t\n\t&\\hspace{-2cm}\n\t=\\sum_{p,q} \\mathbb{P}(A(T_{n+1})=a, C(T_{n+1})=b\\,|\\,A(T_n)=p,C(T_n)=q) \\mathbb{P}(A(T_n)=p,C(T_n)=q) \\notag \\\\\n\t\\hspace{-1cm}\n\t&\\hspace{-2cm}\n\t=\\sum_{p,q} \\mathbb{P} (A(T_{n+1})=a, C(T_{n+1})=b\\,|\\,A(T_n)=p,C(T_n)=q) \\mathbb{P}(A_{n}=p, C_{n}=q),\n\t\\end{align}\n\twhere the first and second equalities follow from the law of total probability, and the definition of random variables $A_n$ and $C_n$.\n\t\nLet $e_n$ be the edge in $T_n$ chosen in the above Ford process for generating $T_{n+1}$, that is, $T_{n+1}=T_n[e_n]$. Since Lemma~\\ref{lem:edge-set} implies that\n\t\\begin{equation}\n\t\\label{eq:yule:5}\n\t\\mathbb{P}(A(T_{n+1})=a, C(T_{n+1})=b~|~A(T_n)=p,C(T_n)=q)=0 \n\t\\end{equation}\n\tfor $(p,q) \\not \\in\\{(a,b),(a+1,b-1),(a-1,b),(a,b-1)\\}$,\n\tit suffices to consider the following four cases in the summation in (\\ref{eq:total:yule}): case (i): $p=a, q=b$; case (ii): $p=a+1, q=b-1$; case (iii): $p=a-1, q=b$; and case (iv): $p=a, q=b-1$.\n\t\n\t\n\tFirstly, Lemma~\\ref{lem:edge-set} implies that case (i) occurs if and only if $e_n\\in E_4(T_n)$. Using Lemma~\\ref{lem:edge-set} again, it follows that $E_4(T_n)$ contains precisely $2A(T_n)$ pendent edges and $(n-1)+A(T_n)-C(T_n)$ interior edges. Therefore we have\n\t\\begin{align}\n\t&\\mathbb{P}(A(T_{n+1})=a, C(T_{n+1})=b~|~A(T_n)=a,C(T_n)=b) \\nonumber \\\\\n\t&\\quad \\quad =\\frac{2A(T_n)(1-\\alpha)+\\alpha(n-1+A(T_n)-C(T_n))}{n-\\alpha}=\n\t\\frac{2a+ \\alpha(n-a-b-1)}{n-\\alpha}.\\label{eq:yule:1}\n\t\\end{align}\n\n\n\t\n\tSimilarly, Lemma~\\ref{lem:edge-set} implies that case (ii)\n\toccurs if and only if $e_n\\in E_1(T_n)$.\n\t Using Lemma~\\ref{lem:edge-set} again, it follows that $E_1(T_n)$ contains precisely $A(T_n)$ pendent edges and no interior edges. Therefore we have\n\t\\begin{align}\n\t\\label{eq:yule:2}\n\t\\mathbb{P}(A(T_{n+1})=a, C(T_{n+1})=b~|~A(T_n)=a+1,C(T_n)=b-1)\n\t=\\frac{(a+1)(1-\\alpha)}{n-\\alpha}.\n\t\\end{align}\n\t\n\tNext, Lemma~\\ref{lem:edge-set} implies case (iii) occurs if and only if $e_n\\in E_2(T_n)$. \t Using Lemma~\\ref{lem:edge-set} again, it follows that $E_2(T_n)$ contains precisely $2(A(T_n)-C(T_n))$ pendent edges and $A(T_n-C(T_n)$ interior edges. Thus we have\n\t\\begin{align}\n\t&\\mathbb{P}(A(T_{n+1})=a, C(T_{n+1})=b~|~A(T_n)=a-1,C(T_n)=b) \\nonumber \\\\\n\t&\t\\quad \\quad\n\t=\\frac{2(a-1-b)(1-\\alpha)+\\alpha(a-1-b)}{n-\\alpha}=\\frac{(2-\\alpha)(b-a+1)}{n-\\alpha}.\\label{eq:yule:3}\n\t\\end{align}\n\n\t\n\tFinally, Lemma~\\ref{lem:edge-set} implies case (iv) occurs\n\tif and only if $e_n$ is contained in $E_3(T_n)$. Using Lemma~\\ref{lem:edge-set} again, it follows that $E_3(T_n)$ contains precisely $n-A(T_n)-2C(T_n)$ pendent edges and no interior edges. Hence, it follows that\n\t\\begin{equation}\n\t\\label{eq:yule:4}\n\t\\mathbb{P}(A(T_{n+1})=a, C(T_{n+1}=b)~|~A(T_n)=a,C(T_n)=b-1)\n=\\frac{(1-\\alpha)(n-a-2b+2)}{n-\\alpha}. \n\t\\end{equation}\n\t\n\tSubstituting Eq.~\\eqref{eq:yule:1}--\\eqref{eq:yule:4} into Eq.~\\eqref{eq:total:yule} completes the proof of the theorem.\n\t\\end{proof}\n\n\nTo study the moments of $A_n$ and $C_n$, we present below a functional recursion form of Theorem~\\ref{jointpmf}, whose proof is straightforward and hence omitted here. \n\n\n\\begin{theorem}\n\\label{jointrr}\nLet $\\varphi: \\mathbb{N}\\times \\mathbb{N} \\to \\mathbb{R}$ be an arbitrary function. For $n \\ge 3$,\nunder the Ford model with parameter $\\alpha\\in [0,1]$ we have\n\\begin{eqnarray*}\n(n-\\alpha) \\mbox{${\\mathbb E}$} \\varphi(A_{n+1}, C_{n+1}) &=& \\mbox{${\\mathbb E}$}\\bigg[ \\big\\{\\alpha(n-A_n-C_n-1) +2A_n \\big\\} \\varphi(A_n, C_n) \\\\\n&& +(1-\\alpha)A_n \\varphi(A_n-1, C_n+1) +(2-\\alpha) (C_n-A_n) \\varphi(A_n+1, C_n) \\\\\n&& + (1-\\alpha)(n-A_n-2C_n) \\varphi(A_n, C_n+1) \\bigg ].\n\\end{eqnarray*}\n\\end{theorem}\n\nFor a fix integer $k$, consider the indicating function $I_k(x,y)$ that equals to 1 if $y=k$, and $0$ otherwise. Then by Theorem~\\ref{jointrr} the following result on the distribution of cherries follows.\n\n\\begin{corollary}\n\\label{cherrypmf}\nFor integers $n \\ge 3$ and $0\\le k \\le n\/2$, under the Ford model with parameter $\\alpha\\in [0,1]$ we have\n$$\n(n-\\alpha) \\mathbb{P}(C_{n+1}=k) = [(n-1) \\alpha +2(1-\\alpha) k ] \\mathbb{P}(C_{n}=k)\n+(1-\\alpha)(n-2k +2) \\mathbb{P}(C_{n+1}=k-1).\n$$\n\\end{corollary}\n\nFor the purpose of next section, we end this section by writing the recurrence relation in the following form in the next Corollary.\n\\begin{corollary}\n\\label{recurrence}\nFor $n \\ge 3$, under the Ford model with parameter $\\alpha\\in [0,1]$ we have\n\\begin{eqnarray}\n(n-\\alpha) \\mbox{${\\mathbb E}$} [C_{n+1}] - (n -2+\\alpha) \\mbox{${\\mathbb E}$} [C_n] &=& n(1-\\alpha), \\label{cherrymean}\\\\\n(n-\\alpha) \\mbox{${\\mathbb E}$}[A_{n+1}] - (n-3+\\alpha) \\mbox{${\\mathbb E}$}[A_n] &=& (2-\\alpha) \\mbox{${\\mathbb E}$} [C_n], \\label{forkmean} \\\\\n(n-\\alpha) \\mbox{${\\mathbb E}$} [C_{n+1}^2] - (n-4+3\\alpha) \\mbox{${\\mathbb E}$} [C_n^2] &=& 2(n-1)(1-\\alpha) \\mbox{${\\mathbb E}$} [C_n] + n(1-\\alpha), \\label{cherry2nd}\\\\\n(n-\\alpha)\\mbox{${\\mathbb E}$}[A_{n+1}C_{n+1}] - (n-5+3\\alpha) \\mbox{${\\mathbb E}$} [A_nC_n] &=& (n-1)(1-\\alpha)\\mbox{${\\mathbb E}$}[A_n] + (2-\\alpha) \\mbox{${\\mathbb E}$}[ C_n^2], \\label{cov}\\\\\n(n-\\alpha) \\mbox{${\\mathbb E}$}[A_{n+1}^2]- (n-6+3\\alpha)\\mbox{${\\mathbb E}$}[A_n^2] &=& 2(2-\\alpha)\\mbox{${\\mathbb E}$} [A_nC_n] + (2-\\alpha)\\mbox{${\\mathbb E}$} [C_n] - \\mbox{${\\mathbb E}$} [A_n] \\label{fork2nd}\n\\end{eqnarray}\nwith initial conditions $\\mbox{${\\mathbb E}$}[A_3]= \\mbox{${\\mathbb E}$}[C_3]=\\mbox{${\\mathbb E}$}[A_3^2]=\\mbox{${\\mathbb E}$}[C_3^2]=\\mbox{${\\mathbb E}$}[A_3C_3]=1. $\n\\end{corollary}\n\n\\begin{remark}\nLet $\\mu_n = \\mbox{${\\mathbb E}$} [C_n]$ and \t$\\si_{n}^2=\\mathrm{var}(C_n)$. \nSubstituting $\\mbox{${\\mathbb E}$}[C_{n}^2]=\\si_{n}^2 + \\mu_{n}^2$ into (\\ref{cherry2nd}) and applying (\\ref{cherrymean}), we obtain below a recurrence relation of the $\\si_n^2$, which was also obtained in Ford's thesis (Theorem 60, \\cite{Ford2006}):\n\\begin{eqnarray*}\n(n-\\alpha) \\si_{n+1}^2 - (n-4+3\\alpha) \\si_n^2 \n&=& -\\frac{4(1-\\alpha)^2}{n-\\alpha} \\mu_n^2 + \\frac{2(1-\\alpha)[(1-2\\alpha)n +\\alpha]}{n-\\alpha} \\mu_n\n+ \\frac{\\alpha (1-\\alpha)n(n-1)}{n-\\alpha}. \\label{cherryvar}\n\\end{eqnarray*}\n\\end{remark}\n\n\\section{Higher Order Asymptotic Expansion of the Joint Moments}\n\\label{sec:expansion}\n\nAlthough the leading terms of the first and second moments of the distributions of cherries and pitchforks, $\\mbox{${\\mathbb E}$} [A_n], \\mbox{${\\mathbb E}$} [C_n], \\mathrm{var}(A_n),\\mathrm{var}(C_n)$ and $cov(A_n, C_n)$, can be identified from Theorem \\ref{Thm:Convg-ChPh}, for better understanding of their asymptotic behaviour \n we derive their higher order expansions in this Section. \n\n We start with the following result on the first moments. Note that Proposition~\\ref{Prop:firstm} (i) has been obtained in \\cite{Ford2006}.\n\\begin{proposition\n\\label{Prop:firstm} Under the Ford model with parameter $\\alpha\\in [0,1]$, the following exact expansions hold for $\\mbox{${\\mathbb E}$} [C_n] $ and $\\mbox{${\\mathbb E}$} [A_n]$.\n\\begin{enumerate}[\\normalfont(i)]\n\\item $ \\mbox{${\\mathbb E}$} [C_n] = \\dfrac{1-\\alpha}{3-2\\alpha} \\ n + \\dfrac{\\alpha}{2(3-2\\alpha)} +x_n, $\nwhere\n\\[x_2= \\frac{(2- \\alpha)}{2(3-2\\alpha)}, \\quad x_3 = \\frac{\\alpha}{2(3-2\\alpha)}, \\quad x_n = \\frac{\\alpha }{2(3-2\\alpha)} \\prod_{i=3}^{n-1} \\frac{i-2+\\alpha}{i-\\alpha}, \\quad n \\ge 4. \\]\nFurther, as $n \\to \\infty$,\n\\begin{equation}\\label{Order:xn}\nx_n = \\frac{\\alpha\\Ga(3-\\alpha)}{2(3-2\\alpha)\\Ga(1+\\alpha)}n^{-2(1-\\alpha)} \\left(1+o(1) \\right).\n\\end{equation}\n\n\\item $\\mbox{${\\mathbb E}$} [A_n] = \\dfrac{1-\\alpha}{2(3-2\\alpha)} \\ n + \\dfrac{\\alpha}{2(3-2\\alpha)} + y_n,$\nwhere\n\\[y_2 = \\frac{\\alpha-2}{2(3-2\\alpha)}, ~ y_3=\\frac{1}{2}, ~ y_n =\\frac{1}{2} \\prod_{i=3}^{n-1}\\frac{i-3+\\alpha}{i-\\alpha } + \\frac{(2- \\alpha) \\alpha }{2(3-2\\alpha)} \\frac{n-3}{n-3+\\alpha} \\prod_{i=3}^{n-1}\\frac{i-2+\\alpha}{i-\\alpha }, ~~ n \\geq 4.\\]\nFurther, as $n \\to \\infty$,\n\\begin{equation}\\label{Order:yn}\ny_n = \\frac{(2- \\alpha) \\Gamma(3-\\alpha) }{2(3-2\\alpha)\\Gamma(\\alpha)} n^{-2(1-\\alpha)} \\left(1+o(1) \\right).\n\\end{equation}\n\\end{enumerate}\n\\end{proposition}\n\n\\begin{proposition\n\\label{Prop:secondm}\nUnder the Ford model with parameter $\\alpha\\in [0,1]$, the following asymptotic expansions hold for $\\mathrm{var} (C_n)$, $cov(A_n, C_n)$ and $\\mathrm{var} (A_n)$:\n\\begin{enumerate}[\\normalfont(i)]\n\\item $$\\mathrm{var} (C_n) = \\frac{(1-\\alpha)(2-\\alpha)}{(3-2\\alpha)^2(5-4\\alpha)} \\ n - \\frac{\\alpha (1-\\alpha)(2-\\alpha)}{(3-2\\alpha)^2(5-4\\alpha)} +\\mbox{${\\mathcal O}$}(n^{-2(1-\\alpha)}).$$\n\\item\n$$cov(A_n, C_n) = \\frac{-(1-\\alpha)(2-\\alpha)(1-2\\alpha)}{2(3-2\\alpha)^2(5-4\\alpha) } \\ n -\\frac{ \\alpha (1-\\alpha)(2-\\alpha)}{(3-2\\alpha)^2(5-4\\alpha)} +\\mbox{${\\mathcal O}$}(n^{-2(1-\\alpha)}).$$\n\\item\n$$ \\mathrm{var}(A_n) = \\frac{(1-\\alpha)(69-135\\alpha+96\\alpha^2-24\\alpha^3)}{4(3-2\\alpha)^2(5-4\\alpha)(7-4\\alpha)} \\ n + \\frac{3\\alpha(1-\\alpha)(1-2\\alpha)(5-3\\alpha)}{4(3-2\\alpha)^2(5-4\\alpha)(7-4\\alpha)} +\\mbox{${\\mathcal O}$}(n^{-2(1-\\alpha)}).$$\n\\end{enumerate}\n\\end{proposition}\n\n\n\\begin{remark}\nWhen $n$ is large, $Cov(A_n, C_n)$ changes sign. Specifically, for $ \\alpha \\in (0, 1\/2)$, $A_n$ and $C_n$ are negatively correlated, which is expected; and for $ \\alpha \\in (1\/2, 1)$, $A_n$ and $C_n$ are positively correlated, which is unexpected.\n\\end{remark}\n\n\n\n\n\\subsection{Proofs of Propositions~\\ref{Prop:firstm} and~\\ref{Prop:secondm}} \nWe need the lemmas below to prove the two propositions.\n\n\\begin{lemma}\\label{Lemma1}\nSuppose a real sequence $\\{X_n, n \\ge n_0\\}$ satisfies the recursion\n\\[ X_{n+1} = f_n X_n +g_n, \\qquad n \\geq n_0,\\]\nwhere $\\{f_n, n \\ge n_0\\}$ and $\\{g_n, n \\ge n_0\\}$ are sequences such that for every $ \\ell \\geq n_0$, $ \\left|\\prod_{i=\\ell}^n f_i \\right| \\leq C (n\/\\ell)^{-a} $ and $|g_\\ell| \\leq C \\ell^{-b}$, for some finite $a, b$ and $C>0$. Then, there exists a finite positive constant $C'$ (which depends on $|X_{n_0}|$ and $C$) such that $|X_n | \\leq C' n^{-q_{a,b}}$ where $q_{a,b} \\coloneqq \\min\\{a, b-1\\}$.\n\\end{lemma}\n\n\\begin{proof}[Proof of Lemma \\ref{Lemma1}]\nIt is easy to verify that the solution to the given recursion is given by\n\\[X_n =X_{n_0} \\prod_{i= n_0}^{n-1} f_i + \\sum_{i=n_0}^{n-1} g_i \\prod_{j=i+1}^{n-1}f_j, \\quad n \\ge n_0.\\]\nTherefore,\n\\[ |X_n| \\leq |X_{n_0}| \\left| \\prod_{i= n_0}^{n-1} f_i \\right| + \\sum_{i=n_0}^{n-1} |g_i| \\left| \\prod_{j=i+1}^{n-1} f_j \\right|.\\]\nUnder the assumptions of the Lemma,\n\\[ |X_{n_0}| \\left| \\prod_{i= k}^{n-1}f_i \\right| \\leq C |X_{n_0}| n^{-a}\n\\leq C' n^{-a}; \\]\nand\n\\begin{align*}\n \\sum_{i=n_0}^{n-1} |g_i| \\left| \\prod_{j=i+1}^{n-1} f_j \\right|\n &\\leq C \\sum_{i=n_0}^{n-1} |g_i| (n\/i)^{-a} \\leq C^2 \\, n^{-a} \\sum_{i=n_0}^{n-1} i^{-b} i^{a} \\leq C' \\, n^{-a} \\, n^{-b+a +1} = C' n^{-b+1}.\n\\end{align*}\nThus\n\\[ |X_n| \\leq C' \\max( n^{-a} , n^{-b+1}) = C' n^{-q_{a,b}},\\]\nwhere $q_{a,b} = \\min\\{a, b-1\\}$. This completes the proof.\n\\end{proof}\n\n\\begin{lemma} \\label{Lemma2}\nFor finite non-negative integers $l,k$ such that $l\\geq k$, $m\\geq 1$ and $\\alpha\\in [0,1]$, there exists a positive constant $K=K(\\alpha,l)$ such that\n\\begin{equation} \\label{eq:bound1}\n\t\\left|\\prod_{i=l }^{n-1} \\frac{i-k+m\\alpha}{i-\\alpha} \\right| \\leq\n\tK \\left(n\/l\\right)^{-k+(m+1)\\alpha}\n\t~~\\mbox{for all $1\\le l\\le n-1$.}\n\t\\end{equation}\nand as $n\\to \\infty$\n\\begin{equation}\\label{Order:prod}\n\\prod_{i=l }^{n-1} \\frac{i-k+m\\alpha}{i-\\alpha} = \\frac{\\Gamma(l-\\alpha) }{\\Gamma(l-k+m\\alpha)} n^{-k+(m+1)\\alpha} \\left(1+o(1) \\right).\n\\end{equation}\n\\end{lemma}\n\n\\begin{proof}[Proof of Lemma \\ref{Lemma2}]\nThe bound in \\eqref{eq:bound1} follows from Lemma 2 of \\cite{Paper1}. We now prove \\eqref{Order:prod}. Note that, we can write\n\\[ \\frac{i-k+m\\alpha}{i-\\alpha} = \\frac{\\Gamma(i+1-k+m\\alpha)\\Gamma(i-\\alpha) }{\\Gamma(i-k+m\\alpha)\\Gamma(i+1-\\alpha)}.\\]\nThus\n\\begin{align}\n\\prod_{i=l }^{n-1} \\frac{i-k+m\\alpha}{i-\\alpha}\n& = \\prod_{i=l }^{n-1} \\frac{\\Gamma(i+1-k+m\\alpha)\\Gamma(i-\\alpha) }{\\Gamma(i-k+m\\alpha)\\Gamma(i+1-\\alpha) } \\nonumber\\\\\n& = \\frac{\\Gamma(n-k+m\\alpha) }{\\Gamma(l-k+m\\alpha)} \\frac{\\Gamma(l-\\alpha) }{\\Gamma(n-\\alpha)} \\label{Prod:Gamma}\\\\\n& = \\frac{\\Gamma(l-\\alpha)}{\\Gamma(l-k+m\\alpha)} \\frac{\\Gamma(n+m\\alpha) }{\\Gamma(n-\\alpha)} \\prod_{j=1}^k \\frac{1}{n-j+m\\alpha}.\\nonumber\n\\end{align}\n\n\\begin{equation}\\label{Eq:prodk}\n\\prod_{j=1}^k \\frac{1}{n-j+m\\alpha} =n^{-k}\\left(1+o(1) \\right).\n\\end{equation}\nBy Stirling's approximation formula, $\\Ga(x) = \\sqrt{2 \\pi} \\ x^{x-1\/2} e^{-x} \\left(1+o(1)\\right)$, we have\n\\begin{eqnarray}\n\\frac{\\Ga(n+m\\alpha)}{\\Ga(n-\\alpha)}\n&=& \\frac{\\sqrt{2\\pi} (n+m\\alpha)^{n+m\\alpha-1\/2} \\, e^{-(n+m\\alpha)}}{\\sqrt{2\\pi} (n-\\alpha)^{n-\\alpha-1\/2} \\, e^{-(n-\\alpha)}}\\left(1+o(1) \\right) \\nonumber \\\\\n&=& n^{(m+1)\\alpha} \\frac{(1+ m\\alpha\/n)^{n+m\\alpha-1\/2}}{(1-\\alpha\/n)^{n-\\alpha-1\/2}} e^{-(m+1)\\alpha} \\left(1+o(1) \\right) \\nonumber \\\\\n&=& n^{(m+1)\\alpha} \\left(1+o(1) \\right). \\label{Eq:Str}\n\\end{eqnarray}\nCombining \\eqref{Eq:prodk} and \\eqref{Eq:Str}, we get \\eqref{Order:prod}.\n\\end{proof}\n\n\n\\begin{proof}[Proof of Proposition \\ref{Prop:firstm}]\nRecall $\\mu_n= \\mbox{${\\mathbb E}$}[C_n]$. By Theorem \\ref{Thm:Convg-ChPh}, $\\mu_n = \\frac{1-\\alpha}{3-2\\alpha} \\ n + \\mbox{${\\mathcal O}$}(1)$. Thus, we write $\\mu_n$ as\n\\begin{equation}\\label{Exp:mu}\n\\mu_n = \\frac{1-\\alpha}{3-2\\alpha} \\ n + \\frac{\\alpha}{2(3-2\\alpha)} +x_n.\n\\end{equation}\nFor simplicity, the dependence of $\\mu_n$ and $x_n$ on $\\alpha$ are suppressed.\n\nSince $\\mu_2= \\mu_3=1$, we get $x_2 = 1-\\frac{4-3\\alpha}{2(3-2\\alpha)}=\\frac{2-\\alpha}{2(3-2\\alpha)}$\nand\n$x_3 = 1- \\frac{6-5\\alpha}{2(3-2\\alpha)} = \\frac{\\alpha}{2(3-2\\alpha)}.$\nSubstituting (\\ref{Exp:mu}) into (\\ref{cherrymean}) leads to\n$$(n-\\alpha) x_{n+1}- (n-2+\\alpha)x_n=0, \\quad n \\ge 2, $$\nand hence,\n\\[ x_n=\\begin{cases}\n\\frac{\\alpha }{2(3-2\\alpha)} \\prod_{i=3}^{n-1} \\frac{i-2+\\alpha}{i-\\alpha} & \\quad n \\ge 4, \\\\\n\\frac{\\alpha}{2(3-2\\alpha)} & \\quad n=3, \\\\\n\\frac{(2- \\alpha)}{2(3-2\\alpha)} & \\quad n=2.\n\\end{cases} \\]\nTo prove \\eqref{Order:xn}, we rewrite $x_n$ as follows\n\\[x_n = x_3 \\prod_{i=3}^{n-1} \\frac{i-2+\\alpha}{i-\\alpha} = x_3 \\frac{ \\Gamma(3-\\alpha)}{ \\Ga(1+\\alpha)} \\frac{\\Ga(n-2+\\alpha)}{\\Ga(n-\\alpha)}, \\quad n \\ge 4. \\]\nApply Lemma \\ref{Lemma2}, \\eqref{Order:xn} holds.\nConsequently,\n\\begin{equation}\\label{Exp:mu_n}\n\\mu_n = \\frac{1-\\alpha}{3-2\\alpha} n + \\frac{\\alpha}{2(3-2\\alpha)} + \\frac{ \\alpha\\Ga(3-\\alpha)}{2(3-2\\alpha) \\Ga(1+\\alpha)} n^{-2(1-\\alpha)} \\left(1+o(1) \\right).\n\\end{equation}\nThis completes the proof of part (i).\n\nThe same method of proof can be used to prove part (ii).\nRecall $\\nu_n = \\mbox{${\\mathbb E}$}[A_n]$. By Theorem \\ref{Thm:Convg-ChPh}, $\\nu_n = \\frac{1-\\alpha}{2(3-2\\alpha)} \\ n +\\mbox{${\\mathcal O}$}(1)$, and we write it as\n\n\n\\begin{equation}\\label{Exp:nu}\n\\nu_n = \\frac{1-\\alpha}{2(3-2\\alpha)} \\ n + \\frac{\\alpha}{2(3-2\\alpha)} + y_n,\n\\end{equation}\nwhere, again, the dependence of $\\nu_n$ and $y_n$ on $\\alpha$ are suppressed. Substituting (\\ref{Exp:nu}) into (\\ref{forkmean}) leads to\n\\[ y_{n+1}= \\frac{n-3+\\alpha}{n-\\alpha } y_n + \\frac{2- \\alpha}{n-\\alpha } x_n, \\quad n \\ge 4.\\]\nThe solution to this recurrence relation is given by \n\\[ y_n =y_3 \\prod_{i=3}^{n-1}\\frac{i-3+\\alpha}{i-\\alpha } + \\sum_{i=3}^{n-1} \\frac{2- \\alpha}{i-\\alpha } x_i \\prod_{j=i+1}^{n-1}\\frac{j-3+\\alpha}{j-\\alpha }.\\]\nSince $y_3=1\/2$ and the expression for $x_i$ from part (i), we get\n\\begin{align*}\ny_n\n& =y_3 \\prod_{i=3}^{n-1}\\frac{i-3+\\alpha}{i-\\alpha } + \\sum_{i=3}^{n-1} \\frac{2- \\alpha}{i-\\alpha } \\times \\frac{\\alpha }{2(3-2\\alpha)} \\prod_{j=3}^{i-1} \\frac{j-2+\\alpha}{j-\\alpha} \\times \\prod_{j=i+1}^{n-1}\\frac{j-3+\\alpha}{j-\\alpha } \\\\\n& =\\frac{1}{2} \\prod_{i=3}^{n-1}\\frac{i-3+\\alpha}{i-\\alpha } + \\frac{(2- \\alpha) \\alpha }{2(3-2\\alpha)} \\sum_{i=3}^{n-1} \\prod_{j=i+1}^{n-1}\\frac{j-3+\\alpha}{j-\\alpha } \\times \\frac{1}{i-\\alpha}\\times \\prod_{j=3}^{i-1} \\frac{j-2+\\alpha}{j-\\alpha} \\\\\n& =\\frac{1}{2} \\prod_{i=3}^{n-1}\\frac{i-3+\\alpha}{i-\\alpha } + \\frac{(2- \\alpha) \\alpha}{2(3-2\\alpha)} \\sum_{i=3}^{n-1} \\frac{1}{3-\\alpha}\\prod_{j=4}^{n-1}\\frac{j-3+\\alpha}{j-\\alpha } \\\\\n& =\\frac{1}{2} \\prod_{i=3}^{n-1}\\frac{i-3+\\alpha}{i-\\alpha } + \\frac{(2- \\alpha) \\alpha}{2(3-2\\alpha)} \\frac{(n-3)}{(3-\\alpha)} \\prod_{j=4}^{n-1}\\frac{j-3+\\alpha}{j-\\alpha }.\n\\end{align*}\nThus, for $n \\geq 5$,\n\\begin{equation}\\label{sol:yn}\ny_n = \\left( \\frac{1}{2} + \\frac{(2- \\alpha)\\alpha }{2(3-2\\alpha)} \\frac{(n-3)}{(3-\\alpha)} \\right) \\prod_{j=4}^{n-1}\\frac{j-3+\\alpha}{j-\\alpha }.\n\\end{equation}\nBy Lemma \\ref{Lemma2},\n\\begin{align*}\ny_n &= \\left( \\frac{1}{2} + \\frac{(2- \\alpha) \\alpha}{2(3-2\\alpha)} \\, \\frac{(n-3)}{(3-\\alpha)} \\right) \\frac{\\Gamma(4-\\alpha) }{\\Gamma(1+\\alpha)} n^{-3+2\\alpha} \\left(1+o(1) \\right)\\\\\n&= \\frac{(2- \\alpha) \\alpha}{2(3-2\\alpha)} \\frac{\\Gamma(3-\\alpha) }{\\Gamma(1+\\alpha)} n^{-2+2\\alpha} \\left(1+o(1) \\right) \\\\\n&=\\frac{(2- \\alpha)\\Gamma(3-\\alpha) }{2(3-2\\alpha)\\Gamma(\\alpha)} n^{-2(1-\\alpha)} \\left(1+o(1) \\right)\n\\end{align*}\nas $n \\to \\infty$.\nThis completes the proof of part (ii) and hence the Proposition.\n\\end{proof}\n\n\\begin{proof}[Proof of Proposition \\ref{Prop:secondm}]\n\nThe method of proof is similar to that of Proposition \\ref{Prop:firstm}. \n\nRecall $\\si_n ^2= \\mathrm{var}(C_n)$. From Theorem \\ref{Thm:Convg-ChPh}, we have $ \\si_n^2 = \\frac{(1-\\alpha)(2-\\alpha)}{(5-4\\alpha)(3-2\\alpha)^2} n+ \\mbox{${\\mathcal O}$}(1)$. \nWe first consider $\\mbox{${\\mathbb E}$}[C_n^2]$. As \n$$\\mbox{${\\mathbb E}$}[C_n^2] = \\mu_n^2 + \\si_n^2 = \\frac{(1-\\alpha)^2}{(3-2\\alpha)^2} n^2 +\\mbox{${\\mathcal O}$}(n),$$ we rewrite it as\n\\begin{equation}\n\\label{CM2}\n\\mbox{${\\mathbb E}$}[C_n^2] = \\frac{(1-\\alpha)^2}{(3-2\\alpha)^2} \\ n^2 + \\frac{2(1-\\alpha)(1+ 2\\alpha -2\\alpha^2)}{(5-4\\alpha)(3-2\\alpha)^2} \\ n - \\frac{\\alpha (8-17\\alpha+8\\alpha^2)}{4(5-4\\alpha)(3-2\\alpha)^2} +z_n,\n\\end{equation} \nand derive below a recursion on $z_n$. Substituting (\\ref{CM2}) into (\\ref{cherry2nd}) and after straightforward algebraic simplification, we have \n$$ (n-\\alpha) z_{n+1} - (n-4+3\\alpha) z_n = 2(1-\\alpha)(n-1) x_n, \\quad n \\ge 2. $$\nSince $C_2=C_3=1$, we get $ z_2 = \\frac{3(2-\\alpha) (8\\alpha^2 -21\\alpha+14)}{4(3-2\\alpha)^2 (5-4\\alpha)}$ and\n$ z_3 = \\frac{88 \\alpha^3 - 213 \\alpha^2 + 152 \\alpha - 24}{4(3-2\\alpha)^2 (5-4\\alpha)}.$ Consequently, \n\\[ \\si_n^2 = \\frac{(1-\\alpha)(2-\\alpha)}{(5-4\\alpha)(3-2\\alpha)^2} \\ n - \\frac{\\alpha (1-\\alpha)(2-\\alpha)}{(5-4\\alpha)(3-2\\alpha)^2} + v_n -x_n^2,\\]\nwhere\n \\[v_n=z_n - \\frac{2(1-\\alpha)}{3-2\\alpha} n x_n - \\frac{\\alpha}{3-2\\alpha} x_n = z_n - \\frac{[2(1-\\alpha) n +\\alpha]}{3-2\\alpha}x_n.\\]\nThen, for $n\\geq 6$,\n\\begin{align*}\n(n-\\alpha) v_{n+1} \n&= (n-\\alpha)z_{n+1} - \\frac{[2(1-\\alpha) (n+1) +\\alpha]}{3-2\\alpha} (n-\\alpha)x_{n+1}\\\\\n&= (n-4+3\\alpha) z_n + 2(1-\\alpha)(n-1) x_n - \\frac{[2(1-\\alpha) (n+1) +\\alpha]}{3-2\\alpha} (n-2+\\alpha)x_{n}\\\\\n& = (n-4+3\\alpha) v_n + (n-4+3\\alpha) \\frac{[2(1-\\alpha) n +\\alpha]}{3-2\\alpha}x_n \\\\\n& \\quad + 2(1-\\alpha)(n-1) x_n - \\frac{[2(1-\\alpha) (n+1) +\\alpha]}{3-2\\alpha} (n-2+\\alpha)x_{n}\\\\\n&= (n-4+3\\alpha)v_n -\\frac{2(1-\\alpha)}{3-2\\alpha} x_n.\n\\end{align*}\nEquivalently, \n\\[ v_{n+1} =\\frac{n-4+3\\alpha}{n-\\alpha} v_n -\\frac{2(1-\\alpha)}{(3-2\\alpha)} \\frac{x_n}{(n-\\alpha)}.\\]\nApplying Lemma \\ref{Lemma1}, with $f_n = \\frac{(n-4+3\\alpha)}{(n-\\alpha)}$, $g_n = -\\frac{2(1-\\alpha)x_n}{(3-2\\alpha)(n-\\alpha)}$, $a = 4-3\\alpha$ and $b= 3-2\\alpha$, we get $v_n = \\mbox{${\\mathcal O}$}(n^{-2+2\\alpha})$.\nThis proves part (i) of the proposition.\n\nPart (ii) is proved in a similar fashion. By Theorem \\ref{Thm:Convg-ChPh}, $ Cov(A_n, C_n) = -\\frac{(1-\\alpha)(2-\\alpha)(1-2\\alpha)}{2(3-2\\alpha)^2(5-4\\alpha)} n+ \\mbox{${\\mathcal O}$}(1).$ \nSince $\\mbox{${\\mathbb E}$}[A_nC_n]= Cov(A_n, C_n)+\\mu_n \\nu_n $, with $\\mu_n$ and $ \\nu_n $ found in Proposition \\ref{Prop:firstm}, we write \n\\begin{eqnarray}\n\\mbox{${\\mathbb E}$}[A_nC_n] &=& \\frac{(1-\\alpha)^2}{2(3-2\\alpha)^2} \\ n^2 -\\frac{(1-\\alpha)(4-25\\alpha+16\\alpha^2)}{4(5-4\\alpha)(3-2\\alpha)^2} n -\\frac{\\alpha (8- 17\\alpha +8\\alpha^2 )}{4(5-4\\alpha)(3-2\\alpha)^2}+ t_n. \\label{Eq:E[AnCn]}\n\\end{eqnarray}\nCombining (\\ref{cov}) and \\eqref{Eq:E[AnCn]}, $t_n$ satisfies the recursion, \n\\begin{equation}\n(n-\\alpha)t_{n+1}-(n-5+3\\alpha)t_n = (2-\\alpha) z_n + (1-\\alpha)(n-1)y_n, \\quad n\\geq 6.\n\\end{equation}\nBy \\eqref{Exp:mu}, \\eqref{Exp:nu} and \\eqref{Eq:E[AnCn]},\n\\begin{eqnarray*}\nCov(A_n, C_n) &=& \\frac{-(1-\\alpha)(2-\\alpha)(1-2\\alpha)}{2(5-4\\alpha)(3-2\\alpha)^2} \\ n -\\frac{ \\alpha (1-\\alpha)(2-\\alpha)}{(5-4\\alpha)(3-2\\alpha)^2} + w_n -x_n y_n,\n\\end{eqnarray*}\nwhere $w_n = t_n -\\dfrac{[(1-\\alpha)n +\\alpha]x_n + [2(1-\\alpha)n +\\alpha]y_n}{2(3-2\\alpha)}$. Consider\n\\begin{align*}\n&(n-\\alpha) w_{n+1} \\\\\n&=(n-\\alpha) t_{n+1} -(n-\\alpha)\\frac{[(1-\\alpha)(n+1) +\\alpha]x_{n+1} + [2(1-\\alpha)(n+1) +\\alpha]y_{n+1}}{2(3-2\\alpha)}\\\\\n&= (n-5+3\\alpha) t_n +(2-\\alpha)z_n+(1-\\alpha)(n-1)y_n \\\\\n& \\quad - (n-\\alpha) \\frac{[(1-\\alpha)(n+1) +\\alpha]x_{n+1} + [2(1-\\alpha)(n+1) +\\alpha]y_{n+1}}{2(3-2\\alpha)} \\\\\n&= (n-5+3\\alpha) \\left( t_n -\\frac{[(1-\\alpha)n +\\alpha]x_n + [2(1-\\alpha)n +\\alpha]y_n}{2(3-2\\alpha)}\\right) \\\\\n& \\quad +(n-5+3\\alpha) \\frac{[(1-\\alpha)n +\\alpha]x_n + [2(1-\\alpha)n +\\alpha]y_n}{2(3-2\\alpha)}\\\\\n&\\quad +(2-\\alpha)z_n+(1-\\alpha)(n-1)y_n -(n-\\alpha) \\frac{[(1-\\alpha)(n+1) +\\alpha]x_{n+1} + [2(1-\\alpha)(n+1) +\\alpha]y_{n+1}}{2(3-2\\alpha)}\\\\\n&= (n-5+3\\alpha)w_n +(n-5+3\\alpha) \\frac{[(1-\\alpha)n +\\alpha]x_n + [2(1-\\alpha)n +\\alpha]y_n}{2(3-2\\alpha)}\\\\\n&\\quad +(2-\\alpha)z_n+(1-\\alpha)(n-1)y_n -(n-\\alpha) \\frac{[(1-\\alpha)(n+1) +\\alpha]x_{n+1} + [2(1-\\alpha)(n+1) +\\alpha]y_{n+1}}{2(3-2\\alpha)}.\n\\end{align*}\nThus $w$ satisfies the following recursion for $n\\geq 6$\n\\begin{align*}\n(n-\\alpha) w_{n+1} - (n-5+3\\alpha)w_n &= {\\rm RHS},\n\\end{align*}\nwhere RHS is given by \n\\begin{align*}\n& (n-5+3\\alpha) \\frac{[(1-\\alpha)n +\\alpha]x_n + [2(1-\\alpha)n +\\alpha]y_n}{2(3-2\\alpha)} +(1-\\alpha) (n-1)y_n \\\\\n&\\quad - (n-\\alpha)\\frac{[(1-\\alpha)(n+1) +\\alpha]x_{n+1} + [2(1-\\alpha)(n+1) +\\alpha]y_{n+1}}{2(3-2\\alpha)} +(2-\\alpha)z_n \\\\\n& = (2-\\alpha)v_n - \\frac{1-\\alpha}{3-2\\alpha} x_n.\n\\end{align*}\nWe omit the straightforward but tedious algebraic simplification steps. Hence,\n\\begin{equation}\\label{Rec:w}\n w_{n+1} =\\frac{ n-5+3\\alpha}{n-\\alpha} w_n + (2-\\alpha) \\frac{v_n}{n-\\alpha} - \\frac{(1-\\alpha)}{(3-2\\alpha)} \\frac{x_n}{(n-\\alpha)}.\n\\end{equation}\nApplying Lemma \\ref{Lemma1} with $f_n = \\frac{n-5+3\\alpha}{n-\\alpha} $, $g_n = \\ (2-\\alpha) \\frac{v_n}{n-\\alpha} - \\frac{(1-\\alpha)}{(3-2\\alpha)} \\frac{x_n}{(n-\\alpha)} $, $a = 5-4\\alpha$ and $b= 3-2\\alpha$, we get $v_n = \\mbox{${\\mathcal O}$}(n^{-2+2\\alpha})$. This proves part (ii).\n\nTo prove (iii), we let $\\tau_n^2 =\\mathrm{var}(A_n) = \\frac{(1-\\alpha)(2-\\alpha)}{(3-2\\alpha)^2(5-4\\alpha)} n +\\mbox{${\\mathcal O}$}(1)$ by Theorem \\ref{Thm:Convg-ChPh}. So, \n$ \\mbox{${\\mathbb E}$}[A_n^2] = \\nu_n^2 + \\tau_n^2= \\frac{(1-\\alpha)^2}{4(3-2\\alpha)^2} n^2 +\\mbox{${\\mathcal O}$}(n)$. We write \n\\[ \\mbox{${\\mathbb E}$}[A_n^2] = \\frac{(1-\\alpha)^2}{4(3-2\\alpha)^2} n^2 + \\frac{2(1-\\alpha)(1+ 2\\alpha -2\\alpha^2)}{(5-4\\alpha)(3-2\\alpha)^2} n + \\frac{\\alpha( 5-3\\alpha+\\alpha^2)}{4(3-2\\alpha) (5-4\\alpha)(7-4 \\alpha)} + s_n. \\]\n\nLet $u_n = s_n - \\dfrac{[(1-\\alpha)n + \\alpha] y_n}{3-2\\alpha}$. Then,\n\\begin{align*}\n(n-\\alpha) u_{n+1} & = (n-\\alpha) s_{n+1} - \\frac{[(1-\\alpha)(n+1) + \\alpha] (n-\\alpha) y_{n+1} }{3-2\\alpha} \\\\\n& = (n-6+3\\alpha)s_n + (2-\\alpha)(2t_n+x_n)-y_n - \\frac{[(1-\\alpha)(n+1) + \\alpha] (n-\\alpha) y_{n+1} }{3-2\\alpha} \\\\\n& = (n-6+3\\alpha)[ s_n-\\frac{[(1-\\alpha)n + \\alpha] y_n}{3-2\\alpha}] + (n-6+3\\alpha) \\frac{[(1-\\alpha)n + \\alpha] y_n}{3-2\\alpha} \\\\\n&\\quad + (2-\\alpha)(2t_n+x_n)-y_n - \\frac{[(1-\\alpha)(n+1) + \\alpha] (n-\\alpha) y_{n+1} }{3-2\\alpha} \\\\\n& = (n-6+3\\alpha)u_n + (n-6+3\\alpha) \\frac{[(1-\\alpha)n + \\alpha] y_n}{3-2\\alpha} \\\\\n&\\quad + (2-\\alpha)(2t_n+x_n)-y_n - \\frac{[(1-\\alpha)(n+1) + \\alpha] (n-\\alpha) y_{n+1} }{3-2\\alpha}.\n\\end{align*}\nThus,\n\\begin{align*}\n&(n-\\alpha) u_{n+1} - (n-6+3\\alpha)u_n \\\\\n& = (n-6+3\\alpha) \\frac{[(1-\\alpha)n + \\alpha] y_n}{3-2\\alpha} -y_n - \\frac{[(1-\\alpha)(n+1) + \\alpha] (n-\\alpha) y_{n+1} }{3-2\\alpha} +(2-\\alpha)x_n + 2(2-\\alpha)t_n\\\\\n& = (n-6+3\\alpha) \\frac{[(1-\\alpha)n + \\alpha]y_n }{3-2\\alpha} -y_n - \\frac{[(1-\\alpha)(n+1) + \\alpha] [ (n-3+\\alpha) y_n + (2-\\alpha) x_n] }{3-2\\alpha}\\\\\n& \\quad + 2(2-\\alpha)w_n +(2-\\alpha)x_n + (2-\\alpha) \\frac{[(1-\\alpha)n +\\alpha]x_n + [2(1-\\alpha)n +\\alpha]y_n}{3-2\\alpha}\\\\\n& = y_n \\left\\{ (n-6+3\\alpha) \\frac{[(1-\\alpha)n + \\alpha] }{3-2\\alpha} -1 +(2-\\alpha) \\frac{[2(1-\\alpha)n +\\alpha]}{3-2\\alpha} - (n-3+\\alpha) \\frac{[(1-\\alpha)(n+1) + \\alpha] }{3-2\\alpha} \\right\\} \\\\\n& \\quad +2(2-\\alpha)w_n +(2-\\alpha) x_n \\left\\{ \\frac{[(1-\\alpha)n +\\alpha] }{3-2\\alpha} - \\frac{[(1-\\alpha)(n+1) + \\alpha]}{3-2\\alpha}+1\\right\\} \\\\\n& =2(2-\\alpha)w_n - y_n \\frac{\\alpha(2-\\alpha)}{3-2\\alpha} +\\frac{(2-\\alpha)^2}{3-2\\alpha} x_n.\n\\end{align*}\nEquivalently, \n\\[ u_{n+1} = \\frac{n-6+3\\alpha}{n-\\alpha} u_n + \\frac{\\alpha(2-\\alpha)}{(3-2\\alpha)} \\frac{y_n}{(n-\\alpha)} +\\frac{(2-\\alpha)^2}{(3-2\\alpha)} \\frac{x_n}{(n-\\alpha)}.\\]\nApply Lemma \\ref{Lemma1} as in the proofs of parts (i) and (ii), \nwe can conclude that $u_n =\\mbox{${\\mathcal O}$}(n^{-2+2\\alpha})$. This completes the proof of (iii) and hence the Proposition.\n\\end{proof}\n\n\\section*{Acknowledgements} \nThe work of Gursharn Kaur was supported by NUS Research Grant R-155-000-198-114, and that of Kwok Pui Choi by Singapore Ministry of Education Academic Research Fund\nR-155-000-188-114. We thank Chris Greenman and Ariadne Thompson for stimulating discussions on the Ford model. \n\n\n\n\\bibliographystyle{abbrv} \n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\nThe Dirac semi-metals, whose low energy physics can be described by three dimensional (3D) pseudorelativistic Dirac equation with the linear dispersion around the Fermi level \\cite{Burkov2011}, have attracted lots of attention in recent days, owing to their exotic physical properties \\cite{WangZJ-2012-Na3Bi,WangZJ-2013,li2010dynamical,potter2014quantum,Parameswaran2014} and large application potentials in the future \\cite{Abrikosov1998,liang2014,He2014}. Current studies mainly focus on two types of Dirac semi-metals with both inversion symmetry and time-reversal (TR) symmetry. One is achieved at the critical point of a topological phase transition. This type of Dirac semi-metal is not protected by any topology and can be gapped easily via small perturbations \\cite{sato2011-TlBiSSe,wu2013sudden,LiuJP2013}. In contrast, the other type is protected by the uniaxial rotation symmetry \\cite{ChenF2012}, so is quite stable. And according to even or odd parity of the states at the axis of $C_n$ rotation, the symmetry protected Dirac semi-metals can be further classified as two subclasses \\cite{YangBJ2014}. The first subclass has a single Dirac point (DP) at a time-reversal invariant momentum (TRIM) point on the rotation axis protected by the lattice symmetry \\cite{YoungSM2012,Steinberg2014}, while the second one possesses non-trivial band inversion and has a pair of DPs on the rotation axis away from the TRIM points. For the materials of the second subclass (such as Na$_3$Bi \\cite{WangZJ-2012-Na3Bi,liu2014discovery}, Cd$_3$As$_2$ \\cite{WangZJ-2013,liu2014stable,Borisenko2014,yi2014evidence,liang2014,JeonSJ2014,He2014,Narayanan2015}, and some charge balanced compounds \\cite{gibson20143d,du2014dirac}) the non-zero $\\mathbb{Z}_{2}$ number can be well defined at the corresponding two dimensional (2D) plane of the Brillouin zone (BZ) \\cite{Morimoto2014,Gorbar2015}. And due to the non-trivial topology, these stable Dirac semi-metals are regarded as a copy of Weyl semi-metals \\cite{YangBJ2014}. Thus, its Fermi arcs are observed on the specific surfaces \\cite{xu2015}, and a quantum oscillation of the topological property is expected to be achieved in the thin film with the change of thicknesses \\cite{WangZJ-2013}.\n\nIn spite of these successful progresses, the 3D Dirac semi-metal materials either take uncommon lattice structures or contain heavy atoms, which are not compatible with current semiconductor industry. On the other hand, the group \\uppercase\\expandafter{\\romannumeral4} elements, including C, Si, Ge, Sn and Pb, have been widely used in electronics and microelectronics. Generally, for some of the group \\uppercase\\expandafter{\\romannumeral4} elements, the diamond structure is one of the most stable 3D forms at ambient conditions. However, under specific experimental growth conditions, various allotropes with exotic phyiscal and chemical properties are discovered experimentally. For example, the new orthorhombic allotrope of silicon, Si$_{24}$, is found to be a semiconductor with a direct gap of 1.3 eV at the $\\Gamma$ point \\cite{kim2015Si24}; and the 2D forms of silicene \\cite{Seymur2009,Seymur2013-Sil,Seymure-sil-2014}, germanene \\cite{Daviala2014,Chensi-2014} and stanene \\cite{TangPz2014-stanene,Yong2013,zhu2015epitaxial} have been theoretically predicted to exist or experimentally grown on different substrates, which can be 2D topological insulators (TIs) and used as 2D field-effect transistors \\cite{tao2015silicene}.\n\nIn this article, by using \\emph{ab initio} density functional theory (DFT) with hybrid functional \\cite{heyd2003hybrid}, we predict new 3D metastable allotropes for Ge and Sn with staggered layered dumbbell (SLD) structure, named as germancite and stancite; and discover that they are stable Dirac semi-metals with a pair of gapless DPs on the rotation axis of $C_3$ protected by the lattice symmetry. Similar to the conventional Dirac semi-metals, such as Na$_3$Bi and Cd$_3$As$_2$, the topologically non-trivial Fermi arcs can be observed on the surfaces parallel to the rotation axis in the germancite and stancite. And via tuning the Fermi level, we can observe a Lifshitz transition in the momentum space. More importantly for future applications, the thin film of the germancite is found to be an intrinsic 2D TI, and the ultrahigh mobility and giant magnetoresistance can be expected in these compounds due to the 3D linear dispersion.\n\n\\section{Methods}\nThe calculations were carried out by using DFT with the projector augmented wave method \\cite{PhysRevB.50.17953,PhysRevB.59.1758}, as implemented in the Vienna \\textit{ab initio} simulation package \\cite{PhysRevB.54.11169}. Plane wave basis set with a kinetic energy cutoff of $\\mathrm{250~eV}$ and $\\mathrm{150~eV}$ was used for germancite and stancite respectively. The structure is allowed to fully relax until the residual forces are less than $1\\times 10^{-3}~\\mathrm{eV\/\\AA}$. The Monkhorst-Pack $k$ points are $9\\times 9\\times 9$. With the relaxed structure, the electronic calculation of germancite and stancite using hybrid functional HSE06 \\cite{heyd2003hybrid} has been done with and without SOC. The maximally localized Wannier functions \\cite{Mostofi2008685} are constructed to obtain the tight-binding Hamiltonian for the Green's function method \\cite{0305-4608-15-4-009}, which is used to calculate the surface electronic spectrum and surface states.\n\n\\section{Results}\nAs shown in Fig. \\ref{fig:1}, the germancite and stancite share the same rhombohedral crystal structure with the space group of $D_{3d}^6$ ($R\\bar{3}c$) \\cite{PhysRevB.90.085426}, which contains the spacial inversion symmetry and $C_3$ rotation symmetry along the trigonal axis (defined as $z$ axis). In one unit-cell, fourteen atoms bond with each others to form six atomic layers; and in each layer, one dumbbell site can be observed. To clearly visualize the SLD structure in the germancite and stancite, we plot the side view of the hexagonal lattice shown in Fig. \\ref{fig:1}(b) and the top view from (111) direction in Fig. \\ref{fig:1}(c). As the grey shadow shown, the layers containing dumbbell sites stack along (111) direction in the order of $\\cdots B\\bar{A}C\\bar{B}A\\bar{C}\\cdots$. The interlayer interaction is the covalent bonding between adjacent layers, whose bond lengths are almost equal to those of intralayer bonding (the difference is about $0.03$\\AA). Meanwhile, different from the diamond structure, the tetrahedral symmetry is absent in the SLD structure and the coupling here is not typical $sp^3$ hybridization. Furthermore, in order to test the structural stability, we calculate the phonon dispersion for the germancite and stancite shown in Fig. \\ref{fig:1}(e). It can be seen that the frequencies of all modes are positive over the whole Brillouin zone, which indicates that the SLD structures are thermodynamically stable. Furthermore, compared with the other experimentally discovered metastable allotropes of Ge and Sn \\cite{guloy2006guest,kiefer2010synthesis,PhysRevLett.110.085502,ja304380p,Ceylan2015407,PhysRevB.34.362}, the germancite and stancite share the same order of magnetite of the mass density and cohesive energies (see Supplemental Information for details), so we expect the germancite and stancite could be composed in the future experiments.\n\n\n\\begin{figure}\n\\centerline{ \\includegraphics[clip,width=0.8\\linewidth]{Figure1.eps}}\n \\caption{(Color online) (a) The unit cell of the SLD structure with three private lattice vectors set as \\textbf{a$_{1,2,3}$}. The balls in different colors stand for the same kind of atoms in different layers. (b) The side view and (c) top view of the SLD structure. The layers containing dumbbell (DB) structures are labelled. The letters ($A,B,C$) denote the positions of DB sites and the sign of bar is applied to distinguish between two trigonal lattices transformed to each other by inversion. As an example, the top view of two adjacent layers (marked by dashed blue lines) is shown. The DB structures are labeled by the grey shadow shown in the top view of a single layer, and the atoms in one DB structure are represented by grey balls. (d) The 3D Brillouin zone (BZ) of germancite and stancite. The four inequivalent TRIM points are $\\Gamma$ (0,0,0), $L$ (0,$\\pi$,0), $F$ ($\\pi$,$\\pi$,0) and T ($\\pi$,$\\pi$,$\\pi$). The hexagon and square, connected to $\\Gamma$ by blue lines, show the 2D BZs projected to (111) and (2$\\bar{1}\\bar{1}$) surfaces respectively, and the high-symmetry $k$ points are labelled. (e) The phonon dispersion of germancite and stancite along high symmetry lines of 3D BZ.}\n\\label{fig:1}\n\\end{figure}\n\nThe calculated electronic structures of the germancite and stancite around the Fermi level are shown in Fig. \\ref{fig:2}(a), in which the solid lines and the yellow shadow stand for the bulk bands with and without spin-orbit coupling (SOC) respectively. It could be observed that: when the SOC effect is not included, the germancite is a conventional semi-metal whose bottom of the conduction bands and top of valence bands touch at the $\\Gamma$ point with the parabolic dispersions; while for stancite, it is a metal whose band touching at the $\\Gamma$ point is higher than the Fermi level. When the SOC effect is fully considered, our calculations indicate both germancite and stancite to be 3D Dirac semi-metals with a pair of DPs in the trigonal rotation axis (DP at (0,0,$\\pm k_{z0}$)). Therefore, the low energy physics of this kind of materials can be described by the 3D Dirac-type Hamiltonian. And the schematic band structure based on the effective $k\\cdot p$ model (see Supplemental Information for details) for germancite and stancite is shown in Fig. \\ref{fig:2}(c), in which the pair of 3D DPs is clear.\n\nTo understand the physical origin of the 3D gapless Dirac Fermions in the SLD structure, we plot the schematic diagram of the band evolution for the germancte and stancite in Fig. \\ref{fig:2}(b). In contrast to isotropic coupling in the diamond structure, the hybridizations in the layered SLD structure are anisotropic, in which the inter-layer couplings are relatively weaker than intra-layer couplings and the $p_z$ and $p_{x\\pm iy}$ states are splited. Furthermore, based on our calculations, the kind of anisotropic coupling will further shift down the anti-bonding state of $s$ orbital which is even lower than the bonding states of the $p_{x\\pm iy}$ orbitals at the $\\Gamma$ point. So the band inversion occurs at the $\\Gamma$ point even without SOC effect, and the SOC herein just removes the degeneracy of $p_{x\\pm iy}$ orbitals around the Fermi level. In the 2D BZ which contains the $\\Gamma$ point and is perpendicular to the $\\Gamma$-$\\text{T}$ direction, the non zero $\\mathbb{Z}_{2}$ topological number can be well defined. On the other hand, the $C_{3v}$ symmetry along the $\\Gamma$-$\\text{T}$ line contains one 2D ($\\Lambda_{4}$) and two degenerate 1D ($\\Lambda_{5}$, $\\Lambda_{6}$) irreducible representations for its double space group \\cite{koster1963properties}. As shown in the Fig. \\ref{fig:2}(b), the two crossing bands at the Fermi level belong to $\\Lambda_{5}+\\Lambda_{6}$ and $\\Lambda_{4}$ respectively. So there is no coupling and a TR pair of 3D DPs can be observed at the Fermi level along the $\\Gamma$-$\\text{T}$ direction.\n\n\\begin{figure}\n\\centerline{ \\includegraphics[clip,width=0.8\\linewidth]{Figure2.eps}}\n \\caption{(Color online) (a) The band structures of germancite (left) and stancite (right) along high symmetry lines with the corresponding DOS around the Fermi level (dashed horizontal line). In the k-path $\\text{T}$-$\\Gamma$, the size of the red dots represents the contribution from the atomic $s$ and $p_z$ orbitals. The cyan dots are the Dirac points at (0,0,$k_{z0}$), where $k_{z0}\\approx 0.08 $ \\AA$^{-1}$ and $\\approx 0.18 $ \\AA$^{-1}$ respectively. Shaded regions denote the calculated energy spectrum without SOC. (b) Schematic diagrams of the lowest conduction bands and highest valence bands from the $\\text{T}$ point to the $\\Gamma$ point for germancite and stancite. The black lines present the SOC effect at the $\\text{T}$ and $\\Gamma$ point. Between them, the red and blue lines denote doubly degenerate bands belonging to different irreducible representations, where the solid\/dashed red line is for germancite\/stancite. And the crossing points (solid cyan dots) correspond to those gapless Dirac points in (a) respectively. (c) Schematic band dispersion based on the effective $k\\cdot p$ model for germancite and stancite. The $k_{\\perp}$ direction refers to any axis perpendicular to the $k_{z}$ direction in the momentum space and the color becomes warmer, as the energy increases.}\n\\label{fig:2}\n\\end{figure}\n\nDue to the non-trivial topology of 3D Dirac semi-metals, the projected 2D DPs and Fermi arcs are expected to be observed on some specific surfaces for the germancite and stancite. As shown in the Fig. \\ref{fig:3}, by using the surface Green's function method \\cite{0305-4608-15-4-009}, we study the electronic spectrum on the (111) and (2$\\bar{1}\\bar{1}$) surface whose BZs are perpendicular and parallel to the $\\Gamma$-$\\text{T}$ direction respectively. For the BZ of (111) surface, the pair of 3D DPs project to the $\\widetilde{\\Gamma}$ point as 2D Dirac cones (see Fig. \\ref{fig:3}(a) and (d)); when the coupling between two projected 2D DPs is considered, a finite band gap could be easily obtained. Furthermore, besides the projected Dirac cones, we also observe the trivial surface states in the germancite and stancite ($\\alpha_{1,2}$ states in the Fig. \\ref{fig:3}(a) and (d)) which mainly originate from the dangling bonds on the (111) surface.\n\n\\begin{figure}\n\\centerline{ \\includegraphics[clip,width=0.8\\linewidth]{Figure3.eps}}\n \\caption{(Color online) The electronic spectrum on the $(111)$ surface and its corresponding Fermi surface for (a) germanctie and (d) stancite respectively. Two bulk DPs are projected to the $\\widetilde{\\Gamma}$ point. The electronic spectrum on the $(2\\bar{1}\\bar{1})$ surface and its corresponding Fermi surface for (b) germanctie and (e) stancite respectively. The cyan dots label the projected DPs and the yellow dot represents the band crossing at the $\\bar{\\Gamma}$ point. On the Fermi surface, the Fermi arcs connect two projected DPs (cyan dots). For stancite $(2\\bar{1}\\bar{1})$ surface, the constant-energy contour is at $\\epsilon_f-5.2$ meV, slightly away from the Fermi level, to distinguish the Fermi arcs. Stacking plots of constant-energy contours at different energies on its $(2\\bar{1}\\bar{1})$ surface of (c) germanctie and (f) stancite respectively. The Fermi level is set to be zero.}\n\\label{fig:3}\n\\end{figure}\n\nFor the (2$\\bar{1}\\bar{1}$) surface of the germancite and stancite, the electronic structures are quite different. Because the BZ of (2$\\bar{1}\\bar{1}$) surface is parallel to the $\\Gamma$-$\\text{T}$ direction, the pair of 3D DPs are projected to different points (0,0,$\\bar{\\pm k_{z0}}$) which are marked by the cyan dots in the Fig. \\ref{fig:3}(b) and (e). Between the projected DPs, a pair of the Fermi arcs could be observed clearly, which share the helical spin-texture and are not continuous at the projected points. This Fermi arcs originate from the non-trivial $\\mathbb{Z}_{2}$ topology in the Dirac semi-metals. On any 2D plane in the bulk whose BZ is perpendicular to the $\\Gamma$-$\\text{T}$ direction with $-k_{z0} = \\int d^3{r}_1 \\ldots d^3{r}_M\\; \\psi^*(\\bar{r}_1, \\ldots ,\\bar{r}_M,t)\\;{A}\\;\\psi(\\bar{r}_1, \\ldots ,\\bar{r}_M,t)\\;.\\label{grom8}\n\\end{eqnarray}\n\n\\section{Many-body quantum mechanics for twisted N-enla-rged Newton-Hooke space-times}\n~~\\\\\n~~\\\\\n~~\\\\\nLet us now turn to the main aim of our investigations - to the quantum mechanical model of many particles defined on quantum space-times (\\ref{spaces300}).\nIn first step of our construction we extend the described in second section spaces to the whole algebra of momentum and position operators as follows\n\\begin{eqnarray}\n&&[\\;\\hat{ x}_{1A},\\hat{ x}_{2B}\\;] = if_{\\kappa_a}({t})\\delta_{AB}\\;\\;\\;,\\;\\;\\;[\\;\\hat{ x}_{1A},\\hat{ x}_{3B}\\;] =\n [\\;\\hat{ x}_{2A},\\hat{ x}_{3B}\\;] =\n [\\;\\hat{ p}_{iA},\\hat{ p}_{jB}\\;] =0\\;,\\label{phasespaces1}\\\\\n&&~~~~~~~~~~~~~~~~~[\\;\\hat{ x}_{iA},\\hat{ p}_{jB}\\;] = {i\\hbar}\\delta_{ij}\\delta_{AB}\\;\\;;\\;\\;i,j=1,2,3\\;. \\label{phasespaces2}\n\\end{eqnarray}\nOne can check that relations (\\ref{phasespaces1}), (\\ref{phasespaces2}) satisfy the Jacobi identity and for deformation parameters\n$\\kappa_a$ approaching zero become classical. \\\\\nNext, by analogy to the commutative case (see formula (\\ref{grom0})) we define the following multi-particle hamiltonian operator\n\\begin{eqnarray}\nH(\\bar{\\hat{p}}_1, \\ldots ,\\bar{\\hat{p}}_M;\\bar{\\hat{r}}_1, \\ldots ,\\bar{\\hat{r}}_M) =\n\\sum_{A=1}^{M}\\left(\\frac{\\bar{\\hat{p}}_A^2}{2m_A} +V_A(\\bar{\\hat{r}}_A)\\right)\n+\\frac{1}{2}\\sum_{A\\ne B}V_{AB}(\\bar{\\hat{r}}_A,\\bar{\\hat{r}}_B)\\;,\n\\label{gromek1}\n\\end{eqnarray}\nwith $\\bar{\\hat{p}}_{A}=[\\;{\\hat{x}_{1A},\\hat{p}_{2A},\\hat{p}_{3A}}\\;]$ and $\\bar{\\hat{r}}_{A}=[\\;{\\hat{x}_{1A},\\hat{x}_{2A},\\hat{x}_{3A}}\\;]$. \\\\\nIn order to analyze the above system we represent the\nnoncommutative operators $({\\hat x}_{iA}, {\\hat p}_{iA})$ by classical\nones $({ x}_{iA}, { p}_{iA})$ as (see e.g.\n\\cite{lodzianieosc}, \\cite{lukiluk2})\n\\begin{eqnarray}\n&~~&{\\hat x}_{1A} = { x}_{1A} - \\frac{1}{2\\hbar}f_{\\kappa_a}(t)\np_{2A}\\;\\;\\;,\\;\\;\\;{\\hat x}_{2A} = { x}_{2A} +\\frac{1}{2\\hbar}f_{\\kappa_a}(t)\np_{1A}\\;,\\\\\n&~~&~~~~~~~~~~~~~~~~ {\\hat x}_{3A}= x_{3A} \\;\\;\\;,\\;\\;\\; {\\hat p}_{iA}=\np_{iA}\\;. \\label{rep}\n\\end{eqnarray}\nThen, the hamiltonian (\\ref{gromek1}) takes the form\n\\begin{eqnarray}\n&~~&H(\\bar{p}_1, \\ldots ,\\bar{p}_M;\\bar{r}_1, \\ldots ,\\bar{r}_M,t) = \\nonumber \\\\\n&=&\\sum_{A=1}^{M}\\left[\\;\\frac{\\bar{{p}}_A^2}{2m_A} +\nV_A\\left(\\bar{\\hat{r}}_A = \\left({ x}_{1A} - \\frac{1}{2\\hbar}f_{\\kappa_a}(t)\np_{2A},{ x}_{2A} +\\frac{1}{2\\hbar}f_{\\kappa_a}(t)\np_{1A},x_{3A}\\right)\\right)\\right. + \\nonumber \\\\\n&~~&~~~~+\\frac{1}{2}\\sum_{A\\ne B}V_{AB}\\left(\\bar{\\hat{r}}_A = \\left({ x}_{1A} - \\frac{1}{2\\hbar}f_{\\kappa_a}(t)\np_{2A},{ x}_{2A} +\\frac{1}{2\\hbar}f_{\\kappa_a}(t)\np_{1A},x_{3A}\\right), \\right.\\label{gromek2}\\\\\n&,&\\left.\\left.\\bar{\\hat{r}}_B = \\left({ x}_{1B} - \\frac{1}{2\\hbar}f_{\\kappa_a}(t)\np_{2B},{ x}_{2B} +\\frac{1}{2\\hbar}f_{\\kappa_a}(t)\np_{1B},x_{3B}\\right)\\right)\\;\\right]\n\\;,\\nonumber\n\\end{eqnarray}\nand, consequently, the corresponding Schroedinger equation in the position representation looks as follows\n\\begin{eqnarray}\n&~~&i\\frac{\\partial}{\\partial t} \\psi(\\bar{r}_1, \\ldots ,\\bar{r}_M,t) = \\nonumber \\\\\n&=&\\left\\{\\;\\sum_{A=1}^{M}\\left[\\;\\frac{1}{2m_A}\\Delta_A +\nV_A\\left(\\bar{\\hat{r}}_A = \\left({ x}_{1A} + \\frac{i}{2}f_{\\kappa_a}(t)\n{\\partial_{2A}},{ x}_{2A} -\\frac{i}{2}f_{\\kappa_a}(t)\n{\\partial_{1A}},x_{3A}\\right)\\right)\\right. \\right.+ \\nonumber \\\\\n&~~&~~~~~~~+\\frac{1}{2}\\sum_{A\\ne B}V_{AB}\\left(\\bar{\\hat{r}}_A = \\left({ x}_{1A} + \\frac{i}{2}f_{\\kappa_a}(t)\n{\\partial_{2A}},{ x}_{2A} -\\frac{i}{2}f_{\\kappa_a}(t)\n{\\partial_{1A}},x_{3A}\\right), \\right.\\label{gromek2}\\\\\n&,&\\left.\\left. \\left.\\bar{\\hat{r}}_B = \\left({ x}_{1B} + \\frac{i}{2}f_{\\kappa_a}(t)\n{\\partial_{2B}},{ x}_{2B} -\\frac{i}{2}f_{\\kappa_a}(t)\n{\\partial_{1B}},x_{3B}\\right)\\right)\\;\\right] \\;\\right\\}\\psi(\\bar{r}_1, \\ldots ,\\bar{r}_M,t)\\;.\\nonumber\n\\end{eqnarray}\nFurther, we expand the hamiltonian function (\\ref{gromek2}) in Taylor series up to the terms linear in deformation parameter $\\kappa_a$, i.e. to the terms linear in\nfunction $f_{\\kappa_a}(t)$; then, we have\\footnote{We denote by ${\\cal O}(\\kappa_a)$ the higher order terms in deformation parameter $\\kappa_a$.}\n\\begin{eqnarray}\n&~~&H(\\bar{p}_1, \\ldots ,\\bar{p}_M;\\bar{r}_1, \\ldots ,\\bar{r}_M,t) = \\nonumber \\\\\n&=&\\sum_{A=1}^{M}\\left(\\frac{\\bar{{p}}_A^2}{2m_A} +V_A(\\bar{{r}}_A)\\right)\n+\\frac{1}{2}\\sum_{A\\ne B}V_{AB}(\\bar{{r}}_A,\\bar{{r}}_B) +\\nonumber \\\\\n&~~&~~~~+\\left[\\;\\sum_{A=1}^{M}\\left(\\left.-\\frac{\\partial V_A(\\bar{\\hat{r}}_A)}{\\partial \\hat{x}_{1A}}\\cdot \\frac{1}{2 \\hbar}p_{2A}\\right.\\right.\n+\\left.\\left.\\frac{\\partial V_A(\\bar{\\hat{r}}_A)}{\\partial \\hat{x}_{2A}}\\cdot \\frac{1}{2 \\hbar}p_{1A}\\right.\\right)\\right.+\\nonumber\\\\\n&+&\\frac{1}{2}\\sum_{A\\ne B}\\left.\\left.\\left(-\\frac{\\partial V_{AB}(\\bar{\\hat{r}}_A,\\bar{\\hat{r}}_B)}{\\partial \\hat{x}_{1A}}\\cdot \\frac{1}{2 \\hbar}p_{2A}\\right.\n+ \\frac{\\partial V_{AB}(\\bar{\\hat{r}}_A,\\bar{\\hat{r}}_B)}{\\partial \\hat{x}_{2A}}\\cdot\\frac{1}{2 \\hbar}p_{1A}\\right.\\right. + \\label{gromek4}\\\\\n&-&\\left.\\left.\\left.\\left.\\left.\\frac{\\partial V_{AB}(\\bar{\\hat{r}}_A,\\bar{\\hat{r}}_B)}{\\partial \\hat{x}_{1B}}\\cdot \\frac{1}{2 \\hbar}p_{2B}\\right.\n+ \\frac{\\partial V_{AB}(\\bar{\\hat{r}}_A,\\bar{\\hat{r}}_B)}{\\partial \\hat{x}_{2B}}\\cdot \\frac{1}{2 \\hbar}p_{1B}\\right.\\right)\\;\\right]\\right|_{f_{\\kappa_a}(t)=0}\\cdot f_{\\kappa_a}(t) +\\nonumber \\\\\n&+& {\\cal O}(\\kappa_a) \\;, \\nonumber\n\\end{eqnarray}\nwith the corresponding wave equation given by\n\\begin{eqnarray}\n&~~&i\\frac{\\partial}{\\partial t} \\psi(\\bar{r}_1, \\ldots ,\\bar{r}_M,t) = \\nonumber \\\\\n&=&\\left\\{\\;\\sum_{A=1}^{M}\\left(\\frac{1}{2m_A}\\Delta_A +V_A(\\bar{{r}}_A)\\right)\n+\\frac{1}{2}\\sum_{A\\ne B}V_{AB}(\\bar{{r}}_A,\\bar{{r}}_B)\\right. +\\nonumber \\\\\n&~~&~~~~+\\left[\\;\\sum_{A=1}^{M}\\left(\\left.\\frac{\\partial V_A(\\bar{\\hat{r}}_A)}{\\partial \\hat{x}_{1A}}\\cdot \\frac{i}{2 }\\partial_{2A}\\right.\\right.\n\\left.\\left.-\\frac{\\partial V_A(\\bar{\\hat{r}}_A)}{\\partial \\hat{x}_{2A}}\\cdot \\frac{i}{2 }\\partial_{1A}\\right.\\right)\\right.+\\label{gromek5}\\\\\n&+&\\frac{1}{2}\\sum_{A\\ne B}\\left.\\left.\\left(\\frac{\\partial V_{AB}(\\bar{\\hat{r}}_A,\\bar{\\hat{r}}_B)}{\\partial \\hat{x}_{1A}}\\cdot \\frac{i}{2 }\\partial_{2A}\\right.\n- \\frac{\\partial V_{AB}(\\bar{\\hat{r}}_A,\\bar{\\hat{r}}_B)}{\\partial \\hat{x}_{2A}}\\cdot \\frac{i}{2}\\partial_{1A}\\right.\\right. + \\nonumber\n\\end{eqnarray}\n\\begin{eqnarray}\n&+&\\left.\\left.\\left.\\left.\\left.\\frac{\\partial V_{AB}(\\bar{\\hat{r}}_A,\\bar{\\hat{r}}_B)}{\\partial \\hat{x}_{1B}}\\cdot \\frac{i}{2}\\partial_{2B}\\right.\n -\\frac{\\partial V_{AB}(\\bar{\\hat{r}}_A,\\bar{\\hat{r}}_B)}{\\partial \\hat{x}_{2B}}\\cdot \\frac{i}{2}\\partial_{1B}\\right.\\right)\\;\\right]\\right|_{f_{\\kappa_a}(t)=0}\\cdot f_{\\kappa_a}(t) +\\nonumber \\\\\n&+& \\left.{\\cal O}(\\kappa_a) \\;\\right\\}\\psi(\\bar{r}_1, \\ldots ,\\bar{r}_M,t)\\;. \\nonumber\n\\end{eqnarray}\nConsequently, we see that space-time noncommutativity (\\ref{spaces}) generates in the hamiltonian (\\ref{gromek1}) two types of additional dynamical terms. First of them arises from\nthe single-particle potential $V_A(\\bar{\\hat{r}}_A)$ while the second one corresponds to the correlations $V_{AB}(\\bar{\\hat{r}}_A,\\bar{\\hat{r}}_B)$. Of course, for deformation parameters $\\kappa_a$ approaching zero all additional \"potential\" terms disappear.\n\nLet us now turn to the mentioned in pervious section the system of M particles moving \"in\" and interacting \"by\" the Coulomb potential. Then, in accordance with formulas (\\ref{gromek4}) and (\\ref{gromek5}) the corresponding hamiltonian function as well as the corresponding Schroedinger equation take the form\n\\begin{eqnarray}\nH(\\bar{p}_1, \\ldots ,\\bar{p}_M;\\bar{r}_1, \\ldots ,\\bar{r}_M,t) &=& \\sum_{A=1}^{M}\\left(\\frac{\\bar{{p}}_A^2}{2m_A} -\\frac{Ze^2}{|{\\bar{{r}}_A}|}\\right)\n+\\frac{1}{2}\\sum_{A\\ne B}\\frac{e^2}{{|\\bar{{r}}_A - \\bar{{r}}_B|}} + \\nonumber\\\\\n&~~&~~~~- \\sum_{A=1}^{M} \\frac{Ze^2{f_{\\kappa_a}(t)}}{2\\hbar{|\\bar{r}_A|^3}}\\cdot L_{3A} +\n\\label{gromek6}\\\\\n&+&\\frac{1}{2}\\sum_{A\\ne B} \\frac{e^2{f_{\\kappa_a}(t)}}{2\\hbar{|\\bar{r}_A-\\bar{r}_B|^3}} \\cdot \\left(L_{3B}+L_{3A}\\right) + \\nonumber\\\\\n&-&\\frac{1}{2}\\sum_{A\\ne B} \\frac{e^2{f_{\\kappa_a}(t)}}{2\\hbar{|\\bar{r}_A-\\bar{r}_B|^3}} \\cdot \\left(G_{AB}+G_{BA}\\right) + {\\cal O}(\\kappa_a)\\;,\\nonumber\n\\end{eqnarray}\nand\n\\begin{eqnarray}\ni\\frac{\\partial}{\\partial t} \\psi(\\bar{r}_1, \\ldots ,\\bar{r}_M,t) &=& \\left[\\;\\sum_{A=1}^{M}\\left(\\frac{1}{2m_A}\\Delta_A -\\frac{Ze^2}{|{\\bar{{r}}_A}|}\\right)\n+\\frac{1}{2}\\sum_{A\\ne B}\\frac{e^2}{{|\\bar{{r}}_A - \\bar{{r}}_B|}} + \\right.\\nonumber\\\\\n&~~&~~~~ -\\sum_{A=1}^{M} \\frac{Ze^2{f_{\\kappa_a}(t)}}{2\\hbar{|\\bar{r}_A|^3}}\\cdot L_{3A} +\n\\label{gromek7}\\\\\n&+&\\left.\\frac{1}{2}\\sum_{A\\ne B} \\frac{e^2{f_{\\kappa_a}(t)}}{2\\hbar{|\\bar{r}_A-\\bar{r}_B|^3}}\\cdot \\left(L_{3B}+L_{3A}\\right) +\\;\\right. \\nonumber \\\\\n&-&\\left.\\frac{1}{2}\\sum_{A\\ne B} \\frac{e^2{f_{\\kappa_a}(t)}}{2\\hbar{|\\bar{r}_A-\\bar{r}_B|^3}}\\cdot \\left(G_{AB}+G_{BA}\\right) + {\\cal O}(\\kappa_a)\\;\\right]\\times \\nonumber \\\\\n&\\times& \\psi(\\bar{r}_1, \\ldots ,\\bar{r}_M,t)\\;.\\nonumber\n\\end{eqnarray}\nrespectively, with $L_{3A} =x_{1A}p_{2A} - x_{2A}p_{1A}$ and $G_{AB} = x_{1B}p_{2A} - x_{2B}p_{1A}$. Particulary, in the case of single particle, for canonical deformation $f_{\\kappa_a}(t) = \\kappa_a$ we reproduce\nthe noncommutative model of hydrogen atom proposed in \\cite{qm1} and \\cite{qm2}\n\\begin{eqnarray}\nH(\\bar{p},\\bar{x}) &=& \\frac{\\bar{p}^2}{2m} -\\frac{Ze^2}{{|\\bar{r}|}} - \\frac{Ze^2{\\kappa_a}}{2\\hbar{|\\bar{r}|^3}}\\cdot L_3 + \\mathcal{O}\n(\\kappa_a) \\;,\\label{gromek8}\n\\end{eqnarray}\nwhile for more complicated (time-dependent) functions $f_{\\kappa_a}(t)$, we get the one-particle system described by\n\\begin{eqnarray}\nH(\\bar{p},\\bar{x},t) &=& \\frac{\\bar{p}^2}{2m} -\\frac{Ze^2}{{|\\bar{r}|}} - \\frac{Ze^2f_{\\kappa_a}(t)}{2\\hbar{|\\bar{r}|^3}}\\cdot L_3 + \\mathcal{O}\n(\\kappa_a) \\;. \\label{gromek9}\n\\end{eqnarray}\nIt is well-known, that the solution of the corresponding (associated with (\\ref{gromek9})) Schroedinger equation can be found with use of time-dependent perturbation theory \\cite{24}. It looks as follows\n\\begin{eqnarray}\n {\\psi}(\\bar{r},t) = \\sum_{n=0}^{\\infty}\\sum_{l=0}^{n-1}\\sum_{m=-l}^{l} c_{nlm}(t){\\rm e}^{iE_n(t-t_0)}\\psi_{nlm}(\\bar{x})\\;, \\label{gromek10}\n\\end{eqnarray}\nwhere symbols $E_n$ and $\\psi_{nlm}$ denote eigenvalues and eigenfunctions for hydrogen atom, while coefficients $c_{nlm}(t)$ are defined as the solutions of the following differential equations\n\\begin{eqnarray}\n\\frac{dc_{nlm}(t)}{dt} &=& -\\frac{1}{i\\hbar} \\sum_{n'=0}^{\\infty}\\sum_{l'=0}^{n-1}\\sum_{m'=-l}^{l} \\left(\\psi_{nlm}(\\bar{r}),\\frac{Ze^2f_{\\kappa_a}(t)}{2\\hbar{|\\bar{r}|^3}}\\cdot L_3\\psi_{n'l'm'}(\\bar{r})\\right)c_{n'l'm'}(t_0)\\;\\cdot \\cr\n&\\cdot&{\\rm e}^{i\\omega_{nn'}(t-t_0)}\\;\\;\\;;\\;\\;\\;\\omega_{nn'} \\;=\\; \\frac{1}{\\hbar}(E_n-E_{n'})\\;.\\label{gromek11}\n\\end{eqnarray}\nHence, in accordance with prescription (\\ref{grom3}), the solution of multiparticle wave equation (\\ref{gromek7}) with neglected correlation potential $V_{AB}(|\\bar{r}_A-\\bar{r}_B|)$ and vanishing $\\mathcal{O}\n(\\kappa_a)$-terms takes the form\n\\begin{eqnarray}\n \\psi(\\bar{r}_1, \\ldots ,\\bar{r}_M,t) = {\\psi}_1(\\bar{r}_1,t) \\cdots {\\psi}_M(\\bar{r}_M,t)\\;,\\label{gromek3}\n\\end{eqnarray}\nwith one-particle functions ${\\psi}_A(\\bar{r}_A,t)$ given by (\\ref{gromek10}).\n\nFinally, it should be noted that the average values of energy operators (\\ref{gromek2}), (\\ref{gromek4}) and (\\ref{gromek6}) can be found with use of the formula (\\ref{grom8}).\n\n\\section{Final remarks}\n\nIn this article we construct the quantum model of M\nnonrelativistic particles moving in noncommutative space-time\n(\\ref{spaces300}). The corresponding Schroedinger equation for arbitrary stationary\npotential is provided and, in\nparticular, there is analyzed the distinguished example of such\nsystem - the set of M particles moving \"in\" and interacting \"by\" the Coulomb potential. It\nshould be noted, however, that by analogy to the investigations performed in article \\cite{qm1}, one can\nask about more physical features (such as for example the energy spectrum or the Lamb shift) of the model defined by Hamiltonian (\\ref{gromek6}).\nBesides, it should be added, that the presented considerations give a starting\npoint for the construction of Dirac quantum mechanics for\nmany particles defined on the relativistic counterpart of modified space-time (\\ref{canamm}).\n The studies in these directions already started and\nare in progress.\n\n\n\n\n\n\n\\section*{Acknowledgments}\nThe author would like to thank J. Lukierski\nfor valuable discussions. This paper has been financially supported by Polish\nNCN grant No 2011\/01\/B\/ST2\/03354.\n\n\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\n\\section{Introduction}\n\n\\begin{figure}[h!]\n\\centering\n\\includegraphics[width=\\columnwidth, angle=0]{figures\/fig_Intro_DCA.pdf}\n\\caption{\\textbf{An Illustration of the Dynamic Context Augmentation process.} A traditional global EL model jointly optimizes the linking configuration after iterative calculations over all mentions, which is computationally expensive. In contrast, the DCA process only requires one pass of the document to accumulate knowledge from previously linked mentions to enhance fast future inference.\n}\n\n\\label{fig:DCA}\n\\end{figure}\n\nLinking mentions of entities in text to knowledge base entries (i.e., entity linking, or EL) is critical to understanding and structuring text corpora. In general, EL is approached by first obtaining candidate entities for each mention, and then identifying the true referent among the candidate entities. \nPrior distribution and local contexts, either in the form of hand-crafted features~\\cite{ratinov2011local,shen2015entity} or dense embeddings~\\cite{he2013learning,nguyen2016joint,francis2016capturing}, play key roles in distinguishing different candidates. However, in many cases, local features can be too sparse to provide sufficient information for disambiguation.\n\nTo alleviate this problem, various collective EL models have been proposed to globally optimize the inter-entity coherence between mentions in the same document\n~\\cite{hoffart2011robust,cheng2013relational,nguyen2014aida,alhelbawy2014graph,pershina2015personalized}.\nDespite of their success, existing global EL models try to optimize the entire linking configuration of all mentions, with extra assumptions of either all-mention coherence or pairwise coherence \\cite{phan2018pair}.\nSuch assumptions are against human intuitions, as they imply that no inference can be made until all mentions in a document have been observed.\nAlso, there usually exists a trade-off between accuracy and efficiency: state-of-the-art collective\/global models suffer from high time complexity.\nFrom the perspective of computational efficiency, optimal global configuration inference is NP-hard.\nApproximation methods, such as loopy belief propagation \\cite{ganea2017deep} or iterative substitutions \\cite{shen2015entity}, are still computationally expensive due to the huge hypothesis space, and thus can hardly be scaled to handle large corpus.\nMany previous works have discussed the urgent needs of more efficient linking system for production, both in time complexity~\\cite{hughes2014trading} and memory consumption~\\cite{blanco2015fast}. \n\n\nIn this paper, we propose a simple yet effective \\textbf{D}ynamic \\textbf{C}ontext \\textbf{A}ugmentation (DCA) process to incorporate global signal for EL. \nAs Figure \\ref{fig:DCA} shows, in contrast to traditional global models, DCA only requires one pass through all mentions to achieve comparable linking accuracy.\nThe basic idea is to accumulate knowledge from previously linked entities as dynamic context to enhance later decisions.\nSuch knowledge come from not only the inherent properties (e.g., description, attributes) of previously linked entities, but also from their closely related entities, which empower the model with important associative abilities. \nIn real scenarios, some previously linked entities may be irrelevant to the current mention. Some falsely linked entities may even introduce noise.\nTo alleviate error propagation, we further explore two strategies: (1) soft\/hard attention mechanisms that favour the most relevant entities; (2) a reinforcement learning-based ranking model, which proves to be effective as reported in other information extraction tasks.\n\n\\smallskip\n\\noindent\n\\textbf{Contributions.} \nThe DCA model forms a new linking strategy from the perspective of data augmentation and thus can serve as a plug-and-enhance module of existing linking models. The major contributions of this work are as follows:\n(1) DCA can introduce topical coherence into local linking models without reshaping their original designs or structures; (2) Comparing to global EL models, DCA only requires one pass through all mentions, yielding better efficiency in both training and inference; (3) Extensive experiments show the effectiveness of our model under different learning settings, base models, decision orders and attention mechanisms.\n\\section{Background}\n\n\\subsection{Problem Definition}\nGiven a set of entity mentions $\\mathcal{M} = \\{m_1, ..., m_T\\}$ in corpus $\\mathcal{D}$, Entity Linking aims to link each mention $m_t$ to its corresponding gold entity $e_t^*$. Such a process is usually divided into two steps:\n\\emph{Candidate generation} first collects a set of possible (candidate) entities $\\mathcal{E}_t = \\{e_t^1, ..., e_t^{|\\mathcal{E}_t|}\\}$ for $m_t$;\n\\emph{Candidate ranking} is then applied to rank all candidates by likelihood. The linking system selects the top ranked candidate as the predicted entity $\\hat{e}_t$.\nThe key challenge is to capture high-quality features of each entity mention for accurate entity prediction, especially when local contexts are too sparse to disambiguate all candidates.\n\nWe build our DCA model based on two existing local EL models. In this section, we first introduce the architecture of the base models, then present the proposed DCA model under the standard supervised learning framework. Since the DCA process can be naturally formed as a sequential decision problem, we also explore its effectiveness under the Reinforcement Learning framework. Detailed performance comparison and ablation studies are reported in Section \\ref{sec:result}.\n\\subsection{Local Base Models for Entity Linking}\n\\label{sec:base}\nWe apply the DCA process in two popular local models with different styles: the first is a neural attention model named ETHZ-Attn~\\cite{ganea2017deep}, the other is the Berkeley-CNN~\\cite{francis2016capturing} model which is made up of multiple convolutional neural networks (CNN).\n\n\\medskip\n\\noindent\\textbf{ETHZ-Attn.}\nFor each mention $m_t$ and a candidate $e^j_t \\in \\mathcal{E}_t$, three local features are considered: \n(1) \\emph{Mention-entity Prior} $\\hat{P}(e_t^j | m_t)$ is the empirical distribution estimated from massive corpus (e.g. Wikipedia); \n(2) \\emph{Context Similarity} $\\Psi_C(m_t, e^j_t)$ measures the textual similarity between $e^j_t$ and the local context of $m_t$;\n(3) \\emph{Type Similarity} $\\Psi_T(m_t, e^j_t)$ considers the similarity between the type of $e_t^j$ and contexts around $m_t$.\n$\\hat{P}(e_t^j | m_t)$ and $\\Psi_C(m_t, e^j_t)$ are calculated in the same way as \\cite{ganea2017deep}.\nFor $\\Psi_T(m_t, e^j_t)$, we first train a typing system proposed by \\cite{xu2018neural} on AIDA-train dataset, yielding 95\\% accuracy on AIDA-A dataset. In the testing phase, the typing system predicts the probability distribution over all types (PER, GPE, ORG and UNK) for $m_t$, and outputs $\\Psi_T(m_t, e^j_t)$ for each candidate accordingly. All local features are integrated by a two-layer feedforward neural network with 100 hidden units, as described in \\cite{ganea2017deep}.\n\n\\medskip\n\\noindent\\textbf{Berkeley-CNN.} The only difference between \\textbf{ETHZ-Attn} and \\textbf{Berkeley-CNN} is that, this model utilizes CNNs at different granularities to capture \\emph{context similarity} $\\Psi_C(m_t, e^j_t)$ between a mention's context and its target candidate entities.\n\\section{Dynamic Context Augmentation}\n\\label{sec:DCA}\n\\label{sec:dca}\nAs Figure \\ref{fig:DCA} demonstrates, the basic idea of DCA is to accumulate knowledge from previously linked entities as dynamic context to enhance later decisions.\nFormally, denote the list of previously linked entities as $S_t = \\{\\hat{e}_1, ..., \\hat{e}_t\\}$, where each $\\hat{e}_i$ is represented as an embedding vector.\nThe augmented context can be represented by accumulated features of all previous entities and their neighbors (e.g. by averaging their embeddings, in the simplest way).\nIn actual scenarios, some entities in $S_t$ are irrelevant, if not harmful, to the linking result of $m_{t+1}$. \nTo highlight the importance of relevant entities while filtering noises, we also try to apply a neural attention mechanism on dynamic contexts (Figure \\ref{fig:attention}).\nFor mention $m_{t+1}$, candidates that are more coherent with $S_t$ are preferred.\nMore specifically, we calculate the relevance score for each $\\hat{e}_i \\in S_t$ as\n\\begin{equation}\n u(\\hat{e}_i) = \\max_{e^j_{t+1} \\in \\mathcal{E}_{t+1}} {e_{t+1}^j}^\\top \\cdot A \\cdot \\hat{e}_i,\n\\end{equation}\nwhere $A$ is a parameterized diagonal matrix. Top $K$ entities in $S_t$ are left to form dynamic context while the others are pruned. The relevance scores are transformed to attention weights with\n\\begin{equation}\n a(\\hat{e}_i) = \\frac{\\exp[u(\\hat{e}_i)]}{\\sum_{\\hat{e}_j \\in S_t} \\exp[u(\\hat{e}_j)]}~.\n\\end{equation}\nThus, we can define a weighted coherence score between $e_{t+1}^j \\in \\mathcal{E}_{t+1}$ and $S_t$ as\n\\begin{equation}\n \\Phi(e_{t+1}^j, S_t) = \\sum_{\\hat{e}_i \\in S_t} a(\\hat{e}_i) \\cdot {e^j_{t+1}}^\\top \\cdot R \\cdot \\hat{e}_i,\n\\end{equation}\nwhere $R$ is a learnable diagonal matrix. Such a coherence score will be later incorporated in the final representation of $e_{t+1}^j$.\n\n\\begin{figure}[]\n\\centering\n\\includegraphics[width=0.45\\textwidth, angle=0]{figures\/fig_method_attention.pdf}\n\\caption{Neural attention mechanism on the dynamic context. \nThe soft attention module assigns higher weights to entities that are more relevant to the target mention.\nThe hard attention module only considers top $K$ entities as dynamic contexts.\n}\n\n\\label{fig:attention}\n\\end{figure}\n\nTo empower the linking model with associative ability, aside from previously linked entities, we also incorporate entities that are closely associated with entities in $S_t$. Specifically, for each $\\hat{e}_i \\in S_t$, we collect its neighborhood $\\mathcal{N}(\\hat{e}_i)$ consisting of Wikipedia entities that have inlinks pointing to $\\hat{e}_i$. Denoting $S'_t$ as the union of $\\{\\mathcal{N}(\\hat{e}_i) | \\hat{e}_i \\in S_t\\}$, we define a similar weighted coherence score between $e_{t+1}^j \\in \\mathcal{E}_{t+1}$ and $S'_t$ as\n\\begin{equation}\n \\Phi'(e_{t+1}^j, S'_t) = \\sum_{\\hat{e}_i \\in S'_t} a'(\\hat{e}_i) \\cdot {e^j_{t+1}}^\\top \\cdot R' \\cdot \\hat{e}_i,\n\\end{equation}\nwhere $a'$ is defined similarly to $a$, and $R'$ is a learnable diagonal matrix. \nThe final representation $\\vec{h_0}(m_{t+1}, e^j_{t+1})$ is the concatenation of $\\Phi(e_{t+1}^j, S_t)$, $\\Phi'(e_{t+1}^j, S'_t)$, $\\Psi_T(m_t, e_{t+1}^j)$, $\\Psi_C(m_t, e^j_{t+1})$ and $\\log\\hat{P}(e^j_{t+1} | m_{t+1})$. \n\\section{Model Learning for DCA}\nIn this section, we explore different learning strategies for the linking model. Specifically, we present a Supervised Learning model, where the model is given all gold entities for training, and a Reinforcement Learning model, where the model explores possible linking results by itself in a long-term planning task.\n\n\\subsection{Supervised Ranking Method}\nGiven a mention-candidate pair $(m_t, e_t^j)$, the ranking model parameterized by $\\theta$ accepts the feature vector $\\vec{h_0}(m_{t}, e^j_{t})$ as input, and outputs the probability $P_\\theta(e^j_t | m_t)$.\nIn this work, we use a two-layer feedforward neural network as the ranking model. We apply the max-margin loss as\n\\begin{equation*}\n\\begin{split}\n \\mathcal{L_\\theta} &= \\sum_{D \\in \\mathcal{D}} \\sum_{m_t \\in D} \\sum_{e_t^j \\in \\mathcal{E}_t} g_\\theta(e_t^j, m_t),\\\\\n g_\\theta(e_t^j, m_t) &= \\max(0, \\gamma - P_\\theta(e^*_t | m_t) + P_\\theta(e^j_t | m_t)).\n\\end{split} \n\\end{equation*}\nThe learning process is to estimate the optimal parameter such that $\\theta^* = \\arg\\min_\\theta \\mathcal{L}_\\theta$.\n\nNote that, in the Supervised Ranking model, dynamic contexts are provided by previous gold entities: $S_t = \\{e_1^*, ..., e_t^*\\}$, $S'_t = \\bigcup\\limits_{i=1}^{t} \\mathcal{N}(e_i^*)$. In the testing phase, however, we do not have access to gold entities. Wrongly linked entities can introduce noisy contexts to future linking steps. To consider such long-term influences, we introduce an alternative Reinforcement Learning model in the next section.\n\\subsection{Reinforcement Learning Method}\nNaturally, the usage of dynamic context augmentation forms a sequential decision problem, as each linking step depends on previous linking decisions.\nCorrect linking results provide valuable information for future decisions, while previous mistakes can lead to error accumulation. \nReinforcement Learning (RL) algorithms have proven to be able to alleviate such accumulated noises in the decision sequence in many recent works \\cite{narasimhan2016improving, feng2018relation}.\nIn this work, we propose an RL ranking model for DCA-enhanced entity linking.\n\n\\vspace{0.15cm}\n\\noindent\\textbf{Agent}: The Agent is a candidate ranking model that has a similar architecture to~\\cite{clark2016deep}, aiming to output the action preference $H_\\theta(S_{t-1}, S'_{t-1}, A_t^j)$ of each linking action $A_t^j = (m_t \\rightarrow e_t^j)$. It is a 2-layer feedforward neural network with following components:\n\n\\vspace{0.15cm}\n\\noindent{\\bf Input Layer}: For each $( m_t, e_t^j )$ pair, DCA-RL extracts context-dependent features from $S_{t-1}, S'_{t-1}$, and concatenates them with other context-independent features to produce an $I$-dimensional input vector $\\vec{h_0}(m_{t}, e^j_{t})$.\n\n\\vspace{0.15cm}\n\\noindent{\\bf Hidden Layers}: Let $Drop(\\vec x)$ be the dropout operation~\\cite{srivastava2014dropout} and $ReLU(\\vec x)$ be the rectifier nonlinearity \\cite{nair2010rectified}. So the output $\\vec {h}_1$ of the hidden layer is defined as:\n\\begin{equation}\n\\vec {h}_1 = Drop(ReLU(\\vec{W}_1 \\cdot \\vec{h}_0 + \\vec{b}_1)),\n\\end{equation}\nwhere $\\vec{W}_1$ is a $H_1 \\times I$ weight matrix.\n\n\\vspace{0.15cm}\n\\noindent{\\bf Output Layers}: This scoring layer is also fully connected layer of size 1.\n\\begin{equation}\n\\vec {h}_2 = \\vec{W}_2 \\cdot \\vec{h}_1 + \\vec{b}_2,\n\\end{equation}\nwhere $\\vec{W}_2$ is a $1 \\times H_1$ weight matrix. In the end, all action preference would be normalized together using an exponential softmax distribution, getting their action probabilities $\\pi_\\theta(A_t^j|S_{t-1},S'_{t-1})$:\n\n\nAccording to policy approximating methods, the best approximate policy may be stochastic. So we randomly sample the actions based on the softmax distribution during the training time, whereas deliberately select the actions with the highest ranking score at the test time.\n\n\\vspace{0.15cm}\n\\noindent\n\\textbf{Reward.}\nThe reward signals are quite sparse in our framework. For each trajectory, the Agent can only receive a reward signal after it finishes all the linking actions in a given document. Therefore the immediate reward of action $t$, $R_t = 0$, where $0 \\leq t 0$ such that for any $\\theta_1,\\theta_2\\in V$,\n \\begin{equation*}\n \\vert F(\\theta_1) - F(\\theta_2)\\vert \\leq C \\left\\|\\theta_1 - \\theta_2\\right\\|,\n \\end{equation*}\n where $\\left\\| \\cdot \\right\\|$ is any norm on $\\mathbb{R}^P$. A function $F:\\mathbb{R}^P\\to \\mathbb{R}^D$ is locally Lipschitz continuous if each of its coordinates is locally Lipschitz continuous. The variable $\\theta \\in \\mathbb{R}^P$ is the parameter of the model ($ P $ can be very large), while $ x\\in \\mathbb{R}^M $ and $y\\in \\mathbb{R}^D$ represent input and output data. For instance, the vector $ x $ may embody an image while $y$ is a label explaining its content. Consider further a data set of $ N$ samples $ (x_n,y_n)_{n=1,\\ldots,N} $. Training the network amounts to finding a value of the parameter $ \\theta $ such that, for each input data $ x_n $ of the data set, the output $ f(x_n,\\theta)$ of the model predicts the real value $ y_n $ with good accuracy. To do so, we follow the traditional approach of minimizing an empirical risk loss function, \\begin{equation}\\label{eq::loss}\n\t\\mathbb{R}^P\\ni\\theta \\mapsto \\mathcal{J}(\\theta)=\\sum_{n=1}^N l(f(x_n,\\theta),y_n),\n\t\\end{equation} where $l:\\mathbb{R}^D\\times \\mathbb{R}^D\\to \\mathbb{R}$ is a locally Lipschitz continuous dissimilarity measure. \\new{In the sequel, for $n\\geq1$, we will sometimes denote by $\\mathcal{J}_n$ the $n$-th term of the sum: $\\mathcal{J}_n(\\theta) \\triangleq l(f(x_n,\\theta),y_n)$, so that $\\mathcal{J}=\\sum_{n=1}^N \\mathcal{J}_n$. Despite the nonsmoothness and the nonconvexity of typical DL loss functions, they generally possess a very strong property sometimes called tameness. We now introduce this notion which is essential to obtain the convergence results of Section~\\ref{sec:proof}.}\n\n\t\\subsection{Neural Networks and Tameness in a Nutshell}\\label{sec::favstruct}\n\nTameness refers to an ubiquitous geometrical property of loss functions and constraints encompassing most finite dimensional optimization problems met in practice. Prominent classes of tame objects are piecewise-linear or piecewise-polynomial objects (with finitely many pieces), and more generally, semi-algebraic objects. However, the notion is much more general, as we intend to convey below. The formal definition is given at the end of this subsection (Definition~\\ref{DEF}).\n\nInformally, sets or functions are called tame when they can be described by a finite number of basic formulas, inequalities, or Boolean operations involving standard functions such as polynomial, exponential, or max functions. We refer to \\cite{attouch} for illustrations, recipes and examples within a general optimization setting or \\cite{davis2018stochastic} for illustrations in the context of neural networks. The reader is referred to \\cite{van1998tame,coste2000introduction,shiota} for foundational material. To apprehend the strength behind tameness it is convenient to remember that it models nonsmoothness by confining the study to sets and functions which are union of smooth pieces. This is the so-called {\\em stratification} property of tame sets and functions. It was this property which motivated the term of {\\em tame topology},\\footnote{``{\\em La topologie mod\\'er\\'ee}'' wished for by Grothendieck.} see \\cite{van1998tame}. In a nonconvex optimization settings, the stratification property is crucial to generalize qualitative algorithmic results to nonsmooth objects.\n\n{\\em All finite dimensional DL optimization models we are aware of yield tame loss functions $\\mathcal{J}$}. To understand this assertion and illustrate the wide scope of tameness assumptions, let us provide concrete examples (see also \\cite{davis2018stochastic}).\nAssume that the DNNs under consideration are built from the following traditional components:\n\\begin{itemize}\n \\renewcommand\\labelitemi{--}\n \\item the network architecture describing $ f $ is fixed with an arbitrary number of layers of arbitrary dimensions and arbitrary Directed Acyclic Graph (DAG) representing computation,\n\\item the activation functions are among classical ones: ReLU, sigmoid, SQNL, RReLU, tanh, APL, soft plus, soft clipping, and many others including multivariate activations (norm, sorting), or activations defined piecewise with polynomials, exponential and logarithm,\n\\item the dissimilarity function $l$ is a standard loss such as $\\ell_p$ norms, logistic loss or cross-entropy or more generally a function defined piecewise using polynomials, exponentials and logarithms,\n\\end{itemize}\nthen one can easily show, by elementary quantifier elimination arguments (property (iii) below), that the corresponding loss, $\\mathcal{J}$, is tame.\n\\medskip\n\n\nFor the sake of completeness, we provide below the formal definition of tameness and o-minimality. \n\\begin{definition}\\label{DEF}\n{\\rm {\\bf [o-minimal structure] }}{\\rm\n\\cite[Definition\\,1.5]{coste2000introduction}} \\label{Domin}\\rm{An {\\it\no-minimal } structure on $(\\mathbb{R},+,.)$ is a countable collection of sets ${\\cal O}=\\{\\mathcal{O}_{q}\\}_{q\\geq 1}$ where each $\\mathcal{O}_{q}$ is itself a collection of subsets of $\\mathbb{R}^q$, called {\\em definable} subsets. They must have the following properties, for each $q\\geq 1$:}\n\\begin{enumerate}\\itemsep=1mm\n\\item[(i)] (Boolean properties) $\\mathcal{O}_{q}$ contains the empty set, is stable by finite union, finite intersection and complementation;\n\\item[(ii)] (Lifting property) \n{\\rm if $A$ belongs to $\\mathcal{O}_{q}$, then $A\\times\\mathbb{R}$ and\n$\\mathbb{R}\\times A$ belong to $\\mathcal{O}_{q+1}$.}\n\\item[(iii)] (Projection or quantifier elimination property) \n{\\rm if $\\Pi:\\mathbb{R}^{q+1}\\rightarrow\\mathbb{R}^q$ is the canonical\nprojection onto $\\mathbb{R}^q$ then for any $A$ in $\\mathcal{O} _{q+1}$,\nthe set $\\Pi(A)$ belongs to $\\mathcal{O}_{q}$.}\n\\item[(iv)] (Semi-algebraicity)\n{\\rm $\\mathcal{O}_{q}$ contains the family of algebraic\nsubsets of $\\mathbb{R}^q$, that is, every set of the form\n\\[\n\\{\\theta\\in\\mathbb{R}^q\\mid\\zeta(\\theta)=0\\},\n\\]\nwhere $\\zeta:\\mathbb{R}^q\\rightarrow\\mathbb{R}$ is a polynomial function.}\n\\item[(v)] (Minimality property), \n{\\rm the elements of $\\mathcal{O}_{1}$ are exactly the finite\nunions of intervals and points. }\n\\end{enumerate}\n\\end{definition}\n\n\n\\noindent A mapping $F:S\\subset \\mathbb{R}^m\\rightarrow \\mathbb{R}^q$ is said to be\n{\\em definable in ${\\cal O}$} if its graph is definable in $\\cal O$ as a subset of~$\\mathbb{R}^m\\times\\mathbb{R}^q$. For illustration of o-minimality in the context of optimization one is referred to \\cite{attouch,davis2018stochastic}.\n\n\n\n\\begin{center}\n\\fbox{\\begin{minipage}{14cm} From now on we fix an o-minimal structure $\\mathcal{O}$ and a set or a mapping definable in $\\cal O$ will be called {\\em tame}.\n\\end{minipage}}\n\\end{center}\n\n\\subsection{From DIN to INDIAN}\\label{sec::optpart}\n \n \n We describe in this section the construction of our proposed algorithm INDIAN from the discretization of the second-order ODE~\\eqref{eq:physicalIntuitionSmooth}.\n \n \n \n \n\t\t\\subsubsection{Handling Nonsmoothness and Nonconvexity}\\label{sec::nonsmoothnonconv}\n\n\t\t We first show how the formalism offered by Clarke's subdifferential can be applied to generalize \\eqref{eq:physicalIntuitionSmooth} to the nonsmooth nonconvex setting. Recall that the dynamical system \\eqref{eq:physicalIntuitionSmooth} is described by,\n\t\t %\n\t\t\\begin{equation}\\label{eq::secondorderDIN}\n\t\t\\ddot{\\theta}(t)+\\alpha\\dot{\\theta}(t) + \\beta \\nabla^2 \\mathcal{J}(\\theta(t))\\dot{\\theta}(t) +\\nabla \\mathcal{J}(\\theta(t))=0,\n\t\t\\end{equation}\n\t\t%\n\t\twhere $\\mathcal{J}$ is a twice-differentiable potential, $\\alpha>0$, $\\beta>0$ are two hyper-parameters and $\\theta: \\mathbb{R}_+ \\rightarrow \\mathbb{R}^P$. We cannot exploit \\eqref{eq::secondorderDIN} directly since in most DL applications $\\mathcal{J}$ is not twice differentiable (and even not differentiable at all). We first overcome the explicit use of the Hessian matrix $\\nabla^2 \\mathcal{J}$ \\new{by introducing an auxiliary variable $\\psi: \\mathbb{R}_+ \\rightarrow \\mathbb{R}^P$ like in \\cite{alvarez2002second}. Consider the following dynamical system (defined for $\\mathcal{J}$ merely differentiable),}\n\t\t\\begin{equation}\\label{eq::contdinSmooth}\n\t\t\\begin{cases}\n\t\t\\dot{\\theta}(t) + \\beta \\nabla \\mathcal{J}(\\theta(t)) &+(\\alpha -\\frac{1}{\\beta})\\theta(t) + \\frac{1}{\\beta} \\psi(t) = 0\\\\\n\t\t\\dot{\\psi}(t) &+(\\alpha -\\frac{1}{\\beta})\\theta(t) + \\frac{1}{\\beta} \\psi(t) = 0 \\end{cases} \\mbox{,\\quad for a.e. $t\\in (0,+\\infty)$}.\n\t\t\\end{equation}\n\t\t\\new{As explained in \\cite{alvarez2002second}, \\eqref{eq::secondorderDIN} is equivalent to \\eqref{eq::contdinSmooth} when $\\mathcal{J}$ is twice differentiable. Indeed, one can rewrite \\eqref{eq::secondorderDIN} into \\eqref{eq::contdinSmooth} by introducing $\\psi = -\\beta\\dot{\\theta}-\\beta^2\\nabla\\mathcal{J}(\\theta)-(\\alpha\\beta-1)\\theta$. Conversely, one can substitute the first line of \\eqref{eq::contdinSmooth} into the second one to retrieve \\eqref{eq::secondorderDIN}.} Note however that \\eqref{eq::contdinSmooth} does not require the existence of second-order derivatives.\n\t\t\n\t\tLet us now introduce a new {\\em nonconvex nondifferentiable} version of \\eqref{eq::contdinSmooth}.\n\t\tBy Rademacher's theorem, locally Lipschitz continuous functions $\\mathcal{J}:\\mathbb{R}^P\\to \\mathbb{R}$ are differentiable almost everywhere. Denote by $\\mathsf{R}$ the set of points where $\\mathcal{J}$ is differentiable. Then, $\\mathbb{R}^P\\setminus \\mathsf{R}$ has zero Lebesgue measure. It follows that for any $\\theta^\\star\\in\\mathbb{R}^P\\setminus \\mathsf{R}$, there exists a sequence of points in $\\mathsf{R}$ whose limit is this $\\theta^\\star$. This motivates the introduction of the subdifferential due to \\cite{clarke1990optimization}, defined next.\n\t\t\\begin{definition}[Clarke subdifferential of Lipschitz functions] \\label{def:clarke}\n\t\t\tFor any locally Lipschitz continuous function $F: \\mathbb{R}^P\\to \\mathbb{R}$, the Clarke subdifferential of $F$ at $\\theta\\in\\mathbb{R}^P$, denoted $\\partial F(\\theta)$, is the set defined by,\n\t\t\t\\begin{equation}\n\t\t\t\t\\partial F(\\theta) = \\mathrm{conv}\\left\\{ v\\in\\mathbb{R}^P \\mid \\exists (\\theta_k)_{k\\in\\mathbb{N}}\\in \\mathsf{R}^\\mathbb{N},\\text{ such that } \\theta_k \\xrightarrow[k\\to\\infty]{}\\theta \\text{ and } \\nabla F(\\theta_k) \\xrightarrow[k\\to\\infty]{} v \\right\\},\n\t\t\t\\end{equation}\n\t\t\twhere $\\mathrm{conv}$ denotes the convex hull operator. \\new{The elements of the Clarke subdifferential are called Clarke subgradients.}\n\t\t\\end{definition}\n\t\tThe Clarke subdifferential is a nonempty compact convex set. \\new{It coincides with the gradient for smooth functions and with the traditional subdifferential for nonsmooth convex functions. As already mentioned, and contrarily to the (sub)differential operator, it does not enjoy a sum rule.} \n\t\t\n\t\tThanks to Definition~\\ref{def:clarke}, we can extend \\eqref{eq::contdinSmooth} to nondifferentiable functions. Since $\\partial \\mathcal{J}(\\theta)$ is a set, we no longer study a differential equation but rather a {\\em differential inclusion}, given by,\n\t\t \\begin{equation}\\label{eq::contdinClarke}\n\t\t \\begin{cases}\n\t\t \\dot{\\theta}(t) + \\beta \\partial \\mathcal{J}(\\theta(t)) &+(\\alpha -\\frac{1}{\\beta})\\theta(t) + \\frac{1}{\\beta} \\psi(t) \\ni 0\\\\\n\t\t \\dot{\\psi}(t) &+(\\alpha -\\frac{1}{\\beta})\\theta(t) + \\frac{1}{\\beta} \\psi(t) \\ni 0 \\end{cases} \\mbox{,\\quad for a.e. $t\\in (0,+\\infty)$}.\n\t\t \\end{equation}\n\t\t For a given initial condition $(\\theta_0,\\psi_0)\\in \\mathbb{R}^P\\times\\mathbb{R}^P$, we call {\\em solution} (or {\\em trajectory}) of this system any absolutely continuous curve $(\\theta,\\psi)$ from $\\mathbb{R}_+$ to $\\mathbb{R}^P\\times\\mathbb{R}^P$ for which $(\\theta(0),\\psi(0)) = (\\theta_0,\\psi_0)$ and \\eqref{eq::contdinClarke} holds. We recall that absolute continuity amounts to the fact that $\\theta$ is differentiable almost everywhere with integrable derivative and,\n\t\t$$\\theta(t) -\\theta(0)=\\int_0^t\\dot\\theta(s)\\diff s, \\mbox{ for $t\\in [0,+\\infty)$.}$$\n\t\tDue to the properties of the Clarke subdifferential, existence of a solution to differential inclusions such as \\eqref{eq::contdinClarke} is ensured, see \\cite{aubin}; note however that uniqueness of the solution does not hold in general. We will now use the structure of \\eqref{eq::contdinClarke} to build a new algorithm to train DNNs.\n\n\t\t \\subsubsection{Discretization of the Differential Inclusion}\n\n To obtain the basic form of our algorithm, we discretize \\eqref{eq::contdinClarke} according to the classical explicit Euler method. Given $(\\theta,\\psi)$ a solution of \\eqref{eq::contdinClarke} and any time $t_k$, set $\\theta_k = \\theta(t_k)$ and $\\psi_k = \\psi(t_k)$. Then, at time $t_{k+1}=t_{k}+\\gamma_k$ with $\\gamma_k$ positive small, one can approximate $\\dot{\\theta}(t_{k+1})$ and $\\dot{\\psi}(t_{k+1})$ by\n \\[ \\dot{\\theta}(t_{k+1})\\simeq \\frac{\\theta_{k+1}-\\theta_k}{\\gamma_k},\\quad \\quad \\dot{\\psi}(t_{k+1})\\simeq \\frac{\\psi_{k+1}-\\psi_k}{\\gamma_k}. \\]\n This discretization yields the following algorithm,\n \\begin{equation}\\,\\,\\,\\label{eq::notindian}\n \t\\begin{cases}\n \t v_k &\\in \\partial\\mathcal{J}(\\theta_k)\\\\\n \t\\theta_{k+1}&= \\theta_k + \\gamma_k \\left( (\\frac{1}{\\beta}-\\alpha)\\theta_k -\\frac{1}{\\beta}\\psi_k - \\beta v_k \\right)\\\\\n \t\\psi_{k+1}& = \\psi_k + \\gamma_k \\left( (\\frac{1}{\\beta}-\\alpha)\\theta_k - \\frac{1}{\\beta} \\psi_k \\right)\n \t\\end{cases}\n \t\\end{equation}\n\n Although the algorithm above is well defined for our problem, \\new{it is not suited to DL. First, the computation of $\\partial\\mathcal{J}(\\theta_k)$ is generally not possible since there is no general operational calculus for Clarke's subdifferential; secondly, a mini-batch strategy must be designed to cope with the large dimensionality of DL problems which makes the absence of sum rule even more critical.\n \n The next section is meant to address these issues and design a practical algorithm.}\n \n \n\n\n \\subsubsection{INDIAN Algorithm with a New Notion of Steady States}\n\n\n In order to compute or approximate the subdifferential of $\\mathcal{J}$ at each iteration and to cope with large data sets, $\\mathcal{J}$ can be approximated by mini-batches, reducing the memory footprint and computational cost of evaluation. For any $\\mathsf{B} \\subset \\{1,\\ldots, N\\}$, let us define\n\t \\begin{align}\\label{eq::minibatch}\n\t \\mathcal{J}_{\\mathsf{B}} \\colon \\theta \\mapsto \\sum_{n \\in \\mathsf{B}} l(f(x_n,\\theta),y_n).\n\t \\end{align}\n Unlike in the differentiable case, subgradients do not in general sum up to a subgradient of the sum, that is $\\partial \\mathcal{J}_\\mathsf{B}(\\theta) \\neq \\sum_{n \\in \\mathsf{B}} \\partial l(f(x_n,\\theta),y_n) $ in general. To see this, take for example $ 0 = |\\cdot| - |\\cdot|$, the Clarke subgradient of this function at $0$ is $\\{0\\}$, whereas $\\partial(|0|) + \\partial(-|0|) = [-1,1] + [-1,1] = [-2,2]$. \\new{Standard DL solvers use backpropagation algorithms which implement smooth calculus on nonsmooth and nonconvex objects. Due to the absence of qualification conditions, {\\em the resulting objects are not Clarke subgradients in general}. In order to match the real-world practice of DL,} we introduce a notion of steady states that corresponds to the stationary points generated by a generic mini-batch approach. As we shall see, this allows both for practical applications and convergence analysis (despite the sum rule failure for Clarke subdifferential). \\new{ We emphasize once more that our goal is to {\\em capture the stationary points that are actually met in practice}.}\n \n For any $\\mathsf{B} \\subset \\{1,\\ldots, N\\}$, we introduce the following objects,\n\t \\begin{equation}\n\t\t\tD\\mathcal{J}_{\\mathsf{B}}=\\sum_{n\\in \\mathsf{B}} \\partial\\left[ l(f(x_n,\\cdot),y_n)\\right], \\quad D\\mathcal{J}=\\sum_{n=1}^N \\partial\\left[ l(f(x_n,\\cdot),y_n)\\right].\n\t\t\\end{equation}\n\t Observe that, for each $\\mathsf{B}$, we have $ D\\mathcal{J}_{\\mathsf{B}}\\supset \\partial \\mathcal{J}_{\\mathsf{B}} $ and that $\\mathcal{J}_\\mathsf{B}$ is differentiable almost everywhere with $D\\mathcal{J}_{\\mathsf{B}}= \\partial \\mathcal{J}_{\\mathsf{B}}=\\{\\nabla \\mathcal{J}_{\\mathsf{B}}\\}$, see \\cite{clarke1990optimization}. In particular $D\\mathcal{J} = \\partial \\mathcal{J}$ almost everywhere so that the potential differences with the Clarke subgradient occur on a negligible set. When $\\mathcal{J}$ is tame the equalities even hold on the complement of a finite union of manifolds of dimension strictly lower than $P$---use the classical stratification results for o-minimal structures, \\cite{coste2000introduction}. A point satisfying $D\\mathcal{J}(\\theta)\\ni 0$ will be called {\\em $D$-critical}. \\new{Note that Clarke-critical points ($0 \\in \\partial\\mathcal{J}$) are $D$-critical points but that the converse is not true.} This terminology is motivated by favorable properties: sum and chain rules along curves (see Lemmas \\ref{lem::chainrule} and \\ref{lem:chainRuleSum} below) and the existence of a tame Sard's theorem (see Lemma \\ref{lem:sard}). To our knowledge, this notion of a steady state has not previously been used in the literature.{\\footnote{In a follow-up of this paper, \\cite{BP} have further developed the present ideas and in particular the connection to the backpropagation algorithm.} To the best of our knowledge a direct approach modelling the mini-batch practice has never been considered before.} While this notion is needed for the theoretical analysis, one should keep in mind that $D\\mathcal{J}$ is actually what is computed numerically provided that the automatic differentiation library returns a Clarke subgradient. This computation is usually done with a backpropagation algorithm, similarly to the seminal method of \\cite{rumelhart1986learning}. \n\n\n\t Ultimately, one can rewrite \\eqref{eq::contdinClarke} by replacing $\\partial\\mathcal{J}$ by $D\\mathcal{J}$, which yields a differential inclusion adapted to study mini-batch approximations of nonsmooth loss functions $\\mathcal{J}$. This reads, \n\t \\begin{equation}\\label{eq::contdin}\n\t \\begin{cases}\n\t \\dot{\\theta}(t) + \\beta D \\mathcal{J}(\\theta(t)) &+(\\alpha -\\frac{1}{\\beta})\\theta(t) + \\frac{1}{\\beta} \\psi(t) \\ni 0\\\\\n\t \\dot{\\psi}(t) &+(\\alpha -\\frac{1}{\\beta})\\theta(t) + \\frac{1}{\\beta} \\psi(t) \\ni 0 \\end{cases} \\mbox{,\\quad for a.e. $t\\in (0,+\\infty)$}.\n\t \\end{equation}\n\t Discretizing this system gives a workable version of INDIAN.\n\t Let us consider a sequence $(\\mathsf{B}_k)_{k \\in \\mathbb{N}}$ of nonempty subsets of $\\{1,\\ldots,N\\}$, chosen independently and uniformly at random with replacement, and a sequence of positive step sizes $(\\gamma_k)_{k \\in \\mathbb{N}}$.\n\t For a given initialization $(\\theta_0,\\psi_0)\\in\\mathbb{R}^P\\times\\mathbb{R}^P$, at iteration $k\\geq 1$, our algorithm reads,\n\t \t\\begin{equation}\\hspace{-2cm}\\textrm{(INDIAN)}\\,\\,\\,\\label{eq::discdin}\n \t\\begin{cases}\n \t v_k &\\in D \\mathcal{J}_{\\mathsf{B}_k}(\\theta_k)\\\\\n \t\\theta_{k+1}&= \\theta_k + \\gamma_k \\left( (\\frac{1}{\\beta}-\\alpha)\\theta_k -\\frac{1}{\\beta}\\psi_k - \\beta v_k \\right)\\\\\n \t\\psi_{k+1}& = \\psi_k + \\gamma_k \\left( (\\frac{1}{\\beta}-\\alpha)\\theta_k - \\frac{1}{\\beta} \\psi_k \\right)\n \t\\end{cases}\n \t\\end{equation}\n Here again $\\alpha > 0$ and $\\beta > 0$ are hyper-parameters of the algorithm. {The mini-batch procedure forms} a stochastic approximation of the deterministic dynamics obtained by choosing $\\mathsf{B}_k=\\{1,\\ldots,N\\}$, i.e., when $\\mathcal{J}_{\\mathsf{B}_k}=\\mathcal{J}$ (batch version). This can be seen by observing that the vectors $v_k$ above may be written as $v_k = \\tilde{v}_k+\\eta_k$, where $\\tilde{v}_k \\in D\\mathcal{J}(\\theta_k)$ and $\\eta_k$ compensates for the missing subgradients and can be seen as a zero-mean noise. \t\n %\n\tHence, INDIAN admits the following general abstract stochastic formulation,\n\t\\begin{equation}\\,\\,\n \\begin{cases}\n \t w_k &\\in D \\mathcal{J}(\\theta_k)\\\\\n \\theta_{k+1}&= \\theta_k + \\gamma_k \\left( (\\frac{1}{\\beta}-\\alpha)\\theta_k -\\frac{1}{\\beta}\\psi_k - \\beta w_k +\\xi_k\\right)\\\\\n \\psi_{k+1}& = \\psi_k + \\gamma_k \\left( (\\frac{1}{\\beta}-\\alpha)\\theta_k - \\frac{1}{\\beta} \\psi_k \\right)\n \t\\end{cases}\\label{eq::indiang}\n \\end{equation}\n where $(\\xi_k)_{k \\in \\mathbb{N}}$ is a martingale difference noise sequence adapted to the filtration induced by (random) iterates up to $k$. While~\\eqref{eq::discdin} is the version implemented in practice, its equivalent form~\\eqref{eq::indiang} is more convenient for the convergence analysis of the next section. We stress that the equivalence between \\eqref{eq::discdin} and \\eqref{eq::indiang} relies on the use of $D\\mathcal{J}$ and would not hold with with the use of $\\partial \\mathcal{J}$ as in~\\eqref{eq::notindian}.\n\nINDIAN in its general and practical form is summarized in Table~\\ref{tab:indian}.\n\n\n\n \\begin{table}[ht]\n \\centering\n\\begin{mdframed}[style=MyFrame]\n \\begin{center}\n {\\bf Inertial Newton Algorithm for Deep Learning (INDIAN)}\n \\end{center}\n\\bigskip\n\n\\noindent\n\\textbf{Objective function:} $\\mathcal{J} = \\sum_{n = 1}^N \\mathcal{J}_n$, with $\\mathcal{J}_n\\colon \\mathbb{R}^P \\mapsto \\mathbb{R}$ locally Lipschitz.\\\\\n\\textbf{Hyper-parameters:} $(\\alpha,\\beta)$ positive.\\\\\n\\textbf{Mini-batches:} $(\\mathsf{B}_k)_{k \\in \\mathbb{N}}$, nonempty subsets of $\\{1,\\ldots,N\\}$.\\\\\n\\textbf{Step sizes:} $(\\gamma_k)_{k \\in \\mathbb{N}}$ positive.\\\\\n\\textbf{Initialization:} $(\\theta_0,\\psi_0)\\in\\mathbb{R}^P\\times\\mathbb{R}^P$.\\\\\n\n\n\n\n\n\\textbf{For $k\\in \\mathbb{N}$:}\n\\begin{equation*}\n \t\\begin{cases}\n \t v_k &\\in\\quad \\sum_{n\\in \\mathsf{B}_k} \\partial\\left[ \\mathcal{J}_n(\\theta_k)\\right]\\\\\n \t\\theta_{k+1}&=\\quad \\theta_k + \\gamma_k \\left( (\\frac{1}{\\beta}-\\alpha)\\theta_k -\\frac{1}{\\beta}\\psi_k - \\beta v_k \\right)\\\\\n \t\\psi_{k+1}& =\\quad \\psi_k + \\gamma_k \\left( (\\frac{1}{\\beta}-\\alpha)\\theta_k - \\frac{1}{\\beta} \\psi_k \\right)\n \t\\end{cases}\n\\end{equation*}\n\\end{mdframed}\n\\caption{INDIAN in a nutshell. \\label{tab:indian}}\n\\end{table}\n\n\n\n\n\n \\section{Convergence Results for INDIAN}\n\\label{sec:proof}\n\n\n\t\\subsection{Main result: Accumulation Points of INDIAN are Critical}\n\n\n\nWe now study the convergence of INDIAN. The main idea here is to prove that the discrete algorithm~\\eqref{eq::discdin} asymptotically behaves like the solutions of the continuous differential inclusion~\\eqref{eq::contdin}. Besides tameness, our main assumption is the following:\n\t\\begin{assumption}[Stochastic approximation]\\label{ass:itbound}\n\t The sets $(\\mathsf{B}_k)_{k \\in \\mathbb{N}}$ are taken independently uniformly at random with replacement.\n\t\tThe step-size sequence $\\gamma_k$ is positive with $\\sum_k\\gamma_k=+\\infty$ and satisfies $\\gamma_k = o \\left( \\frac{1}{\\log k}\\right)$, that is $\\displaystyle\\limsup_{k\\to+\\infty} |\\gamma_k \\log k|=0$.\t \\label{ass:mainAssumption}\n\t\\end{assumption}\nTypical admissible choices are $\\gamma_k=C(k+1)^{-a}$ with $a\\in (0,1]$, $C > 0$. \n\tThe main theoretical result of the paper follows. \n\n\t\\begin{theorem}[\\textrm{INDIAN } converges to the set of $D$-critical points of $\\mathcal{J}$]\\label{th::thmDIN}\n\t\tAssume that for \\new{$n\\in\\{1,\\ldots,N\\}$, each $\\mathcal{J}_n$ is locally Lipschitz continuous}, tame and that the step sizes satisfy Assumption~\\ref{ass:mainAssumption}. Set an initial condition $(\\theta_0,\\psi_0)$ and assume that there exists $M>0$ such that $\\sup_{k\\geq 0}\\|(\\theta_k,\\psi_k)\\|\\leq M$ almost surely, where $(\\theta_k,\\psi_k)_{k\\in\\mathbb{N}}$ are generated by INDIAN.\n\t Then, almost surely, any accumulation point $\\bar\\theta$ of the sequence $(\\theta_k)_{k\\in \\mathbb{N}}$ satisfies $D\\mathcal{J}(\\bar\\theta)\\ni 0 $. In addition $ (\\mathcal{J}(\\theta_{k}))_{k\\in\\mathbb{N}}$ converges.\t \n\t\\end{theorem}\n\n\t\\new{Before proving Theorem~\\ref{th::thmDIN}, we will first make some comments and illustrate this result.}\n \\subsection{Comments on the Results of Theorem~\\ref{th::thmDIN}}\\label{sec::comRes}\n \n \\new{\n \n \\paragraph{On the step sizes.}\n First, Assumption~\\ref{ass:mainAssumption} offers much more flexibility than the usual $O(1\/\\sqrt{k})$ assumption commonly used for SGD. We leverage the boundedness assumption on the norms of $(\\theta_k,\\psi_k)$, the local Lipschitz continuity and finite-sum structure of $\\mathcal{J}$, so that the noise is actually uniformly bounded and hence sub-Gaussian, allowing for much larger step sizes than in the more common bounded second moment setting. See \\citet[Remark 1.5]{benaim2005stochastic} and \\cite{benaim1999dynamics} for more details. The interest of this aggressive strategy is highlighted in Figure~\\ref{fig::decay} of the experimental section. \n \n \\paragraph{On the scope of the theorem.}\n Our result actually holds for general locally Lipschitz continuous tame functions with finite-sum structure and for the general stochastic process under uniformly bounded martingale increment noise. We do not use any other specific structure of DL loss functions. Other variants could be considered depending on the assumptions on the noise, see \\cite{benaim2005stochastic}.}\n \n \\new{\n \\paragraph{On $D$-criticality.}\n The result of Theorem~\\ref{th::thmDIN} states that the bounded discrete trajectories of INDIAN are attracted by the $D$-critical points. Recall that $D$-critical points include local minimizers and thus our theoretical finding agrees with our empirical observations that most initializations lead to ``valuable weights'' $\\theta$ and to efficient training. In particular for smooth networks where $\\mathcal{J}$ is differentiable, limit points of INDIAN are simply critical points of $\\cal J$.\n The reader should however remember that when the algorithm is initialized on the $D$-critical set, the algorithm is stationary as well, {\\em even when the initialization is non-Clarke critical}. This last point shows that $D$-points are not introduced to simplify the analysis but to {\\em sharply model the use of mini-batch methods on nonconvex and nonsmooth problems}. Hopefully, in practice one can expect to avoid such points with overwhelming probability. Indeed, following our initial introduction of the $D$-points, \\citet{bolte2020mathematical} proved for SGD, under very mild conditions on the initialization, that $D$-critical points that are not Clarke-critical are not reached with probability one (see also \\cite{bianchi2020convergence}).\n \n \n \n \n \n \\paragraph{On the boundedness assumption.} The bounded assumption on the iterates is a classical assumption for first or second-order algorithms, see for instance \\cite{davis2018stochastic,duchi2018stochastic}. When using deterministic algorithms (i.e., without mini-batch approximations), properties such as the coercivity of $\\mathcal{J}$ can be sufficient to remove the boundedness assumption for descent algorithms. This does not remain true when dealing with mini-batch approximations, yet, in the case of INDIAN, the coercivity of $\\mathcal{J}$ would guarantee at least that the solutions of the continuous underlying differential inclusion \\eqref{eq::contdin} remain bounded. Indeed, we will prove in Section~\\ref{sec::proofofConv} that for any solution $(\\theta,\\psi)$ of \\eqref{eq::contdin}, the function $E(\\theta(t),\\psi(t)) \\triangleq 2(1+\\alpha\\beta)\\mathcal{J}(\\theta(t)) + \\left\\Vert (\\alpha-\\ovb)\\theta(t) +\\ovb \\psi(t) \\right\\Vert^2 $ is decreasing in time (see Lemma~\\ref{lem::Edec} hereafter). As a consequence, we cannot have $\\mathcal{J}(\\theta(t))\\xrightarrow[t\\to\\infty]{}\\infty$ so $\\Vert\\theta(t)\\Vert\\not\\to\\infty$ due to the coercivity of $\\mathcal{J}$. In addition this guarantees $\\Vert\\psi(t)\\Vert\\not\\to\\infty$ as well. However DL loss functions are not coercive in general and studying the boundedness issue in DL or even for nonconvex semi-algebraic problems is far beyond the scope of this paper. Let us however mention that it is not uncommon to project the iterates on a given large ball to ensure boundedness; this is a matter for future research.}\n \n \n \\subsection{Preliminary Variational Results}\n\t\\label{sec:ResultsOnD}\n Prior to proving Theorem 3, we extend some results known for the Clarke subdifferential of tame functions to the operator $D$ that we previously introduced. First, we recall a useful result of \\cite{davis2018stochastic} which follows from the projection formula in \\cite{bolte2007clarke}.\n\t\\begin{lemma}[Chain rule for the Clarke subdifferential]\\label{lem::chainrule}\n\t Let $\\mathcal{J}:\\mathbb{R}^P\\to \\mathbb{R}$ be a locally Lipschitz continuous tame function, then $\\mathcal{J}$ admits a chain rule, meaning that for all absolutely continuous curves $\\theta:\\mathbb{R}_+\\to\\mathbb{R}^P$, \\new{$\\mathcal{J}\\circ\\theta$ is differentiable a.e.} and for a.e. $t\\geq 0$,\n\t \\begin{equation}\\label{eq::chainruleJ}\n\t \\frac{\\diff \\mathcal{J}}{\\diff t} (\\theta(t)) = \\langle \\dot{\\theta}(t),\\partial \\mathcal{J}(\\theta(t)) \\rangle = \\langle \\dot{\\theta}(t) ,v \\rangle,\\ \\ \\forall v\\in \\partial \\mathcal{J}(\\theta(t)).\n\t \\end{equation}\n\t\\end{lemma}\n\\medskip\n\\new{Note that, even though $\\mathcal{J}$ is nondifferentiable on $\\mathbb{R}^P$, the function $t\\mapsto \\mathcal{J}(\\theta(t))$ is differentiable for a.e. $t>0$. Indeed, as introduced in Section~\\ref{sec::nonsmoothnonconv}, an absolutely continuous curve from $t\\geq 0$ to $\\mathbb{R}^P$ is differentiable for a.e. $t>0$. This, combined with the chain-rule of Lemma~\\ref{lem::chainrule} allows to differentiate $\\mathcal{J}\\circ\\theta$ for a.e. $t>0$ whenever $\\mathcal{J}$ is tame and locally Lipschitz continuous. \\new{Besides, notice that the value of $\\frac{\\diff \\mathcal{J}}{\\diff t} (\\theta(t))$ in \\eqref{eq::chainruleJ} does not depend on the element $v$ taken in $\\partial\\mathcal{J}(\\theta(t))$ which justifies the notation $\\langle \\dot{\\theta}(t),\\partial \\mathcal{J}(\\theta(t)) \\rangle$}.}\n\nConsider now a function with an additive finite-sum structure (such as in DL):\n\\begin{align}\\label{eq::fsumofTame}\n\t\t\\mathcal{J} \\colon\\mathbb{R}^P \\ni \\theta \\mapsto \\sum_{n=1}^N \\mathcal{J}_n(\\theta),\n\\end{align}\nwhere each $\\mathcal{J}_n \\colon \\mathbb{R}^P \\mapsto \\mathbb{R}$ is locally Lipschitz continuous and tame. We define for any $\\theta \\in \\mathbb{R}^P$\n\\begin{align*}\n\t\t\t\tD\\mathcal{J}(\\theta) = \\sum_{n=1}^N \\partial \\mathcal{J}_n(\\theta).\n\\end{align*}\nThe following lemma is a direct generalization of the above chain rule.\n\\begin{lemma}[Chain rule for $D\\mathcal{J}$]\\label{lem::chainruleD}\n\t\t\t\tLet $\\mathcal{J}$ be a sum of tame functions like in \\eqref{eq::fsumofTame}. Let $c \\colon [0,1] \\mapsto \\mathbb{R}^P$ be an absolutely continuous curve so that $t \\mapsto \\mathcal{J}(c(t))$ is differentiable almost everywhere. For a.e. $t \\in [0,1]$, and for all $v \\in D\\mathcal{J}(c(t))$,\n\t\t\t\t\\begin{align*}\n\t\t\t\t\t\t\t\t\\frac{d}{dt} \\mathcal{J}(c(t)) = \\left\\langle v, \\dot{c}(t) \\right\\rangle.\n\t\t\t\t\\end{align*}\n\t\t\t\t\\label{lem:chainRuleSum}\n\n\\end{lemma}\n \\begin{proof}\n\t\t\t\tBy local Lipschitz continuity and absolute continuity, each $\\mathcal{J}_n$ is differentiable almost everywhere and Lemma \\ref{lem::chainrule} can be applied:\n\t\t\t\t\\begin{align*}\n\t\t\t\t\t\t\t\t\\frac{d}{dt} \\mathcal{J}_n(c(t)) = \\left\\langle v_n, \\dot{c}(t) \\right\\rangle, \\mbox{for all $v_n \\in \\partial \\mathcal{J}_n(c(t))$ and for a.e. $t\\geq 0$.}\n\t\t\t\t\\end{align*}\n\t\t Thus\n\t\t\t\t\\begin{align*}\n\t\t\t\t\t\t\t\t\\frac{d}{dt} \\mathcal{J}(c(t)) = \\sum_{n=1}^N \\frac{d}{dt} \\mathcal{J}_n(c(t)) = \\sum_{n=1}^N\\left\\langle v_n, \\dot{c}(t) \\right\\rangle,\n\t\t\t\t\\end{align*}\n\t\t\tfor any $v_n \\in \\partial \\mathcal{J}_n(c(t))$, for all $n=\\{1,\\ldots, N\\}$, and for a.e. $t\\geq 0$. This proves the desired result.\n \\end{proof}\n We finish this section with a Sard lemma for $D$-critical values, in the spirit of \\cite{bolte2007clarke}.\n \\begin{lemma}[A Sard's theorem for tame D-critical values]\n\t\t\t\tLet, $$ \\mathsf{S}= D\\mbox{\\rm -crit\\,}\\triangleq\\left\\{ \\theta \\in \\mathbb{R}^P\\mid D\\mathcal{J}(\\theta) \\ni 0 \\right\\},$$ then $\\mathcal{J}(\\mathsf{S})$ is finite. \n\t\t\t\t\\label{lem:sard}\n \\end{lemma}\n \\begin{proof}\n\t\t\t\tThe set $\\mathsf{S}$ is tame and hence it has a finite number of connected components. It is sufficient to prove that $\\mathcal{J}$ is constant on each connected component of $\\mathsf{S}$. Without loss of generality, assume that $\\mathsf{S}$ is connected and consider $\\theta_0,\\theta_1 \\in \\mathsf{S}$. By Whitney regularity \\cite[4.15]{van1998tame}, there exists a tame continuous path $\\Gamma$ joining $\\theta_0$ to $\\theta_1$. Because of the tame nature of the result, we should here conclude with only tame arguments and use the projection formula in \\cite{bolte2007clarke}, but for convenience of readers who are not familiar with this result we use Lemma \\ref{lem::chainrule}. Since $\\Gamma$ is tame, the monotonicity lemma (see for example \\citet[Lemma 2]{kurdyka1998gradients}) gives the existence of a finite collection of real numbers $0 = a_0 < a_1 < \\ldots < a_q = 1$, such that $\\Gamma$ is $C^1$ on each segment $(a_{j-1}, a_{j})$, $j = 1,\\ldots,q$. Applying Lemma \\ref{lem::chainrule} to each $\\Gamma_{|(a_i,a_{i+1})}$, we see that $\\mathcal{J}$ is constant except perhaps on a finite number of points, thus $\\mathcal{J}$ is constant by continuity.\n \\end{proof}\n\n\n\t\\subsection{Proof of Convergence for INDIAN}\n\t\\label{sec::proofofConv}\n Our approach follows the stochastic method for differential inclusions developed in \\cite{benaim2005stochastic} for which the differential system \\eqref{eq::contdin} and its Lyapunov properties play fundamental roles. \n \n \n\tThe steady states of \\eqref{eq::contdin}\\, are given by,\n\t\\begin{equation}\\label{S} \\mathsf{S} = \\left\\{ (\\theta,\\psi)\\in \\mathbb{R}^P\\times \\mathbb{R}^P \\mid 0\\in D \\mathcal{J}(\\theta), \\psi=(1-\\alpha\\beta) \\theta \\right\\}.\\end{equation}\n\tThese points are initialization values for which the system does not evolve and remains constant.\n Observe that the first coordinates of these points are $D$-critical for $\\mathcal{J}$ and that conversely any $D$-critical point of $\\mathcal{J}$ corresponds to a unique rest point in $\\mathsf{S}$. \n \n\n\t\\begin{definition}[Lyapunov function]\\label{def::lyap}\n\t\tLet $ \\mathsf{A} $ be a subset of $\\mathbb{R}^P \\times \\mathbb{R}^P $, we say that $ E : \\mathbb{R}^P\\times \\mathbb{R}^P\\to \\mathbb{R} $ is a Lyapunov function for the set $ \\mathsf{A} $ and the dynamics \\eqref{eq::contdin} if\n\t\t\\renewcommand{\\theenumi}{(\\roman{enumi})}%\n\n\t\t\\begin{enumerate}\n\t\t \\item for any solution $ (\\theta,\\psi)$ of \\eqref{eq::contdin} with initial condition $(\\theta_0,\\psi_0) $, we have:\\\\\n\t\t $E(\\theta(t),\\psi(t) ) \\leq E(\\theta_0,\\psi_0)$ a.e. on $\\mathbb{R}$.\n\t\t \\item for any solution $ (\\theta,\\psi)$ of \\eqref{eq::contdin} with initial condition $(\\theta_0,\\psi_0) \\notin \\mathsf{A}$, we have:\\\\\n\t\t $E(\\theta(t),\\psi(t) ) < E(\\theta_0,\\psi_0)$ a.e. on $\\mathbb{R}$.\n\t\t\\end{enumerate}\n\t\\end{definition}\n\n\tIn practice, to establish that a functional is Lyapunov, one can simply use differentiation through chain rule results, with in particular Lemma \\ref{lem::chainrule}. In the context of INDIAN, we will use Lemma \\ref{lem:chainRuleSum}. To build a Lyapunov function for the dynamics \\eqref{eq::contdin} and the set $ \\mathsf{S} $, consider the two following energy-like functions,\n\t\\begin{equation}\n\t\\begin{cases}\n\tE_{\\min}(\\theta(t),\\psi(t)) &= (1-\\sqrt{\\alpha\\beta})^2 \\mathcal{J}(\\theta(t)) + \\frac 1 2 \\left\\Vert (\\alpha-\\ovb)\\theta(t) +\\ovb \\psi(t) \\right\\Vert^2 \\\\\n\tE_{\\max}(\\theta(t),\\psi(t)) &= (1+\\sqrt{\\alpha\\beta})^2 \\mathcal{J}(\\theta(t)) + \\frac 1 2 \\left\\Vert (\\alpha-\\ovb)\\theta(t) +\\ovb \\psi(t) \\right\\Vert^2.\n\t\\end{cases}\n\t\\end{equation}\n\nThen the following lemma applies.\n\t\\begin{lemma}[Differentiation along DIN trajectories]\\label{lem::dEdt}\n\t\tLet $(\\theta,\\psi)$ be a solution of \\eqref{eq::contdin} with initial condition $(\\theta_0,\\psi_0)$. For a.e. $t>0$, $\\theta$ and $\\psi$ are differentiable at $t$, \\eqref{eq::contdin} holds, $\\frac{\\dt(t)-\\ddp(t)}{\\beta} \\in D \\mathcal{J}(\\theta(t))$ and%\n\t\t\\begin{align*}\n\t\t%\n\t\t\\frac{\\diff E_{\\min}}{\\diff t}\\left(\\theta(t),\\psi(t)\\right) &= -\\left\\Vert \t\\sqrt{\\alpha}\\dot{\\theta}(t) -\\frac{1}{\\sqrt{\\beta}}\\left(\\dot{\\psi}(t)-\\dot{\\theta}(t)\\right)\\right\\Vert^2 \\\\\n\t\t%\n\t\\frac{\\diff E_{\\max}}{\\diff t}\\left(\\theta(t),\\psi(t)\\right) &= -\\left\\Vert \t\\sqrt{\\alpha}\\dot{\\theta}(t) +\\frac{1}{\\sqrt{\\beta}}\\left(\\dot{\\psi}(t)-\\dot{\\theta}(t)\\right) \\right\\Vert^2\n\\end{align*}\n\t\\end{lemma}\n\n\t\\begin{proof}\n\t\tDefine $\\displaystyle E_\\lambda(\\theta,\\psi) = \\lambda {\\mathcal J}(\\theta) + \\frac 1 2 \\left\\Vert (\\alpha-\\ovb)\\theta +\\ovb \\psi \\right\\Vert^2 $. We aim at choosing $ \\lambda $ so that $ E_\\lambda$ is a Lyapunov function. Because $\\mathcal{J}$ is tame and locally Lipschitz continuous, using Lemma~\\ref{lem:chainRuleSum} we know that for any absolutely continuous trajectory $ \\theta:\\mathbb{R}_+\\to \\mathbb{R}^P $ and for a.e. $ t>0 $,\n\t\t\\begin{equation}\n\t\t\\frac{\\diff \\mathcal{J}}{\\diff t} (\\theta(t)) = \\langle \\dot{\\theta}(t), D \\mathcal{J}(\\theta(t)) \\rangle = \\langle \\dot{\\theta}(t) ,v(t) \\rangle,\\ \\ \\forall v(t)\\in D \\mathcal{J}(\\theta(t)).\n\t\t\\end{equation}\n\t\tLet $\\theta$ and $\\psi$ be solutions of (DIN). For a.e. $t\\geq 0$, we can differentiate $ E_\\lambda(\\theta,\\psi) $ to obtain\n\\begin{equation}\\begin{split}\n\\frac{\\diff E_\\lambda}{\\diff t}(\\theta(t),\\psi(t)) = & \\lambda \\langle \\dot{\\theta}(t) ,v(t) \\rangle + (\\alpha-\\ovb) \\langle \\dot{\\theta}(t) ,(\\alpha-\\ovb)\\theta(t)+\\ovb\\psi(t) \\rangle \\\\\n\t& + \\ovb \\langle \\dot{\\psi(t)} ,(\\alpha-\\ovb)\\theta(t)+\\ovb\\psi(t)\\rangle\n\t\\end{split}\n\t\\end{equation}\n\tfor all $v(t)\\in D \\mathcal{J}(\\theta(t))$. Using \\eqref{eq::contdin}, we get $\\frac{1}{\\beta}(\\dot\\theta(t)-\\dot \\psi(t))\\in D \\mathcal{J}(\\theta(t))$ and $-\\dot{\\psi}(t)=(\\alpha-\\ovb)\\theta(t)+\\ovb \\psi(t)$ a.e. Choosing $v(t)=\\frac{1}{\\beta}(\\dot\\theta(t)-\\dot \\psi(t))$ yields:\\begin{align*}\n\t\t \\frac{\\diff E_\\lambda}{\\diff t}(\\theta(t),\\psi(t)) &= \\lambda \\left\\langle \\dot{\\theta}(t) ,\\frac{\\dt(t)-\\ddp(t)}{\\beta} \\right\\rangle - (\\alpha-\\ovb) \\left\\langle \\dot{\\theta}(t) ,\\dot{\\psi}(t) \\right\\rangle - \\ovb \\left\\langle \\dot{\\psi}(t) ,\\dot{\\psi}(t) \\right\\rangle.\n\t\t\\end{align*}\n\t\tThen, expressing everything as a function of $\\dot{\\theta}$ and $\\frac{1}{\\beta}(\\psi-\\theta)$, one can show that a.e. on $\\mathbb{R}_+$:\n\t\t\\begin{align*}\n\t\t\\frac{\\diff E_\\lambda}{\\diff t}&(\\theta,\\psi)(t) = - \\alpha \\Vert \\dot{\\theta}(t)\\Vert^2 -\\beta \\left\\Vert \\frac{\\dt(t)-\\ddp(t)}{\\beta} \\right\\Vert^2 + \\left(\\lambda - \\alpha\\beta -1 \\right)\\langle \\dot{\\theta}(t) ,\\frac{\\dt(t)-\\ddp(t)}{\\beta} \\rangle\\\\\n\t\t&= -\\left\\Vert \\sqrt{\\alpha}\\dot{\\theta}(t) + \\frac{\\alpha\\beta +1 - \\lambda }{2\\sqrt{\\alpha}}\\frac{\\dt(t)-\\ddp(t)}{\\beta}\\right\\Vert^2 - \\left ( \\beta -\\frac{(\\alpha\\beta +1 - \\lambda)^2}{4\\alpha} \\right)\\left\\Vert \\frac{\\dt(t)-\\ddp(t)}{\\beta} \\right\\Vert^2.\n\t\t\\end{align*}\n\t\tWe aim at choosing $ \\lambda $ so that $ E_\\lambda $ is decreasing that is $\\left ( \\beta -\\frac{(\\alpha\\beta +1 - \\lambda)^2}{4\\alpha} \\right) > 0$. This holds whenever $ \\lambda \\in\\, \\left[(1-\\sqrt{\\alpha\\beta})^2,(1+\\sqrt{\\alpha\\beta})^2\\right] $. We choose $ \\lambda_{\\min} =(1-\\sqrt{\\alpha\\beta})^2 $, and $ \\lambda_{\\max} = (1+\\sqrt{\\alpha\\beta})^2 $, for these two values we obtain for a.e. $ t>0 $ ,\n\t\t\\begin{equation}\\begin{cases}\n\t\t\\dot E_{\\lambda_{\\min}}(\\theta(t),\\psi(t)) &= -\\left\\Vert \\sqrt{\\alpha}\\dot{\\theta}(t) + \\frac{1}{\\sqrt{\\beta}}\\left(\\dot{\\theta}(t)-\\dot{\\psi}(t)\\right) \\right\\Vert^2 \\vspace{0.2cm} \\\\\n\t\t\\dot E_{\\lambda_{\\max}} (\\theta(t),\\psi(t))&= -\\left\\Vert \\sqrt{\\alpha}\\dot{\\theta}(t) - \\frac{1}{\\sqrt{\\beta}}\\left(\\dot{\\theta}(t)-\\dot{\\psi}(t)\\right) \\right\\Vert^2\n\t\t\\end{cases}\n\t\t\\end{equation}\n\t\tRemark finally that by definition $E_{\\min}=E_{\\lambda_{\\min}}$ and $E_{\\max}=E_{\\lambda_{\\max}}$.\n\t\\end{proof}\n\tDefine $ E = E_{\\min} + E_{\\max} $ and recall that $ \\displaystyle \\mathsf{S} = \\left\\{ (\\theta,\\psi)\\in \\mathbb{R}^P\\times \\mathbb{R}^P \\mid 0\\in D J(\\theta), \\psi=(1-\\alpha\\beta) \\theta) \\right\\}$. By a direct integration argument, we obtain the following lemma.\n\t\\begin{lemma}[$E$ is Lyapunov function for INDIAN with respect to $\\mathsf{S}$]\\label{lem::Edec}\n\t\tFor any $(\\theta_0,\\psi_0)\\notin \\mathsf{S}$ and any solution $(\\theta,\\psi)$ with initial condition $(\\theta_0,\\psi_0)$,\n\t\t\\begin{equation}\n\t\tE(\\theta(t),\\psi(t))0.\n\t\t\\end{equation}\n\t\\end{lemma}\n\n\\bigskip\n\n\\noindent\nWe are now in position to provide the desired proof.\n\n\\smallskip\n\n\\noindent\n\t{\\bf Proof of Theorem \\ref{th::thmDIN}} Lemmas~\\ref{lem::dEdt} and~\\ref{lem::Edec} state that\n\t\t$ E $ is a Lyapunov function for the set $ \\mathsf{S} $ and the dynamics \\eqref{eq::contdin}. Let $\\mathsf{C}=\\{\\theta\\in \\mathbb{R}^P\\mid(\\theta,\\psi)\\in \\mathsf{S}\\}$ which is actually the set of $D$-critical points of $\\mathcal{J}$. Using Lemma \\ref{lem:sard} of Section \\ref{sec:ResultsOnD}, $\\mathcal{J}(\\mathsf{C})$ is finite. Moreover, since $E(\\theta,\\psi)=2(1+\\alpha\\beta)\\mathcal{J}(\\theta) $ for all $(\\theta,\\psi)\\in\\mathsf{S}$, $ E $ takes a finite number of values on $ \\mathsf{S} $, and in particular, $E(\\mathsf{S})$ has empty interior.\n\n\n\t\t Denote by $\\mathsf{L}$ the set of accumulation points of a the sequences $((\\theta_k, \\psi_k))_{k \\in \\mathbb{N}}$ produced by \\eqref{eq::discdin} starting at $(\\theta_0,\\psi_0)$ and $\\mathsf{L}_1$ its projection on $\\mathbb{R}^P\\times\\{0\\}$. We have the 3 following properties:\\smallskip\\\\\n \\noindent\n\t$\\quad -$ By assumption, we have $\\|(\\theta_k, \\psi_k)\\| \\leq M$ almost surely, for all $k \\in \\mathbb{N}$.\\\\\n\t$\\quad -$ By local Lipschitz continuity $\\partial \\mathcal{J}_\\mathsf{B}(\\theta)$ is uniformly bounded for $\\|\\theta\\| \\leq M$ and any $\\mathsf{B} \\subset \\{1,\\ldots,N\\}$, hence the centered noise $(\\xi_k)_{k \\in \\mathbb{N}}$ is a uniformly bounded martingale difference sequence.\\\\\n $\\quad -$ By Assumption \\ref{ass:mainAssumption}, the sequence $ (\\gamma_k)_{k \\in \\mathbb{N}} $ are chosen such that $ \\gamma_{k} = o(\\frac{1}{\\log k}) $ (see Section~\\ref{sec::comRes}).\n\n \\new{Then the sufficient conditions of Remark 1.5 of \\cite{benaim2005stochastic} state that the discrete process $(\\theta_k,\\psi_k)_{k\\in\\mathbb{N}}$ asymptotically behaves like the solutions of \\eqref{eq::contdin}. We can then combine Proposition 3.27 and Theorem 3.6 of \\cite{benaim2005stochastic}, \n\t\tto obtain that the limit set $\\mathsf{L}$ of the discrete process is contained in the set $\\mathsf{S}$ where the Lyapunov has vanishing derivatives. Thus the set $\\mathsf{L}_1$ (the set of the first coordinates of all accumulation points) contains only $D$-critical points of $\\mathcal{J}$. In addition, $E(\\mathsf{L})$ is a singleton, and for all $(\\theta,\\psi)\\in\\mathsf{S}$, we have $E(\\theta,\\psi) = \\mathcal{J}(\\theta)$, so $\\mathcal{J}(\\mathsf{L}_1) $ is also a singleton and the theorem follows.}\n\n\n\n \\section{Towards Convergence Rates for INDIAN}\\label{sec::rateofCV}\n\n\n In the previous section, connecting INDIAN to \\eqref{eq::contdin} was one of the keys to prove the convergence of the discrete dynamics. Let us now focus on the continuous dynamical system \\eqref{eq::contdinClarke} in the deterministic case where $\\mathcal{J}$ and $\\partial\\mathcal{J}$ are not approximated anymore -- we thus no longer use $D\\mathcal{J}$ although this would be possible but would require more technical proofs. In this section and in this section only, we pertain to loss functions $\\mathcal{J}$ that are real semi-algebraic (a particular case of tame functions).\\footnote{We could extend the results of this section to more general objects including analytic functions on bounded sets. The semi-algebraicity assumption is made here for the sake of clarity.} Recall that a set is called semi-algebraic if it is a finite union of sets of the form,\n $$\\{\\theta\\in \\mathbb{R}^P\\mid \\zeta(\\theta)=0,\\zeta_i(\\theta)<0\\}$$\n where $\\zeta,\\zeta_i$ are real polynomial functions. A function is called semi-algebraic if its graph is semi-algebraic. \n \n We will prove that the continuous time system \\eqref{eq::contdinClarke} is actually a quasi-gradient dynamic, and that we can characterize the convergence rate to critical points. Let us first introduce an essential mechanism to obtain such convergence rates: the Kurdyka-{\\L}ojasiewicz (KL) property.\n\n\n\\subsection{The Nonsmooth Kurdyka-{\\L}ojasiewicz Property for the Clarke Subdifferential}\n\nThe nonsmooth Kurdyka-{\\L}ojasiewicz (KL) property, as introduced in \\citep{bolte2010}, is a measure of ``amenability to sharpness'' \\new{(as illustrated at the end of Section~\\ref{sec::rateIND})}. Here we provide a uniform version for the Clarke subdifferential of semi-algebraic functions as in \\cite{bolte2007clarke} and \\cite{bolte2014proximal}. In the sequel we denote by ``$\\dist$'' any given distance on $\\mathbb{R}^P$.\n\\begin{lemma}[Uniform Nonsmooth KL Property for the Clarke Subdifferential]\\label{lem::UKL}\n Let $\\mathsf{K}$ be a nonempty compact set and let $L:\\mathbb{R}^P\\to \\mathbb{R}$ be a semi-algebraic locally Lipschitz continuous function. Assume that $L$ is constant on $\\mathsf{K}$, with value $L^\\star$. Then there exist $\\varepsilon>0$, $\\delta>0$, $a\\in(0,1)$ and $\\rho>0$ such that, for all\n \\begin{equation*}\n v\\in \\left\\{ v\\in\\mathbb{R}^P \\mid \\dist(v,\\mathsf{K}) < \\varepsilon \\right\\} \\cap \\left\\{ v\\in\\mathbb{R}^P \\mid L^\\star < L(v) < L^\\star+\\delta \\right\\},\n \\end{equation*}\n it holds that,\n \\begin{equation}\\label{eq::SemiAlgKL}\n \\rho(1-a)\\left(L(v) - L(\\bar{v})\\right)^{-a}\\dist\\left( 0, \\partial L(v)\\right) > 1.\n \\end{equation}\n \\end{lemma}\n\n \\new{The proof directly follows from the general inequality provided in \\cite{bolte2007clarke} or the local result of \\cite{bolte2007clarke} with the compactness arguments of \\citet[Lemma 6]{bolte2014proximal}.} In the sequel, we make an abuse of notation by writing $\\Vert \\partial\\mathcal{J}(\\cdot)\\Vert \\triangleq \\dist(0,\\partial \\mathcal{J}(\\cdot))$.\n To obtain a convergence rate we will use inequality \\eqref{eq::SemiAlgKL} on the Lyapunov function $E$. But first we state a general result of convergence that is built around the KL property.\n\n\\subsection{A General Asymptotic Rate Result}\n We state a general theorem that leads to the existence of a convergence rate. This theorem will hold in particular for \\eqref{eq::contdinClarke}. We start by stating the result.\n\n \n \\begin{theorem}\\label{th::genlemma}\n Let $X:[0,+\\infty)\\to \\mathbb{R}^P$ be a bounded absolutely continuous trajectory and let $L:\\mathbb{R}^P\\to \\mathbb{R}$ be a semi-algebraic locally Lipschitz continuous function. If there exists $c_1>0$ such that for a.e. $t>0$,\n \\begin{equation}\\label{it::2}\\tag{i}\n \\frac{\\diff L}{\\diff t} (X(t)) \\leq -c_1 \\Vert(\\partial L)(X(t))\\Vert^2,\n \\end{equation}\n then $L(X(t))$ converges to a limit value $L^\\star$ and,\n \\[ \\vert L(X(t)) - L^\\star \\vert = O\\left(\\frac{1}{t}\\right). \\]\n If in addition there exists $c_2>0$ such that for a.e. $t>0$,\n \\begin{equation}\\label{it::3}\\tag{ii}\n c_2 \\Vert \\dot{X}(t)\\Vert \\leq \\Vert (\\partial L)(X(t))\\Vert,\n \\end{equation}\n \tthen, $X$ converges to a critical point of $L$ with a rate of the form $O(1\/t^b)$ with $b>0$.\\footnote{In some cases we even have linear rates or finite convergence as detailed in the proof.}\n \\end{theorem}\n \n \\begin{proof}\n We first prove the convergence of $L(X(\\cdot))$. Suppose that \\eqref{it::2} holds. Since $X$ is bounded and $L$ is continuous, $L(X(\\cdot))$ is bounded. Moreover from \\eqref{it::2}, $L(X(\\cdot))$ is decreasing, so it converges to some value $L^\\star$. To simplify suppose $L\\geq 0$ and $L^\\star=0$. Define,\n \\begin{equation*}\n\t\t\t\\mathsf{I} = \\left\\{ x\\in\\mathbb{R}^P \\ \\mid \\ L(x)=0 \\right\\}.\n\t\\end{equation*}\n\tSuppose first that there exists $s\\geq 0$, such that $X(s)\\in \\mathsf{I}$. Since $L(X(\\cdot))$ is decreasing with limit $0$, then for all $t\\geq s$, $L(X(t)) = 0$ and the convergence rate holds true.\n\t\n\tLet us thus assume that for all $t\\geq 0$, $L(X(t))>0$. The trajectory $X$ is bounded in $\\mathbb{R}^P$, hence there exists a compact set $\\mathsf{C}\\subset\\mathbb{R}^P$ such that $X(t)\\in\\mathsf{C}$ for all $t\\geq 0$. Define $\\mathsf{K}=\\mathsf{I}\\cap \\mathsf{C}$. It is a compact set since $\\mathsf{I}$ is closed (by continuity of $L$) and $\\mathsf{C}$ is compact. Moreover, $L$ is constant on $\\mathsf{K}$. As such by Lemma~\\ref{lem::UKL}, there exist $\\varepsilon>0,\\ \\delta>0$, $a\\in(0,1)$ and a constant $\\rho>0$ such that for all\n\t\t\\[ v\\in \\left\\{ v\\in\\mathbb{R}^P, \\dist(v,\\mathsf{K}) < \\varepsilon \\right\\} \\cap \\left\\{ 0 < L(v) < \\delta \\right\\}, \\]\n\t\tit holds that\n\t\t\\[\\rho(1-a)\\left(L(v)\\right)^{-a}\\dist\\left( 0, \\partial L(v)\\right) > 1.\\]\n We have $L(X(t))\\to 0$ so there exists $t_0\\geq 0$ such that for all $t\\geq t_0$, $0 1.\n\t\t\\end{equation*}\n\tGoing back to assumption~\\eqref{it::2}, for a.e. $t>0$, one has\n\t\t\\[ \\frac{\\diff L}{\\diff t} (X(t)) \\leq -c_1 \\Vert(\\partial L)(X(t))\\Vert^2, \\]\n\t\tbut the KL property implies that for a.e. $t>0$,\n\t\t$$- \\Vert \\partial L (X(t)) \\Vert^2 < -\\frac{1}{\\rho^2(1-a)^2} L(X(t))^{2a}.$$ Therefore,\n\t\t\\[\\frac{\\diff L}{\\diff t} (X(t)) < -\\frac{c_1}{\\rho^2(1-a)^2} L(X(t))^{2a}. \\]\n\t\t%\n\t\tWe consider two cases depending on the value of $a$. If $00$,\n\t\t\\begin{equation}\n\t\tL(X(t))^{-2a} \\frac{\\diff }{\\diff t}L(X(t)) =\\frac{1}{1-2a}\\frac{\\diff}{\\diff t} L(X(t))^{1-2a} < - \\frac{c_1}{ (\\rho^2 (1 - a)^2)},\n\t\t\\end{equation}\n\t\twith $1-2a<0$. We can integrate from $0$ to $t>0$:\n\t\t\\begin{equation*}\n\t\t \tL(X(t))^{1-2a}> \\frac{(2a -1)c_1}{\\rho^2(1-a)^2}t + L(X(0))^{1-2a}>\\frac{(2a -1)c_1}{\\rho^2(1-a)^2}t.\n\t\t\\end{equation*}\n\t\tSince $\\frac{1}{1-2a}<-1$, one obtains a convergence rate of the form $O\\left(t^\\frac{1}{1-2a}\\right)$. In both cases the rate is at least $O\\left(\\frac{1}{t}\\right)$.\n\t\n\t\n\t\t\n\tWe assume now that both \\eqref{it::2} and \\eqref{it::3} holds and prove convergence of the trajectory with a convergence rate. \n Let $t>s>0$, by the fundamental theorem of calculus and the triangular inequality,\n \t\\begin{equation}\\label{eqpr::dotX}\n \t\\Vert X(t) - X(s)\\Vert \\leq \\left\\| \\int_s^{t} \\dot{X}(\\tau) \\diff \\tau \\right\\| \\leq \\int_s^{t} \\Vert\\dot{X}(\\tau)\\Vert \\diff \\tau.\n \t\\end{equation}\n \tWe wish to bound $\\Vert \\dot{X}\\Vert$ using $L$. Using the chain rule (Lemma~\\ref{lem::chainrule} of Section~\\ref{sec:ResultsOnD}), for a.e. $\\tau>0$,\n \t\\begin{align}\n \t\\begin{split}\n \t\\frac{\\diff }{\\diff \\tau}L(X(\\tau))^{1-a} = (1-a)L(X(\\tau))^{-a} \\langle \\dot{X}(\\tau),(\\partial L)(X(\\tau))\\rangle.\n \t\\end{split}\n \t\\end{align}\n \tThen, from \\eqref{it::2}, we deduce that for a.e. $\\tau>0$,\n \t\\begin{align}\n \t\\begin{split}\n \t\\langle \\dot{X}(\\tau),(\\partial L)(X(\\tau))\\rangle = \\frac{\\diff L}{\\diff \\tau}(X(\\tau)) \\leq -c_1 \\Vert (\\partial L)(X(\\tau)) \\Vert^2,\n \t\\end{split}\n \t\\end{align}\n \tso\n \t\\begin{align}\\label{eq::dtL}\n \t\\begin{split}\n \t\\frac{\\diff }{\\diff \\tau}L(X(\\tau))^{1-a} \\leq -c_1(1-a)L(X(\\tau))^{-a} \\Vert (\\partial L)(X(\\tau)) \\Vert^2.\n \t\\end{split}\n \t\\end{align}\n \tThe KL property \\eqref{eq::SemiAlgKL} implies that for a.e. $\\tau>0$,\n \t\\begin{equation}\\label{eq::KLofL}\n \t-(1-a)L(X(\\tau))^{-a}\\Vert (\\partial L)(X(\\tau))\\Vert< -\\frac{1}{\\rho}.\n \t\\end{equation}\n \tPutting this in \\eqref{eq::dtL} and using assumption \\eqref{it::3} we finally obtain\n \t\\begin{align}\\label{eq::dtL2}\n \t\\begin{split}\n \t\\frac{\\diff }{\\diff t}L(X(\\tau))^{1-a} < -\\frac{c_1}{\\rho} \\Vert (\\partial L)(X(\\tau)) \\Vert\\leq -\\frac{c_1c_2}{\\rho} \\Vert \\dot{X}(\\tau)\\Vert.\n \t\\end{split}\n \t\\end{align}\n \tWe can use that in \\eqref{eqpr::dotX},\n \t\\begin{equation}\n \t\\begin{split}\\label{eq::boundX}\n \t\\Vert X(t) - X(s)\\Vert &\\leq - \\frac{\\rho}{c_1 c_2}\\int_s^{t} \\frac{\\diff }{\\diff t}L(X(\\tau))^{1-a} \\diff \\tau\\\\\n \t&= \\frac{\\rho}{c_1 c_2 } (L(X(s))^{1-a}-L(X(t))^{1-a}).\n \t\\end{split}\n \t\\end{equation}\n \tThen, using the convergence rate we already proved for $L$, we deduce that the Cauchy criterion holds for $X$ inside the compact (hence complete) subset $\\mathsf{C}\\subset\\mathbb{R}^P$ containing the trajectory. Thus, $X$ converges, and from (i) we have that $\\lim\\inf_{t \\to +\\infty} \\|\\partial L(X(t))\\| = 0$ because $\\partial L$ has closed graph. This shows that the limit is a critical points of $L$. Finally, taking the limit in \\eqref{eq::boundX} and using the convergence rate of $L$ we obtain a rate for $X$ as well.\n \\end{proof}\n\n \\begin{remark}\\label{rem::shift}\n {\\rm Theorem \\ref{th::genlemma} takes the form of a general recipe to obtain a convergence rate since it may be applied in many cases, to curves or flows, provided that a convenient Lyapunov function is given. \n Note also that it is sufficient for assumptions \\eqref{it::2} and \\eqref{it::3} to hold only after some time $t_0>0$ as in such case, one could simply do a time shift to use the theorem.} \n \\end{remark}\n\n\n \\subsection{Application to INDIAN}\\label{sec::rateIND}%\nWe now apply Theorem~\\ref{th::genlemma} to the deterministic continuous dynamical model of INDIAN \\eqref{eq::contdinClarke}.\n\\begin{theorem}[Convergence rates]\\label{cor::IndianRate}\n Suppose that $\\mathcal{J}$ is semi-algebraic locally Lipschitz continuous and lower bounded. Then, any bounded trajectory $(\\theta,\\psi)$ that solves \\eqref{eq::contdinClarke} converges to a point $(\\bar{\\theta},\\bar{\\psi}) \\in \\mathsf{S}$, with a convergence rate of the form $O\\left(t^{-b}\\right)$ with $b>0$. Moreover $\\mathcal{J}(\\theta(t))$ converges to its limit $\\bar \\mathcal{J}$ with rate $\\left\\vert\\mathcal{J}(\\theta(t))-\\bar \\mathcal{J}\\right\\vert=O\\left(\\frac{1}{t}\\right)$.\n\\end{theorem}\n\n\n\\begin{proof}\nLet $(\\theta,\\psi)$ be a bounded solution of \\eqref{eq::contdinClarke}. We would like to use Theorem~\\ref{th::genlemma} with $X =(\\theta,\\psi)$, and a well chosen function. In the proof of Theorem~\\ref{th::thmDIN} we proved a descent property along the trajectory for the function $E(\\theta,\\psi) = 2(1+\\alpha\\beta)\\mathcal{J}(\\theta) + \\left\\Vert (\\alpha-\\ovb)\\theta +\\ovb \\psi \\right\\Vert^2 $. This function is semi-algebraic, locally Lipschitz continuous, so it remains to prove that \\eqref{it::2} and \\eqref{it::3} hold for $E$ along $(\\theta,\\psi)$.\n\n For $t\\geq 0$, denote $w(t) = (\\alpha-\\frac{1}{\\beta})\\theta(t) +\\frac{1}{\\beta}\\psi(t)$, then according to Lemma~\\ref{lem::dEdt} for a.e. $t>0$,\n \\begin{align}\\label{eq::valofDT}\n \\begin{split}\n \\frac{\\diff E}{\\diff t}(\\theta(t),\\psi(t)) &=- \\Vert \\sqrt{\\alpha} \\dot{\\theta}(t) - \\frac{1}{\\sqrt{\\beta}} \\left( \\dot{\\psi}(t) - \\dot{\\theta}(t) \\right)\\Vert^2 - \\Vert \\sqrt{\\alpha} \\dot{\\theta}(t) + \\frac{1}{\\sqrt{\\beta}} \\left( \\dot{\\psi}(t) - \\dot{\\theta}(t) \\right)\\Vert^2\\\\\n &= -2\\alpha \\Vert \\dot{\\theta}(t)\\Vert^2 - \\frac{2}{\\beta} \\Vert \\dot{\\psi}(t) -\\dot{\\theta}(t) \\Vert^2\n = -2\\alpha \\Vert \\dot{\\theta}(t) \\Vert^2 - \\frac{2}{\\beta} \\Vert \\beta \\partial \\mathcal{J}(\\theta(t)) \\Vert^2\\\\\n &= -2\\alpha \\Vert -\\beta\\partial \\mathcal{J}(\\theta(t)) -w(t) \\Vert^2 - 2\\beta \\Vert \\partial \\mathcal{J}(\\theta(t)) \\Vert^2.\n \\end{split}\n \\end{align}\n On the other hand, by standard results on the sum rule, we have for all $(\\theta,\\psi)\\in\\mathbb{R}^P\\times\\mathbb{R}^P$,\n \\begin{equation}\n \\partial E(\\theta,\\psi) = 2\\begin{pmatrix}(1+\\alpha\\beta)\\partial \\mathcal{J}(\\theta) + (\\alpha-\\frac{1}{\\beta})\\left( (\\alpha-\\frac{1}{\\beta})\\theta + \\frac{1}{\\beta}\\psi\\right)\n \\\\ \\frac{1}{\\beta} \\left( (\\alpha-\\frac{1}{\\beta})\\theta + \\frac{1}{\\beta}\\psi\\right)\n \\end{pmatrix},\n \\end{equation}\n so for a.e. $t>0$,\n \\begin{align}\\label{eq::NormOfGrad}\n \\frac{\\left\\| \\partial E(\\theta(t),\\psi(t))\\right\\|^2}{4} = &\\left\\| (1+\\alpha\\beta)\\partial \\mathcal{J}(\\theta(t)) + (\\alpha-\\frac{1}{\\beta})w(t) \\right\\|^2 + \\left\\| \\frac{1}{\\beta} w(t) \\right\\|^2.\n \\end{align}\n We wish to find $c_1>0$, such that $\\frac12\\frac{\\diff E}{\\diff t} + \\frac{c_1}{4}\\Vert \\partial E\\Vert^2 < 0$. This follows from the following claim.\n\n \\textit{Claim}: let $r_1>0$, $r_2\\in\\mathbb{R}$, $r_3>0$, then there exist $C_1$ and $C_2$ two positive constants such that for any $a,b\\in\\mathbb{R}$,\n \\begin{equation}\\label{eq::analysisinequality}\n C_1(a^2+b^2) \\leq (r_1 a +r_2 b)^2 + r_3 b^2 \\leq C_2(a^2+b^2).\n \\end{equation}\n Indeed, the function $Q:(a,b) \\mapsto (r_1 a +r_2 b)^2 + r_3 b^2$ is a positive definite quadratic form, $C_1$ and $C_2$ can be taken to be two eigenvalues of the positive definite matrix which represents $Q$. Hence \\eqref{eq::analysisinequality} holds for all $a$ and $b$.\n\n\n Applying the previous claim to \\eqref{eq::NormOfGrad} and \\eqref{eq::valofDT} leads to the existence of $c_1>0$ such that for a.e. $t>0$,\\begin{equation*}\n \\frac{\\diff E}{\\diff t}(\\theta(t),\\psi(t))\\leq - c_1 \\Vert \\partial E (\\theta(t),\\psi(t))\\Vert^2,\n \\end{equation*} so assumption \\eqref{it::2} holds for INDIAN.\n\n It now remains to show that \\eqref{it::3} of Theorem~\\ref{th::genlemma} holds i.e. that there exists $c_2>0$ such that for $(\\theta,\\psi)$ solution of \\eqref{eq::contdinClarke} and for a.e. $t>0$, $\\Vert \\partial E(\\theta(t),\\psi(t))\\Vert^2\\geq c_2 \\left( \\Vert \\dot{\\theta}(t)\\Vert^2 + \\Vert \\dot{\\psi}(t)\\Vert^2 \\right)$.\n Using \\eqref{eq::contdinClarke} and \\eqref{eq::NormOfGrad} we obtain:\n \\begin{align}\\label{eq::assum3}\n \\begin{split}\n \\frac{\\Vert \\partial E(\\theta(t),\\psi(t))\\Vert^2}{4} =\\left\\| \\frac{1}{\\beta}(1+\\alpha\\beta)\\dot{\\theta}(t) + \\left[(\\alpha-\\frac{1}{\\beta}) -\\frac{1}{\\beta}(1+\\alpha\\beta)\\right]\\dot{\\psi}(t)\\right\\|^2 + \\frac{1}{\\beta^2}\\Vert\\dot{\\psi}(t)\\Vert^2 \n \\end{split},\n \\end{align}\n and applying the claim \\eqref{eq::analysisinequality} again to \\eqref{eq::assum3} one can show that there exist $c_2>0$, such that for a.e. $t>0$, \\[\\Vert \\partial E(\\theta(t),\\psi(t))\\Vert^2\\geq c_2 \\left( \\Vert \\dot{\\theta}(t)\\Vert^2 + \\Vert \\dot{\\psi}(t)\\Vert^2 \\right).\\] So assumption \\eqref{it::3} holds for \\eqref{eq::contdinClarke}. To conclude, we can apply Theorem~\\ref{th::genlemma} to \\eqref{eq::contdinClarke} and the proof is complete.\n \\end{proof}\n\n\n \\begin{remark}\\label{rem::rateofIND}\n \\rm{(a) \\new{Since the discrete algorithm INDIAN asymptotically resembles to its continuous time version (see the proof of Theorem~\\ref{th::thmDIN}), the results above suggest that similar behaviors and rates could be hoped for INDIAN itself. Yet, these results remain difficult to obtain in the case of DL, in particular in the mini-batch setting because of the noise $(\\xi_k)_{k\\in\\mathbb{N}}$.}\\\\\n (b) The proof above is significantly simpler when $\\alpha\\beta>1$ since \\cite{alvarez2002second} proved that in this case, \\eqref{eq::contdinClarke} is equivalent to a gradient system, thus assumptions \\eqref{it::2} and \\eqref{it::3} of Theorem~\\ref{th::genlemma} instantly holds.}\\\\\n \\rm{ (c) Theorems \\ref{th::genlemma} and \\ref{cor::IndianRate} can be adapted to the case when the Clarke subdifferential is replaced by $D\\mathcal{J}$, but we do not state it here for the sake of simplicity.}\\\\\n \\rm{ (d) \\new{Theorems~\\ref{th::genlemma} and~\\ref{cor::IndianRate} are actually valid by assuming that $\\mathcal{J}$ belongs to a polynomially bounded o-minimal structure. One of the most common instance of such structures is the one given by globally subanalytic sets (as illustrated in a example below). We refer to \\cite{bolte2007lojasiewicz} for a definition and further references.}}\n \\end{remark}\n \n \\new{Let us now comment the results of Theorem~\\ref{cor::IndianRate}. First, we restrained here to semi-algebraic loss functions $\\mathcal{J}$, which are a subset of tame loss functions. Most networks, activations and dissimilarity measures mentioned in Section~\\ref{sec::favstruct} fall into this category. Nonetheless, the loss functions of the DL experiments of Section~\\ref{sec::trainDL} are not semi-algebraic. Indeed, the dissimilarity measure $l$ used is the cross-entropy: $l(f(x_n,\\theta),y_n)= - \\sum_{d=1}^D \\mathbf{1}_{[y_{n}]_d=1}\\log( [f(x_n,\\theta)]_d)$. Such a function cannot be described by polynomials and presents a singularity whenever $[f(x_n,\\theta)]_d=0$. Fortunately, due to the numerical precision but also to the ``soft-max'' functions often used in classification experiments, the outputs of the network $f$, for inputs restricted to a compact set, have values in $[\\varepsilon,1]$ for some small $\\varepsilon>0$. Therefore, the singularity at $0$ is harmless and the cross-entropy acts as a globally subanalytic function. As a consequence the nonsmooth {\\L}ojasiewicz inequality holds, and the theorems apply (see also numerical experiments).\n \n The rate of convergence of the trajectory in Theorem~\\ref{cor::IndianRate} is non-explicit in the sense that the exponent $b>0$ is unknown in general. In the light of the proof of Theorem~\\ref{th::genlemma}, this exponent depends on the KL exponent $a$ of the Lyapunov function, which is itself hard to determine in practice. However the intuition is that small exponents $a$ may yield faster convergence rates (indeed, when $a\\in(0,1\/2)$ we actually have a linear rate). As an example, for the function: $t\\in\\mathbb{R}\\mapsto \\vert t \\vert^c$ with $c>1$, the exponent at $0$ is $a=1-\\frac{1}{c}$ and thus, the closer \n $c$ is to $1$, the smaller $a$ is, and the faster the convergence becomes.\n }\n \n\n \\section{Experiments}\n\t\\label{sec:numerics}\n\nIn this section we first discuss the role and influence of the hyper-parameters of INDIAN using the 2D example given in Figure~\\ref{fig::rosenbrockexp}. We then compare INDIAN with SGD, ADAGRAD and ADAM on deep learning problems for image recognition.\n\n\t\\subsection{Understanding the Role of the Hyper-parameters of INDIAN}\\label{sec::HPmeaning}\n Both hyper-parameters $\\alpha$ and $\\beta$ can be seen as damping coefficients from the viewpoint of mechanics as discussed by \\cite{alvarez2002second} and sketched in the introduction. Recall the second-order time continuous dynamics which served as a model to the design INDIAN:\n\t \\begin{equation*}\n \t \\ddot{\\theta}(t)+\\alpha\\,\\dot{\\theta}(t) + \\beta\\, \\nabla^2 \\mathcal{J}(\\theta(t))\\dot{\\theta}(t) + \\nabla \\mathcal{J}(\\theta(t))=0.\n\t \\end{equation*}\n\tThis differential equation was inspired by Newton's Second Law of dynamics asserting that the acceleration of a material point coincides with the sum of forces applied to the particle. As recalled in the introduction three forces are at stake: the gravity and two friction terms. The parameter $\\alpha$ calibrates the {\\em viscous damping} intensity as in the Heavy Ball friction method of \\cite{polyak1964some}. It acts as a dissipation term but it can also be seen as a proximity parameter of the system with the usual gradient descent: the higher $\\alpha$ is, the more DIN behaves like a pure gradient descent.\\footnote{This is easier to see when one rescales $\\mathcal{J}$ by $\\alpha$.} On the other hand the parameter $\\beta$ can be seen as a \\textit{Newton damping} which takes into account the geometry of the landscape to brake or accelerate the dynamics in an adaptive anisotropic fashion, see \\cite{felipe,alvarez2002second} for further insights.\n\n\n\tWe now turn our attention to INDIAN, and illustrate the versatility of the hyper-parameters $\\alpha$ and $\\beta$ in this case. We proceed on a 2D visual nonsmooth ill-conditioned example \\`a la Rosenbrock, see Figure~\\ref{fig::rosenbrockexp}. For this example, we aim at finding the minimum of the function $\\mathcal{J}(\\theta_1,\\theta_2)= 100(\\theta_2-\\vert \\theta_1 \\vert)^2 +\\vert 1-\\theta_1\\vert $. This function has a {\\em V}-shaped valley, and a unique critical point at $(1,1)$ which is also the global minimum. Starting from the point $(-1,1.5)$ (the black cross), we apply INDIAN with constant steps $\\gamma_k=10^{-4}$. Figure \\ref{fig::rosenbrockexp} shows that when $\\beta$ is too small, the trajectory presents many transverse oscillations as well as longitudinal ones close to the critical point (subplot (a)). Then, increasing $\\beta$ significantly reduces transverse oscillations (subplot (b)). Finally, the longitudinal oscillations are reduced by choosing a higher $\\alpha$ (subplot (c)). In addition, these behaviors are also reflected in the values of the objective function (subplot (d)).\n\tThe orange curve (first setting) presents large oscillations. Moreover, looking at the red curve, corresponding to plot (c), there is a short period between $20,000$ and $60,000$ iterations when the decrease is slower than for the other values of $\\alpha$ and $\\beta$, but still it presents fewer oscillations. In the longer term, the third choice ($\\alpha=1.3$, $\\beta=0.1$) provides remarkably good performance\n \n \\new{The choice of these hyper-parameters may come with rates of convergence for convex and strongly convex smooth functions \\citep{attouch2019first}. Following this work, one may also consider to make $\\alpha$ and $\\beta$ vary in time (for example like the famous Nesterov damping $\\frac{\\alpha}{t}$). In our DL experiments we will however keep these parameters constant so that our theorems still hold. Yet, different behaviors depending on $(\\alpha,\\beta)$ can also be observed for DL problems as illustrated on Figure~\\ref{fig::Multiparam} and described next.}\n \n\n\t\\subsection{Training a DNN with INDIAN}\\label{sec::trainDL}\n\t\n\n\tBefore comparing INDIAN to state-of-the-art algorithms in DL, we first describe the methodology we followed.\n\t\n\t\n\t\\subsubsection{Methodology}\n\t\\begin{itemize}\n\t \\renewcommand\\labelitemi{--}\n\n\t \\item We train a DNN for classification using the three most common image data sets (MNIST, CIFAR10, CIFAR100) \\citep{lecun1998gradient,krizhevsky2009learning}. These data sets are composed of $60,000$ small images associated with a label (numbers, objects, animals, etc.). We split the data sets into $50,000$ images for training and $10,000$ for testing.\n\t \\item \tRegarding the network, we use a slightly modified version of the popular Network in Network (NiN) \\citep{lin2013network}. It is a reasonably large convolutional network with $P\\sim10^6$ parameters to optimize. We use ReLU activation functions.\n\t \\item The dissimilarity measure $l$ that is used in the empirical loss $\\mathcal{J}$ given by \\eqref{eq::loss} is set to the cross-entropy.\n\t The loss $\\mathcal{J}$ is optimised with respect to $\\theta$ (the weights of the DNN) on the training data. The classification accuracy of the trained DNN is measured using the test data of $10,000$ images. Measuring the accuracy boils down to counting how many of the $10,000$ were correctly classified (in percentage).\n\t \\item \tBased on the results of Section \\ref{sec::HPmeaning}, we run INDIAN for four different values of $(\\alpha,\\beta)$: \\[(\\alpha,\\beta)\\in \\left\\{(0.1,0.1),(0.5,0.1),(0.5,0.5),(0.5,1)\\right\\}.\\]\n \tGiven an initialisation of the weights $\\theta_0$, we \n \tinitialize $\\psi_0$ such that the initial\n \tvelocity is in the direction of $-\\nabla \n \t\\mathcal{J}(\\theta_0)$. More precisely, we use $ \\psi_0 = \n \t(1-\\alpha\\beta)\\theta_0 \n \t-(\\beta^2-\\beta)\\nabla \\mathcal{J}(\\theta_0)$.\n \t\\item We compare our algorithm INDIAN with the classical SGD algorithm and the popular ADAGRAD \\citep{duchi2011adaptive} and ADAM\\citep{kingma2014adam} algorithms. At each iteration $k$, we compute the approximation of $\\partial \\mathcal{J}(\\theta)$ on a subset $\\mathsf{B}_k\\subset\\left\\{1,\\ldots,50,000\\right\\}$ of size $32$. The algorithms are initialized with the same random weights (drawn from a normal distribution). Five random initializations are considered for each experiment.\n \t%\n \t\\item Regarding the selection of step sizes, ADAGRAD and ADAM both use an adaptive procedure based on past gradients, see \\cite{duchi2011adaptive,kingma2014adam}. For the other two algorithms (INDIAN and SGD), we use the classical step size schedule $\\gamma_k=\\frac{\\gamma_0}{\\sqrt{k+1}}$, which meets Assumption~\\ref{ass:mainAssumption}. For all four algorithms, choosing the right initial step length $\\gamma_0$ is often critical in terms of efficiency. \\new{We choose this $\\gamma_0$ using a grid-search: for each algorithm we select the initial step size that most decreases the training error $\\mathcal{J}$ after fifteen epochs (one epoch consisting in a complete pass over the data). The test data is not used to chose the initial step size nor other hyper-parameters.} Note that we could use more flexible step size schedules but chose a standard schedule for simplicity. Other decay schemes are considered in Figure~\\ref{fig::decay}.\n \t\n \t\n\t\\end{itemize}\n For these experiments, we used \\texttt{Keras 2.2.4} \\citep{chollet2015} with \\texttt{Tensorflow 1.13.1} \\citep{abadi2016tensorflow} as backend. The INDIAN algorithm is available in Pytorch, Keras and Tensorflow: \\url{https:\/\/github.com\/camcastera\/Indian-for-DeepLearning\/} \\citep{castera2019github}.\n\n\t\\subsubsection{Results}\\label{sec:numreslenet}\n \n Figure~\\ref{fig::Multiparam} displays the training loss $\\mathcal{J}$ and test accuracy with respect to epochs for INDIAN in its four considered hyper-parameter configurations and for the three data sets considered. Figure~\\ref{fig::vsothalgo} displays the performance of INDIAN \\new{with the hyper-parameter configuration that led to smallest average training error in Figure~\\ref{fig::Multiparam}}, with comparison to SGD, ADAGRAD and ADAM. In these two figures (and also in subsequent Figure~\\ref{fig::decay}), solid lines represent mean values and pale surfaces represent the best and worst runs in terms of training loss and validation accuracy over five random initializations.\n \n \n \n \\begin{figure}[t]\n \t\\begin{center}\n \\begin{minipage}[c]{0.33\\textwidth}\n \t\t\t\t\\centering \\footnotesize (a) CIFAR-10\n \t\t\t\t\\end{minipage}%\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\n \t\t\t\t\\centering \\footnotesize (b) CIFAR-100\n \t\t\t\t\\end{minipage}%\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\n \t\t\t\t\\centering \\footnotesize (c) MNIST\n \t\t\t\t\\end{minipage}%\n\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\n \\centering\n \t\t\t\t\t\t\\includegraphics[clip, trim=0.40cm 0.5cm 0.45cm 0.3cm, width=0.95\\linewidth]{Figures\/EXP_DL_2\/NiN_otheralgo_CIFAR10.pdf}\n \t\t\t\t\\end{minipage}%\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\n \t\t\t\t\\centering\n \t\t\t\t\t\t\\includegraphics[clip, trim=0.40cm 0.5cm 0.35cm 0.3cm, width=0.95\\linewidth]{Figures\/EXP_DL_2\/NiN_otheralgo_CIFAR100.pdf}\n \t\t\t\t\\end{minipage}%\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\n\t\t\t\t\t\t\\centering\\includegraphics[clip, trim=0.40cm 0.5cm 0.35cm 0.3cm, width=0.95\\linewidth]{Figures\/EXP_DL_2\/NiN_otheralgo_MNIST.pdf}\n\t\t\t\t\\end{minipage}%\n\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\\centering\\includegraphics[clip, trim=0.2cm 0.5cm 0.35cm 0.3cm, width=0.95\\linewidth]{Figures\/EXP_DL_2\/NiN_otheralgo_VAL_CIFAR10.pdf}\n \t\t\t\t\\end{minipage}%\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\\centering\\includegraphics[clip, trim=0.5cm 0.5cm 0.3cm 0.5cm, width=0.95\\linewidth]{Figures\/EXP_DL_2\/NiN_otheralgo_VAL_CIFAR100.pdf}\n\t\t\t\t\\end{minipage}%\n\t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\\centering\\includegraphics[clip, trim=0.5cm 0.5cm 0.3cm 0.5cm, width=0.95\\linewidth]{Figures\/EXP_DL_2\/NiN_otheralgo_VAL_MNIST.pdf}\n \t\t\t\t\\end{minipage}%\n\n \t\t\t \t\\begin{minipage}[c]{0.8\\linewidth}\n \t\t\t \t\\centering\\includegraphics[width=0.45\\linewidth]{Figures\/EXP_DL_2\/legend_indian_hor.pdf}\n \t\t\t \t\\end{minipage}%\n \t\t\t \t\\caption{Analysis of the sensibility of INDIAN to the choice of $\\alpha$ and $\\beta$ using NiN for three different image classification problems. Top: logarithm of the loss function $\\mathcal{J}(\\theta)$ during the training. Bottom: classification accuracy on the test set.}\\label{fig::Multiparam}\n \t\t\t \t\\end{center}\n \t\t\\end{figure}\n\n \t\tFigure~\\ref{fig::Multiparam} suggests that the tuning of the hyper-parameters $\\alpha$ and $\\beta$ is not crucial to obtain satisfactory results both for training and testing. It mostly affects the training speed. Thus, INDIAN looks quite stable with respect to these hyper-parameters. Setting $(\\alpha,\\beta)=(0.5,0.1)$ appears to be a good default choice when necessary. Nevertheless, tuning these hyper-parameters is of course advised to get the most from INDIAN.\n \t\t\n \t\t\\begin{figure}[t]\n \t\\begin{center}\n \\begin{minipage}[c]{0.33\\textwidth}\n \t\t\t\t\\centering \\footnotesize (a) CIFAR-10\n \t\t\t\t\\end{minipage}%\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\n \t\t\t\t\\centering \\footnotesize (b) CIFAR-100\n \t\t\t\t\\end{minipage}%\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\n \t\t\t\t\\centering \\footnotesize (c) MNIST\n \t\t\t\t\\end{minipage}%\n\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\n \\centering\n \t\t\t\t\t\t\\includegraphics[clip, trim=0.40cm 0.5cm 0.45cm 0.3cm, width=0.95\\linewidth]{Figures\/EXP_DL_1\/NiN_otheralgo_CIFAR10.pdf}\n \t\t\t\t\\end{minipage}%\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\n \t\t\t\t\\centering\n \t\t\t\t\t\t\\includegraphics[clip, trim=0.40cm 0.5cm 0.35cm 0.3cm, width=0.95\\linewidth]{Figures\/EXP_DL_1\/NiN_otheralgo_CIFAR100.pdf}\n \t\t\t\t\\end{minipage}%\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\n\t\t\t\t\t\t\\centering\\includegraphics[clip, trim=0.40cm 0.5cm 0.35cm 0.3cm, width=0.95\\linewidth]{Figures\/EXP_DL_1\/NiN_otheralgo_MNIST.pdf}\n\t\t\t\t\\end{minipage}%\n\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\\centering\\includegraphics[clip, trim=0.2cm 0.5cm 0.35cm 0.3cm, width=0.95\\linewidth]{Figures\/EXP_DL_1\/NiN_otheralgo_VAL_CIFAR10.pdf}\n \t\t\t\t\\end{minipage}%\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\\centering\\includegraphics[clip, trim=0.5cm 0.5cm 0.3cm 0.5cm, width=0.95\\linewidth]{Figures\/EXP_DL_1\/NiN_otheralgo_VAL_CIFAR100.pdf}\n\t\t\t\t\\end{minipage}%\n\t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\\centering\\includegraphics[clip, trim=0.5cm 0.5cm 0.3cm 0.5cm, width=0.95\\linewidth]{Figures\/EXP_DL_1\/NiN_otheralgo_VAL_MNIST.pdf}\n \t\t\t\t\\end{minipage}%\n\n \t\t\t \t\\begin{minipage}[c]{0.8\\linewidth}\n \t\t\t \t\\centering\\includegraphics[width=0.45\\linewidth]{Figures\/EXP_DL_1\/legend_other_hor.pdf}\n \t\t\t \t\\end{minipage}%\n \t\t\t \t\\caption{Comparison of INDIAN with state-of-the-art algorithms SGD, ADAM and ADAGRAD. Top: logarithm of the loss function $\\mathcal{J}(\\theta)$ during the training. Bottom: classification accuracy on the test set.\n \t\t\t \t}\n \t\t\t \t\\label{fig::vsothalgo}\n \t\t\t \t\\end{center}\n \t\t\\end{figure}\n \t\nFigure~\\ref{fig::vsothalgo} shows that best performing methods achieve state-of-the art accuracy using NiN and represent what can be achieved with a moderately large network and coarse grid-search tuning of the initial step size. In our comparison, INDIAN and ADAM outperform SGD and ADAGRAD for training. While ADAM seems to be faster in the early training phase, INDIAN achieves the best accuracy almost every time especially on CIFAR-100 (Figure~\\ref{fig::vsothalgo}(b)). Thus INDIAN appears to be competitive in comparison to the other algorithms with the advantage of having solid theoretical foundations and a simple step-size rule as compared to ADAM and ADAGRAD.\n\n \t\t\n \t\t\\begin{figure}[t]\n \t\\begin{center}\n \\begin{minipage}[c]{0.33\\textwidth}\n \t\t\t\t\\centering \\footnotesize (a) CIFAR-10\n \t\t\t\t\\end{minipage}%\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\n \t\t\t\t\\centering \\footnotesize (b) CIFAR-100\n \t\t\t\t\\end{minipage}%\n \\begin{minipage}[c]{0.33\\textwidth}\n \t\t\t\t\\centering \\footnotesize (c) MNIST\n \t\t\t\t\\end{minipage}%\n\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\n \\centering\n \t\t\t\t\t\t\\includegraphics[clip, trim=0.40cm 0.5cm 0.45cm 0.3cm, width=0.95\\linewidth]{Figures\/EXP_DL_3\/NiN_decay_CIFAR10.pdf}\n \t\t\t\t\\end{minipage}%\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\n \t\t\t\t\\centering\n \t\t\t\t\t\t\\includegraphics[clip, trim=0.40cm 0.5cm 0.35cm 0.3cm, width=0.95\\linewidth]{Figures\/EXP_DL_3\/NiN_decay_CIFAR100.pdf}\n \t\t\t\t\\end{minipage}%\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\n \t\t\t\t\\centering\n \t\t\t\t\t\t\\includegraphics[clip, trim=0.40cm 0.5cm 0.35cm 0.3cm, width=0.95\\linewidth]{Figures\/EXP_DL_3\/NiN_decay_MNIST.pdf}\n \t\t\t\t\\end{minipage}%\n\n \t\t\t \t\\begin{minipage}[c]{0.8\\linewidth}\n \t\t\t \t\\centering\\includegraphics[width=0.4\\linewidth]{Figures\/EXP_DL_3\/legend_decres_hor.pdf}\n \t\t\t \t\\end{minipage}%\n \t\t\t \t\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\n \\centering\n \t\t\t\t\t\t\\includegraphics[clip, trim=0.40cm 0.5cm 0.45cm 0.3cm, width=0.95\\linewidth]{Figures\/EXP_DL_4\/indian_vs_adam_CIFAR10.pdf}\n \t\t\t\t\\end{minipage}%\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\n \t\t\t\t\\centering\n \t\t\t\t\t\t\\includegraphics[clip, trim=0.40cm 0.5cm 0.35cm 0.3cm, width=0.95\\linewidth]{Figures\/EXP_DL_4\/indian_vs_adam_CIFAR100.pdf}\n \t\t\t\t\\end{minipage}%\n \t\t\t\t\\begin{minipage}[c]{0.33\\textwidth}\n \t\t\t\t\\centering\n \t\t\t\t\t\t\\includegraphics[clip, trim=0.40cm 0.5cm 0.35cm 0.3cm, width=0.95\\linewidth]{Figures\/EXP_DL_4\/indian_vs_adam_MNIST.pdf}\n \t\t\t\t\\end{minipage}%\n\n \t\t\t \t\\begin{minipage}[c]{0.8\\linewidth}\n \t\t\t \t\\centering\\includegraphics[width=0.4\\linewidth]{Figures\/EXP_DL_4\/legend_ind_vs_adam_hor.pdf}\n \t\t\t \t\\end{minipage}%\n \t\t\t \t\\caption{On top: Training loss of INDIAN on three image classification problems with various step size decays. In the legend, $k^{-q}$ means a step size decay at iteration $k$ of the form $\\gamma_k = \\gamma_0 k^{-q}$. The bottom row show the comparison between INDIAN with a well-chosen step size decay and ADAM.}\\label{fig::decay}\n \t\t\t \t\\end{center}\n \t\t\\end{figure}\n\n Finally let us point out that although ADAM was faster in the experiments of Figure~\\ref{fig::vsothalgo}, INDIAN can outperform ADAM using the slow step size decay discussed in Section~\\ref{sec::comRes}. Indeed, in the previous experiments we used a standard decreasing step size of the form $\\gamma_0 \/ \\sqrt{k+1}$ for simplicity, but Assumption~\\ref{ass:mainAssumption} allows for step sizes decreasing much slower. As such, we also considered decays of the form $\\gamma_0 (k+1)^{-q}$ with $q\\leq 1\/2$. The results are displayed on top of Figure~\\ref{fig::decay}. \n Except when $q$ is too small (too slow decay, e.g., $q=1\/16$), these results show that some decays slower than $q=1\/2$ make INDIAN a little bit faster than any of the other algorithms we tried. In particular, with a step size decay proportional to $k^{-1\/4}$, INDIAN outperforms ADAM (bottom of Figure~\\ref{fig::decay}). This suggests that tuning $q$ can also significantly accelerate the training process.\n \n \n\n\t\\section{Conclusion}\n\tWe introduced a novel stochastic optimization algorithm featuring inertial and Newtonian behavior motivated by applications to deep learning. We provided a powerful algorithmic convergence analysis under weak hypotheses applicable to most DL problems. We also provided new general results to study differential inclusions on Clarke subdifferential and obtain convergence rates for the continuous time counterpart of our algorithm. We would like to point out that, apart from SGD \\citep{davis2018stochastic}, the convergence of concurrent methods in such a general setting is still an open question. Our result seems moreover to be the first one to be able to rigorously handle the analysis of mini-batch subsampling for ReLU DNNs via the introduction of the $D$-critical points. Our experiments show that INDIAN is very competitive with state-of-the-art algorithms for DL but also very malleable. We stress that these numerical manipulations were performed on substantial DL benchmarks with only limited algorithm tuning. \n\t\n\tThis facilitates reproducibility and allows to stay as close as possible to the reality of DL applications in machine learning.\n\t\n \t\\acks{\n The authors acknowledge the support of the European Research Council (ERC FACTORY-CoG-6681839), the Agence Nationale de la Recherche (ANR 3IA-ANITI, ANR-17-EURE-0010 CHESS, ANR-19-CE23-0017 MASDOL) and the Air Force Office of Scientific Research (FA9550-18-1-0226).\n\n\n Part of the numerical experiments were done using the OSIRIM platform of IRIT, supported by the CNRS, the FEDER, R\u00e9gion Occitanie and the French government (\\url{http:\/\/osirim.irit.fr\/site\/en}). We thank the development team of the following libraries that were used in the experiments: Python \\citep{rossum1995python}, Numpy \\citep{walt2011numpy}, Matplotlib \\citep{hunter2007matplotlib}, Pytorch \\citep{paszke2019pytorch}, Tensorflow and Keras \\citep{abadi2016tensorflow,chollet2015}.\n \n \n \t We thank Hedy Attouch and Sixin Zhang for useful discussions.\n }%\n\n \n\n\n\n\n\n \\DeclareRobustCommand{\\VAN}[3]{#3}\n\t","meta":{"redpajama_set_name":"RedPajamaArXiv"}} diff --git a/data_all_eng_slimpj/shuffled/split2/finalzzdqvq b/data_all_eng_slimpj/shuffled/split2/finalzzdqvq new file mode 100644 index 0000000000000000000000000000000000000000..cfaad1a718d004e202019bb2d19a83603a06cdf5 --- /dev/null +++ b/data_all_eng_slimpj/shuffled/split2/finalzzdqvq @@ -0,0 +1,5 @@ +{"text":"\\section{Introduction}\nFree probability theory was introduced by Voiculescu around 1983 motivated by \nthe isomorphism problem of von Neumann algebras of free groups. He developed a noncommutative probability theory, on a noncommutative\nprobability space, in which a new notion of freeness plays the\nrole of independence in classical probability. Around 1991, Voiculescu \\cite{V} threw a bridge connecting random matrix theory with free probability \nsince he realized that the freeness property is also present for many classes of random matrices, in the\nasymptotic regime when the size of the matrices tends to infinity. Since then, random matrices have played a key role in operator algebra whereas tools developed in operator algebras and free\nprobability theory could now be applied to random matrix problems.\\\\\nFor the reader's convenience, we recall the following basic definitions from free probability theory. For a thorough introduction to free probability theory, we refer to \\cite{VDN}.\n\\begin{itemize}\n\\item A ${\\cal C}^*$-probability space is a pair $\\left({\\cal A}, \\tau\\right)$ consisting of a unital $ {\\cal C}^*$-algebra ${\\cal A}$ and \na linear map $\\tau: {\\cal A}\\rightarrow \\mathbb{C}$ such that $\\tau(1_{\\cal A})=1$ and $\\tau(aa^*)\\geq 0$ for all $a \\in {\\cal A}$. $\\tau$ is a trace if it satisfies $\\tau(ab)=\\tau(ba)$ for every $(a,b)\\in {\\cal A}^2$. A trace is said to be faithful if $\\tau(aa^*)>0$ whenever $a\\neq 0$. \nAn element of ${\\cal A}$ is called a noncommutative random variable. \n\\item The noncommutative distribution of a family $a=(a_1,\\ldots,a_k)$ of noncommutative random variables in a ${\\cal C}^*$-probability space $\\left({\\cal A}, \\tau\\right)$ is defined as the linear functional $\\mu_a:P\\mapsto \\tau(P(a,a^*))$ defined on the set of polynomials in $2k$ noncommutative indeterminates, where $(a,a^*)$ denotes the $2k$-tuple $(a_1,\\ldots,a_k,a_1^*,\\ldots,a_k^*)$.\nFor any self-adjoint element $a_1$ in ${\\cal A}$, there exists a probability measure $\\nu_{a_1}$ on $\\mathbb{R}$ such that, for every polynomial P, we have\n$$\\mu_{a_1}(P)=\\int P(t) \\mathrm{d}\\nu_{a_1}(t).$$\nThen, we identify $\\mu_{a_1}$ and $\\nu_{a_1}$. If $\\tau$ is faithful then the support of $\\nu_{a_1}$ is the spectrum of $a_1$ and thus $\\|a_1\\| = \\sup\\{|z|, z\\in \\rm{support} (\\nu_{a_1})\\}$. \n\\item A family of noncommutative random variables $(a_i)_{i\\in I}$ in a ${\\cal C}^*$-probability space $\\left({\\cal A}, \\tau\\right)$ is free if for all $k\\in \\mathbb{N}$ and all polynomials $p_1,\\ldots,p_k$ in two noncommutative indeterminates, one has \n\\begin{equation}\\label{freeness}\n\\tau(p_1(a_{i_1},a_{i_1}^*)\\cdots p_k (a_{i_k},a_{i_k}^*))=0\n\\end{equation}\nwhenever $i_1\\neq i_2, i_2\\neq i_3, \\ldots, i_{k-1}\\neq i_k$ and $\\tau(p_l(a_{i_l},a_{i_l}^*))=0$ for $l=1,\\ldots,k$.\n\\item A family $(x_i)_{i\\in I}$ of noncommutative random variables in a ${\\cal C}^*$-probability space $\\left({\\cal A}, \\tau\\right)$ is a semicircular system if\n $x_i=x_i^*$ for all $i\\in I$, $(x_i)_{i\\in I}$\nis a free family and for any $k\\in \\mathbb{N}$, $$\\tau(x_i^k)= \\int t^k d\\mu_{sc}(t)$$\nwhere $d\\mu_{sc}(t)=\n\\frac{1}{2\\pi} \\sqrt{4-t^2}{\\1}_{[-2;2]}(t) dt$ is the semicircular standard distribution.\n\\item Let $k$ be a nonnull integer number. Denote by ${\\cal P}$ the set of polynomials in $2k $ noncommutative indeterminates.\nA sequence of families of variables $ (a_n)_{n\\geq 1} =\n(a_1(n),\\ldots, a_k(n))_{n\\geq 1}$ in ${\\cal C}^* $-probability spaces \n$\\left({\\cal A}_n, \\tau_n\\right)$ converge, when n goes to infinity, respectively in distribution if the map \n$P\\in {\\cal P} \\mapsto\n\\tau_n(\nP(a_n,a_n^*))$ converges pointwise\nand strongly in distribution if moreover the map \n$P\\in {\\cal P} \\mapsto \\Vert P(a_n,a_n^*) \\Vert$ converges pointwise.\n\\end{itemize}\n\n\nVoiculescu considered random matrices in this noncommutative probability context. Let ${\\cal A}_n$ be \nthe algebra of $n\\times n$ matrices\nwhose entries are random variables with finite moments and endow this algebra with\nthe expectation of the normalized trace defined for any $M\\in {\\cal A}_n$ by \n$\\tau_n(M) = \\mathbb{E}[\\frac{1}{n}\\Tr(M)]$. Let us \nconsider r independent $n\\times n$ so-called G.U.E matrices, that is to say random Hermitian matrices $X_n^{(v)} = [X^{(v)}_{jk}]_{j,k=1}^n$, $v=1,\\ldots,r$, for which the random variables $(X^{(v)}_{ii})$,\n$(\\sqrt{2} Re(X^{(v)}_{ij}))_{i0$ and an integer number $n_0>0$ such that, for any $x >x_0$ and any integer number $n >n_0$, we have\n\\begin{equation}\\label{condition}\\frac{1}{n^2} \\sum_{1\\leq i,j\\leq n}\\mathbb{P}\\left( \\vert X^{(v)}_{ij}\\vert >x\\right) \\leq K_v\\mathbb{P}\\left(\\vert Z^{(v)} \\vert>x\\right).\\end{equation}\n\\item $$\\sup_{1\\leq i0$ such that, for any large $n$, $[b-\\delta,c+\\delta]$ lies outside the support of the distribution of the noncommutative random variable $ P\\left(x_1,\\ldots,x_r, a_n^{(1)},\\ldots,a_n^{(t)},(a_n^{(1)})^*,\\ldots,(a_n^{(t)})^*\\right)$ in $({\\cal A},\\tau)$.\n Then, \nalmost surely, for all large $n$, there is no eigenvalue of the $n\\times n$ matrix $ P\\left(\\frac{X_n^{(1)}}{\\sqrt{n}},\\ldots,\\frac{X_n^{(r)}}{\\sqrt{n}}, A_n^{(1)},\\ldots,A_n^{(t)}, (A_n^{(1)})^*,\\ldots,(A_n^{(t)})^*\\right)$ in $[b,c].$\n\\end{theoreme}\n\\begin{remarque}\nWhen $r=t=1$, $A_n^{(1)}=(A_n^{(1)})^*$ and $P(X_1,X_2,X_2^*)=X_1+\\frac{X_2+X_2^*}{2}$, the distribution of $P(x_1,a_n^{(1)},(a_n^{(1)})^*)$ is the so-called free convolution $\\mu_{sc}\\boxplus \\mu_{A_n^{(1)}}$ where $\\mu_{A_n^{(1)}}=\\frac{1}{n} \\sum_{i=1}^n \\lambda_i(A_n^{(1)})$, denoting by \n$ \\lambda_i(A_n^{(1)})$, $i=1,\\ldots,n$, the eigenvalues of $A_n^{(1)}$.\n\\end{remarque}\n\\begin{theoreme}\\label{thprincipal} Let $({\\cal A}, \\tau)$ be a ${\\cal C}^*$-probability space\n equipped with a faithful tracial state.\nLet $x=(x_1,\\ldots,x_r)$ be a semi-circular system and $a=(a_1,\\ldots,a_t)$ be a t-tuple of noncommutative random variables which is free from $x$ in $({\\cal A},\\tau$).\\\\\nAssume moreover that $(A_n^{(1)},\\ldots,A_n^{(t)}, (A_n^{(1)})^*,\\ldots,(A_n^{(t)})^*)$ converges strongly towards $a=(a_1,\\ldots,a_t, a_1^*,\\ldots,a_t^*)$ in\n $({\\cal A}, \\tau)$, that is\nfor any polynomial P in 2t noncommutative indeterminates,\\\\\n\n$\\frac{1}{n} \\Tr P\\left( A_n^{(1)},\\ldots,A_n^{(t)}, (A_n^{(1)})^*,\\ldots,(A_n^{(t)})^*\\right)$ $$ \\rightarrow_{n\\rightarrow +\\infty} \\tau \\left( P(a_1,\\ldots,a_t,a_1^*,\\ldots,a_t^*\\right)$$ and \\\\\n\n$\\left\\|P\\left( A_n^{(1)},\\ldots,A_n^{(t)}, (A_n^{(1)})^*,\\ldots,(A_n^{(t)})^*\\right)\\right\\| $ $$\\rightarrow_{n\\rightarrow +\\infty} \\left\\| P(a_1,\\ldots,a_t,a_1^*,\\ldots,a_t^*)\\right\\|_{\\cal A}.$$\n Then, almost surely, for any polynomial $P$ \\hspace*{-0.1cm}in $r+2t $ \\hspace*{-0.16cm} noncommutative variables, \n$$ \\lim_{n\\rightarrow +\\infty} \\frac{1}{n}\\Tr P\\left(\\frac{X_n^{(1)}}{\\sqrt{n}},\\ldots,\\frac{X_n^{(r)}}{\\sqrt{n}}, A_n^{(1)},\\ldots,A_n^{(t)}, (A_n^{(1)})^*,\\ldots,(A_n^{(t)})^*\\right) $$\\begin{equation}\\label{af} =\\tau \\left( P\\left(x_1,\\ldots,x_r, a_1,\\ldots,a_t, a_1^*,\\ldots,a_t^*\\right) \\right)\\end{equation} and \n$$\\lim_{n\\rightarrow +\\infty} \\left\\| P\\left(\\frac{X_n^{(1)}}{\\sqrt{n}},\\ldots,\\frac{X_n^{(r)}}{\\sqrt{n}}, A_n^{(1)},\\ldots,A_n^{(t)},(A_n^{(1)})^*,\\ldots,(A_n^{(t)})^*\\right) \\right\\|$$\\begin{equation} \\label{saf} =\\left\\| P\\left(x_1,\\ldots,x_r, a_1,\\ldots,a_t, a_1^*,\\ldots,a_t^*\\right) \\right\\|_{\\cal A}.\\end{equation}\n\\end{theoreme}\n\\begin{remarque}{ Note that it is sufficient to prove Theorem \\ref{noeigenvalue} and Theorem \\ref{thprincipal} for Hermitian matrices $A_n^{(1)},\\ldots,A_n^{(t)}$ by considering their Hermitian and anti-Hermitian parts, so that throughout the paper we assume that the $A_n^{(i)}$'s are Hermitian.}\\end{remarque}\n\n\n\\noindent We adopt the strategy from \\cite{HT} and \\cite{Schultz05} based on a linearization trick and sharp estimates on matricial Stieltjes transforms. More precisely, both proofs of Theorem \\ref{thprincipal} and Theorem \\ref{noeigenvalue} are based on the following key Lemma \\ref{inclu2}. First, note that the algebra $M_m(\\C)\\otimes {\\cal A}$ formed by the $m\\times m $ matrices with coefficients in ${\\cal A}$, inherits the structure of ${\\cal C}^*$-probability space with trace $\\frac{1}{m} \\Tr_m \\otimes \\tau$ and norm \n$$\\Vert b \\Vert = \\lim_{k\\rightarrow +\\infty} \\left( \\frac{1}{m} \\Tr_m \\otimes \\tau \\left[(b^*b)^k\\right]\\right)^{\\frac{1}{2k}}, \\; \\forall b \\in M_m(\\C)\\otimes {\\cal A} .$$\n\n\n\\begin{lemme} \\label{inclu2} Let $({\\cal A}, \\tau)$ be a ${\\cal C}^*$-probability space\n equipped with a faithful tracial state and \n $x=(x_1,\\ldots,x_r)$ be a semi-circular system in $({\\cal A}, \\tau)$. Let $a_n=(a_n^{(1)},\\ldots,a_n^{(t)})$ be a t-tuple of noncommutative self-adjoint random variables which is free from $x$ in $({\\cal A},\\tau$), such that the distribution of $a_n$ coincides with the distribution of $(A_n^{(1)},\\ldots, A_n^{(t)})$ in $({ M}_n(\\mathbb{C}), \\frac{1}{n}\\Tr_n)$. Then, for all $m \\in \\N$, all self-adjoint matrices $\\gamma, \\alpha_1, \\ldots,\\alpha_r, \\beta_1, \\ldots, \\beta_t$ of size $m\\times m$ and\nall $\\epsilon >0$, almost surely, for all large $n$, we have\\\\\n\n\\noindent $\nsp(\\gamma \\otimes I_n + \\sum_{v=1}^r \\alpha_v \\otimes \\frac{X_n^{(v)}}{\\sqrt{n}}+ \\sum_{u=1}^t \\beta_u \\otimes A_n^{(u)})$ \\begin{equation} \\label{spectre3} \\subset\nsp(\\gamma \\otimes 1_{\\cal A} + \\sum_{v=1}^r \\alpha_v \\otimes x_v + \\sum_{u=1}^t \\beta_u \\otimes a_n^{(u)}) + ]-\\epsilon, \\epsilon[.\n\\end{equation}\n Here, $sp(T)$ denotes the spectrum of the operator $T$, $I_n$ the identity matrix and $1_{\\cal A}$ denotes the unit of ${\\cal A}$.\n\\end{lemme}\n\n\\begin{remarque}\\label{remarqueinversible}\nBy a density argument, it is sufficient to prove Lemma \\ref{inclu2} for invertible self-adjoint matrices $\\gamma, \\alpha_1, \\ldots,\\alpha_r, \\beta_1, \\ldots, \\beta_t$. This invertibility will be used in the proof of Lemma \\ref{inversion}.\n\\end{remarque}\n The proof of \\eqref{spectre3} requires sharp estimates of $g_n(z)-\\tilde g_n(z)$\nwhere for $z\\in \\mathbb{C}\\setminus \\mathbb{R}$, $$g_n(z) =\\mathbb{E} \\frac{1}{m} \\Tr_m \\otimes \\frac{1}{n} \\Tr_n [ (zI_m \\otimes I_n - \\gamma \\otimes I_n - \\sum_{v=1}^r \\alpha_v \\otimes \\frac{X_n^{(v)}}{\\sqrt{n}}(\\omega)- \\sum_{u=1}^t \\beta_u \\otimes A_n^{(u)})^{-1}]$$ and $$\\tilde g_n(z) = \\frac{1}{m} \\Tr_m \\otimes \\tau [ (zI_m \\otimes 1_{\\cal A} - \\gamma \\otimes 1_{\\cal A} - \\sum_{v=1}^r \\alpha_v \\otimes x_v - \\sum_{u=1}^t \\beta_u \\otimes a_n^{(u)})^{-1}].$$\n More precisely we are going to establish that \n there exists a polynomial $Q$ with nonnegative coefficients such that, for $z \\in \\C \\setminus \\mathbb{R}$, \n\\begin{equation} \\label{estimdiffeqno}\n\\left|g_n(z)-\\tilde g_n(z)+{\\tilde{E}_n(z)}\\right|\\leq \\frac{Q(\\vert \\Im z\\vert^{-1})}{n\\sqrt{n}},\n\\end{equation}\nwhere $\\tilde{E}_n$ is the Stieltjes transform of a compactly supported distribution $\\nabla_n$ on $\\mathbb{R}$ whose support is\nincluded in the spectrum of $\\gamma \\otimes 1_{\\cal A} +\\sum_{v=1}^r \\alpha_v \\otimes x_v +\\sum_{u=1}^t\\beta_u \\otimes a_n^{(u)}$ and such that $\\nabla_n(1)=0$.\\\\\n But this required sharp estimate makes necessary a technical piece of work and a fit use of free operator-valued subordination maps (see Section \\ref{free}). In particular, we need an explicit development of the Stieltjes transform up to the order $\\frac{1}{n\\sqrt{n}}$ but the stability under perturbation argument used in \\cite{CamilleM} does not provide this development from the approximate matricial subordination equation. Therefore we use a strategy based on an invertibility property of matricial subordination maps related to semi-circular system (see Lemma \\ref{inversion}).\\\\\n \n Theorem \\ref{thprincipal} can be deduced from Lemma \\ref{inclu2} by following the proofs in \\cite{HT} and \\cite{Schultz05}. \nGiven a noncommutative polynomial $P$, choosing in Lemma \\ref{inclu2} the $\\gamma$, $(\\alpha_v)_{v=1,\\ldots,r}$, $(\\beta_u)_{u=1,\\ldots,t}$ corresponding to a self-adjoint linearization of $P$ as defined in \\cite{A} allows to deduce Theorem \\ref{noeigenvalue}. \\\\\n\n\nIn Section \\ref{troncation}, we explain why, using a truncation and Gaussian convolution procedure, it is sufficient to prove Theorem \\ref{thprincipal} and Theorem \\ref{noeigenvalue}\n when we assume that the $X_{ij}^{(v)}$'s satisfy:\n\\begin{itemize} \n\\item[(H)] the variables $\\sqrt{2}\\Re X_{ij}^{(v)}$, $\\sqrt{2}\\Im X_{ij}^{(v)}$, $1\\leq i0$ and a nonnegative random variable $Y$ with finite fourth moment for which there exists $x_0>0$ and an integer number $n_0>0$ such that, for any $x >x_0$ and any integer number $n >n_0$, we have \\begin{equation}\\label{majquatreZ}\\frac{1}{n^2} \\sum_{1\\leq i\\leq n,1\\leq j\\leq n}\\mathbb{P}\\left( \\vert Z_{ij}\\vert >x\\right) \\leq K \\mathbb{P}\\left( Y >x\\right).\\end{equation}\nThen, setting $\\sigma^*=\\{\\sup_{1\\leq it\\right) dt \\geq \\sum_{l=1}^{+\\infty} u_l^3 (u_{l+1}-u_l) \\mathbb{P} \\left( \\frac{Y}{\\sigma^*}>u_{l+1}\\right), $$\n it readily follows that for any $\\delta>0$, choosing $u_l= \\delta 2^{(l-1)\/2}$, we have\n $$\\sum_{l=1}^\\infty 2^{2l} \\mathbb{P} \\left( \\frac{Y}{\\sigma^*} >\\delta 2^{l\/2}\\right) <\\infty.$$\n In particular, there exists an increasing sequence $(N_k)_{k\\geq 1}$ of integer numbers such that \n for any $k \\geq 1$, $$\\sum_{l=N_k+1}^\\infty 2^{2l} \\mathbb{P} \\left( \\frac{Y}{\\sigma^*} >\\frac{1}{2^{\\frac{k}{8}}} 2^{l\/2}\\right) \\leq \\frac{1}{2^k}.$$\n Set for any $l \\in ]0,N_{1}]$, $\\epsilon_l={1}$ and for any $l \\in ]N_k,N_{k+1}]$, $\\epsilon_l=\\frac{1}{2^{\\frac{k}{8}}}$. Then, \n $$\\sum_{l=N_1 +1}^\\infty 2^{2l} \\mathbb{P} \\left( \\frac{Y}{\\sigma^*} >\\epsilon_l 2^{l\/2}\\right)= \\sum_{k=1}^{+\\infty}\\sum_{l=N_k+1}^{N_{k+1}} 2^{2l} \\mathbb{P} \\left( \\frac{Y}{\\sigma^*} >\\frac{1}{2^{\\frac{k}{8}} }2^{l\/2}\\right)\\leq \\sum_{k=1}^{+\\infty}\\frac{1}{2^k} <\\infty.$$\n Define $\\delta_n= \\sqrt{2}\\epsilon_l$ for $2^{l-1} < n \\leq 2^{l}$. \n Thus, we exhibited a sequence of nonnegative numbers such that $\\delta_n \\downarrow 0$,\n \n $\\delta_n^2 \\sqrt{n} \\rightarrow +\\infty$ and $$\\sum_{n=1}^\\infty 2^{2n} \\mathbb{P} \\left( \\frac{Y}{\\sigma^*} >\\delta_{2^n} 2^{(n-1)\/2}\\right) <\\infty.$$\nLet us consider $X=Z_n\/\\sigma^*$.\nDefine for any $i\\geq 1, j\\geq 1$, $\\check X_{ij}= X_{ij}{\\1}_{\\vert X_{ij} \\vert \\leq \\sqrt{n} \\delta_n} $.\nWe have for any $k$ large enough, \n \\begin{eqnarray*}\n\\mathbb{P}\\left(X\\neq \\check X \\; i.o \\right) &\\leq & \\sum_{l=k}^\\infty \\mathbb{P} \\left( \\bigcup_{2^{l-1}< n \\leq 2^{l}} \\bigcup_{1\\leq i,j\\leq n} \\left\\{\\vert X_{ij} \\vert > \\sqrt{n}\\delta_n\\right\\} \\right)\\\\\n &\\leq & \\sum_{l=k}^\\infty \\mathbb{P} \\left( \\bigcup_{2^{l-1}< n \\leq 2^{l}} \\bigcup_{1\\leq i,j\\leq n} \\left\\{\\vert X_{ij} \\vert > 2^{\\frac{l-1}{2}}\\delta_{2^l}\\right\\} \\right)\\\\\n &\\leq & \\sum_{l=k}^\\infty \\sum_{1\\leq i,j\\leq 2^{l}} \\mathbb{P} \\left( \\vert X_{ij} \\vert > 2^{\\frac{l-1}{2}}\\delta_{2^l} \\right)\\\\\n &\\leq & K\\sum_{l=k}^\\infty 2^{2l} \\mathbb{P} \\left( Y > 2^{\\frac{l-1}{2}} \\delta_{2^l } \\sigma^*\\right) \\rightarrow_{k \\rightarrow +\\infty} 0.\n\\end{eqnarray*}\n\n\n\n\\noindent Define for $i\\neq j$, $1\\leq i,j\\leq n,$ $ \\hat X_{ij}= X_{ij}{\\1}_{\\vert X_{ij} \\vert \\leq \\sqrt{n} \\delta_n} $ and for any $i$, $\\hat X_{ii}=0$. Then, we have $\\left\\| \\frac{\\check X}{\\sqrt{n}}- \\frac{\\hat X}{\\sqrt{n}}\\right\\| \\leq \\delta_n\\rightarrow_{n\\rightarrow +\\infty} 0.$\n\\noindent Finally define for $i\\neq j$, $1\\leq i,j\\leq n$, $\\tilde X_{ij}= X_{ij}{\\1}_{\\vert X_{ij} \\vert \\leq \\sqrt{n} \\delta_n} -\\mathbb{E}\\left( X_{ij}{\\1}_{\\vert X_{ij} \\vert \\leq \\sqrt{n} \\delta_n}\\right)$ and for any $i$, $\\tilde X_{ii}=0$.\nWe have for large $n$ (denoting by $\\Vert\\cdot\\Vert_2$ the Hilbert-Schmidt norm)\\begin{eqnarray*} \n\\left\\| \\frac{\\hat X}{\\sqrt{n}}- \\frac{\\tilde X}{\\sqrt{n}}\\right\\|&\\leq &\\left\\| \\frac{\\hat X}{\\sqrt{n}}- \\frac{\\tilde X}{\\sqrt{n}}\\right\\|_{2}\n\\\\&=&\\left( \\frac{1}{n}\\sum_{i,j=1;i\\neq j }^n \\left|\\mathbb{E}\\left( X_{ij}{\\1}_{\\vert X_{ij} \\vert > \\sqrt{n} \\delta_n} \\right)\\right|^2\\right)^{1\/2}\\\\\n&\\leq&\\left( \\frac{1}{n^3 \\delta_n^4}\\sum_{i,j=1}^n \\mathbb{E}\\left( \\left| X_{ij}^2\\right| \\right)\\mathbb{E}\\left( \\left| X_{ij}\\right|^4{\\1}_{\\vert X_{ij} \\vert > \\sqrt{n} \\delta_n} \\right)\\right)^{1\/2}\\\\\n&\\leq &\\left( \\frac{K}{n \\delta_n^4}\\mathbb{E}\\left( \\left| \\frac{Y}{\\sigma^*} \\right|^4{\\1}_{\\vert \\frac{Y}{\\sigma^*} \\vert > \\sqrt{n} \\delta_n} \\right)\\right)^{1\/2} \\rightarrow_{n\\rightarrow +\\infty} 0.\n\\end{eqnarray*}\nThus, we have that almost surely \\begin{equation}\\label{normetilde}\\left\\| \\frac{ X}{\\sqrt{n}}- \\frac{\\tilde X}{\\sqrt{n}}\\right\\|\\rightarrow_{n\\rightarrow +\\infty} 0.\\end{equation}\nNote that the entries of $\\tilde X$ satisfy \n\\begin{itemize}\n\\item $\\tilde X_{ii}=0$;\n\\item $\\tilde X_{ij}$, $i0 \\mbox{\\;and all \\;} i\\neq j, \\; l\\geq~3$.\n\\end{itemize}\nThen, sticking to the end of the proof of Theorem 5.1 pages 87-93 in \\cite{BaiSil06}, one can prove that for any even integer $k$, one has \n$$\\mathbb{E}\\left(\\Tr \\left(\\frac{\\tilde X}{\\sqrt{n}}\\right)^k \\right)\\leq n^2\\left[2+ (10(2\\delta_n)^{1\/3} k\/\\log n)^3\\right]^k.$$\nChebychev's inequality yields that for any $\\eta >2$, \n\\begin{equation}\\label{chebychev}\\mathbb{P}\\left(\\left\\|\\frac{\\tilde X}{\\sqrt{n}}\\right\\| >\\eta\\right)\\leq \\frac{1}{\\eta^k}\\mathbb{E}\\left(\\Tr \\left(\\frac{\\tilde X}{\\sqrt{n}}\\right)^k \\right)\n\\leq \nn^2\\left[\\frac{2}{\\eta}+ \\frac{(10 (2\\delta_n)^{1\/3} k\/\\log n)^3}{\\eta}\\right]^k.\\end{equation}\nSelecting the sequence of even integers $k_n=2 \\left\\lfloor \\frac{\\log n }{\\delta_n^{1\/6}}\\right\\rfloor$ with the properties $k_n\/\\log n \\rightarrow +\\infty$ and $k_n \\delta_n^{1\/3}\/\\log n \\rightarrow 0$, we obtain that the right hand side of \\eqref{chebychev} is summable.\nUsing Borel-Cantelli's Lemma, we easily deduce that almost surely $$\\limsup_{n\\rightarrow +\\infty} \\left\\|\\frac{\\tilde X}{\\sqrt{n}}\\right\\| \\leq 2$$ and then, using \\eqref{normetilde}, that almost surely $$\\limsup_{n\\rightarrow +\\infty} \\left\\|\\frac{ X}{\\sqrt{n}}\\right\\| \\leq 2.$$\n\\end{proof}\nAccording to Lemma \\ref{baiyin}, for any $v=1,\\ldots,r$, almost surely, \n$\\sup_n \\left\\| \\frac{X_n^{(v)}}{\\sqrt{n}} \\right\\| < +\\infty.$ Therefore by a simple approximation argument using polynomials with coefficients in $\\mathbb{Q} +i \\mathbb{Q}$, to establish Theorem \\ref{thprincipal}, it is sufficient to prove that for any polynomial, almost surely \\eqref{af} and \\eqref{saf} hold. Now, we are going to show that the proof of Theorem \\ref{thprincipal} can be reduced to the proof of \\eqref{saf} in the case where the $X_{ij}^{(v)}$'s satisfy $(H)$.\\\\\nLet $X = [X_{jk}]_{j,k=1}^n$ be a Hermitian $n\\times n$ matrix such that the random variables $X_{ii}$,\n$\\sqrt{2} \\Re (X_{ij})$, $\\sqrt{2} \\Im (X_{ij}), {i0$ and an integer number $n_0>0$ such that, for\nany $x>x_0$ and any integer number $n>n_0$, we have \\begin{equation}\\label{majquatre}\\frac{1}{n^2} \\sum_{i\\leq n, j\\leq n}\\mathbb{P}\\left( \\vert X_{ij}\\vert >x\\right) \\leq K\\mathbb{P}\\left(\\vert Z \\vert>x\\right).\\end{equation}\n\n\n\\noindent Define for any $C>0$, for any $1\\leq i , j \\leq n$,\n\\begin{eqnarray}Y_{ij}^C &=&\\Re X_{ij}\\1_{\\vert \\Re X_{ij} \\vert \\leq C} - \\mathbb{E}\\left( \\Re X_{ij}\\1_{\\vert \\Re X_{ij} \\vert \\leq C} \\right) \\nonumber \\\\&&+ \\sqrt{-1} \\left\\{\n\\Im X_{ij}\\1_{\\vert \\Im X_{ij} \\vert \\leq C} - \\mathbb{E}\\left( \\Im X_{ij}\\1_{\\vert \\Im X_{ij} \\vert \\leq C} \\right) \\right\\}.\\label{ycdef}\\end{eqnarray}\nWe have \n\\begin{eqnarray*}\\mathbb{E} \\left( \\vert X_{ij}-Y_{ij}^C\\vert^2 \\right)&=&\\mathbb{E} \\left( \\vert \\Re X_{ij}\\vert^2 \\1_{\\vert \\Re X_{ij} \\vert > C} \\right)\n+ \\mathbb{E} \\left( \\vert \\Im X_{ij}\\vert^2 \\1_{\\vert \\Im X_{ij} \\vert > C} \\right) \\\\&&\n-\\left\\{ \\mathbb{E} \\left( \\Re X_{ij} \\1_{\\vert \\Re X_{ij} \\vert > C} \\right)\\right\\}^2-\\left\\{ \\mathbb{E} \\left( \\Im X_{ij} \\1_{\\vert \\Im X_{ij} \\vert > C} \\right)\\right\\}^2\\\\& \\leq & \\frac{\\mathbb{E} \\left(\\vert \\Re X_{ij} \\vert^3\\right)+\\mathbb{E} \\left(\\vert \\Im X_{ij} \\vert^3\\right)}{C}\n\\end{eqnarray*}\nso that $$\\sup_{i\\geq 1,j\\geq 1}\\mathbb{E} \\left( \\vert X_{ij}-Y_{ij}^C\\vert^2 \\right) \\leq \\frac{2\\theta^*}{C}.$$\nAccording to Lemma \\ref{baiyin}, we have almost surely \\begin{equation}\\label{centragebis}\\limsup_{n\\rightarrow+\\infty} \\left\\| \\frac{X}{\\sqrt{n}}-\\frac{Y^C}{\\sqrt{n}}\\right\\| \\leq 2\\frac{\\sqrt{2\\theta^*}}{\\sqrt{C}}.\\end{equation}\nNote that \n\\begin{eqnarray*}1 - 2 \\mathbb{E} \\left( \\vert \\Re Y_{ij}^C\\vert^2 \\right) &=& 1-2 \\mathbb{E} \\left\\{\\left(\\Re X_{ij} \\1_{\\vert \\Re X_{ij} \\vert \\leq C} - \\mathbb{E} \\left( \\Re X_{ij} \\1_{\\vert \\Re X_{ij} \\vert \\leq C}\\right)\\right)^2\\right\\}\n\\\\&=& 2\\left[ \\frac{1}{2} -\\mathbb{E} \\left( \\vert \\Re X_{ij}\\vert^2 \\1_{\\vert \\Re X_{ij} \\vert \\leq C} \\right)\\right] \n+2\\left\\{ \\mathbb{E} \\left( \\Re X_{ij} \\1_{\\vert \\Re X_{ij} \\vert \\leq C} \\right)\\right\\}^2\\\\&=& \n2 \\mathbb{E} \\left( \\vert \\Re X_{ij}\\vert^2 \\1_{\\vert\\Re X_{ij} \\vert > C} \\right)+ 2\\left\\{\\mathbb{E} \\left( \\Re X_{ij} \\1_{\\vert \\Re X_{ij} \\vert > C}\\right) \\right\\}^2. \\end{eqnarray*}\nso that $$\\sup_{i\\geq 1, j\\geq 1}\\vert 1 - 2\\mathbb{E} \\left( \\vert \\Re Y_{ij}^C\\vert^2 \\right) \\vert \\leq \\frac{4\\theta^*}{C}.$$\nSimilarly $$\\sup_{i\\geq 1,j\\geq 1}\\vert 1 - 2\\mathbb{E} \\left( \\vert \\Im Y_{ij}^C\\vert^2 \\right) \\vert \\leq \\frac{4\\theta^*}{C}.$$\nLet us assume that $C>8\\theta^*.$ Then, we have \n$$\\mathbb{E} \\left( \\vert \\Re Y_{ij}^C\\vert^2 \\right)> \\frac{1}{4} \\; \\mbox{and}\\; \\mathbb{E} \\left( \\vert \\Im Y_{ij}^C\\vert^2 \\right)> \\frac{1}{4}.$$\nNow define \\begin{equation}\\label{defxc}{X}_{ij}^C =\\frac{\\Re Y_{ij}^C}{\\sqrt{2\\mathbb{E} \\left( \\vert \\Re Y_{ij}^C\\vert^2 \\right)}} +\\sqrt{-1} \\frac{\\Im Y_{ij}^C}{\\sqrt{2\\mathbb{E} \\left( \\vert \\Im Y_{ij}^C\\vert^2 \\right)}}.\\end{equation}\nNote that \\begin{eqnarray*} {X}_{ij}^C-Y_{ij}^C&=& \\Re X_{ij}^C \\left( 1-\\sqrt{2} \\mathbb{E} \\left( \\vert \\Re Y_{ij}^C\\vert^2 \\right)^{1\/2}\\right)\n+\\sqrt{-1} \\Im X_{ij}^C \\left( 1-\\sqrt{2} \\mathbb{E} \\left( \\vert \\Im Y_{ij}^C\\vert^2 \\right)^{1\/2}\\right)\n\\\\&=&\\Re X_{ij}^C\\frac{1 - 2\\mathbb{E} \\left( \\vert \\Re Y_{ij}^C\\vert^2 \\right) }{1 + \\sqrt{2}\\mathbb{E} \\left( \\vert \\Re Y_{ij}^C\\vert^2 \\right)^{1\/2}}+\n\\sqrt{-1} \\Im X_{ij}^C\\frac{1 - 2\\mathbb{E} \\left( \\vert \\Im Y_{ij}^C\\vert^2 \\right) }{1 + \\sqrt{2}\\mathbb{E} \\left( \\vert \\Im Y_{ij}^C\\vert^2 \\right)^{1\/2}}.\\end{eqnarray*} \n\\noindent Thus, according to Lemma \\ref{baiyin}, we have almost surely \\begin{equation}\\label{normal}\\limsup_{n\\rightarrow+\\infty} \\left\\| \\frac{X^C}{\\sqrt{n}}-\\frac{Y^C}{\\sqrt{n}}\\right\\| \\leq \\frac{{8\\theta^*}}{{C}}.\\end{equation}\nThus, by \\eqref{centragebis} and \\eqref{normal}, we obtain that almost surely \\begin{equation}\\label{xc}\\limsup_{n\\rightarrow+\\infty} \\left\\| \\frac{X}{\\sqrt{n}}-\\frac{X^C}{\\sqrt{n}}\\right\\| \\leq 2\\frac{\\sqrt{2\\theta^*}}{\\sqrt{C}}+ \\frac{{8\\theta^*}}{{C}}.\\end{equation}\nLet $[{\\cal G}_{ij}]_{i\\geq 1, j\\geq 1}$ be an infinite array which is independent of the $X_{ij}'s$ and such that $\\sqrt{2} \\Re {\\cal G}_{ij}$, $ \\sqrt{2} \\Im {\\cal G}_{ij}$, $i0$,\n\\begin{equation}\\label{xcdelta}X^{C,\\delta}= \\frac{ X^C +\\delta {\\cal G}}{\\sqrt{1+\\delta^2}}.\\end{equation}\nNote that the random variables $\\sqrt{2}\\Re (X^{(v)})^{C,\\delta}_{ij}$, $\\sqrt{2}\\Im (X^{(v)})^{C,\\delta}_{ij}$, $ i 8\\theta^*$, we have almost surely\n\\begin{equation}\\label{bybis} \\limsup_{n\\rightarrow+\\infty}\\left\\| \\frac{{X^C}}{\\sqrt{n}} \\right\\|\\leq 2.\\end{equation}\nNow, \\eqref{xc}, \\eqref{by} and \\eqref{bybis} yield that for any $C > 8 \\theta^*$, any $\\delta >0$, almost surely $ \\limsup_{n\\rightarrow +\\infty}\\left\\|\\frac{X -{X^{C,\\delta}}}{\\sqrt{n}} \\right\\| \\leq u_C +v_\\delta$ where $u_C$ and $v_\\delta$ are deterministic positive functions tending to zero when respectively $C$ goes to infinity and $\\delta$ goes to zero. \nHence, it is easy to see that for any $0<\\epsilon<1$, there exists $C_\\epsilon$ and $\\delta_\\epsilon$ such that almost surely for all large $n$, \n$$\\left\\|\\frac{X -{X^{C_\\epsilon,\\delta_\\epsilon}} }{\\sqrt{n}}\\right\\|\\leq \\epsilon.$$\nWe can deduce that for any polynomial $P$ in $r+t$ noncommutative variables, there exists some constant $L>0$ such that the following holds: for any $0<\\epsilon<1$, there exists $C_\\epsilon$ and $\\delta_\\epsilon$ such that almost surely for all large $n$, \\\\\n\n\\noindent $\\left\\| P\\left(\\frac{X_n^{(1)}}{\\sqrt{n}},\\ldots,\\frac{X_n^{(r)}}{\\sqrt{n}}, A_n^{(1)},\\ldots,A_n^{(t)}\\right)\\right. $ \\begin{equation}\\label{approxnorme} \\left.- P\\left(\\frac{(X_n^{(1)})^{C_\\epsilon,\\delta_\\epsilon}}{\\sqrt{n}},\\ldots,\\frac{(X_n^{(r)})^{C_\\epsilon,\\delta_\\epsilon}}{\\sqrt{n}}, A_n^{(1)},\\ldots,A_n^{(t)}\\right)\\right\\|\\leq L \\epsilon.\\end{equation} \nThen, it is clear that it is sufficient to establish Theorem \\ref{thprincipal} and Theorem \\ref{noeigenvalue} for the $(X_n^{(v)})^{C_\\epsilon,\\delta_\\epsilon}$'s. \\\\Moreover, we obviously have that for any $\\epsilon$ and for any $p\\in \\mathbb{N}$, \\begin{equation}\\label{moments}\\max_{v=1,\\ldots,r} \\sup_{i\\geq 1, j\\geq 1} \\mathbb{E}\\left(\\vert (X_{ij}^{(v)})^{C_\\epsilon,\\delta_\\epsilon}\\vert^p\\right) <+\\infty.\\end{equation}\n Then, \\eqref{af} is a consequence of Theorem 5.4.5 in \\cite{AGZ}.\\\\\n\n\n Moreover, note that, by definition, the distributions of the random variables\n$\\sqrt{2}\\Re (X^{(v)})_{ij}^{C_\\epsilon,\\delta_\\epsilon}, \\sqrt{2}\\Im (X^{(v)})_{ij}^{C_\\epsilon,\\delta_\\epsilon}, i0$ if the matrix $M$ is positive definite and $M\\ge0$ if it is nonnegative definite.\nIn general $M>P$ means that $M-P$ is positive definite.\n \\end{itemize}\n\\noindent We now define the random variables of interest. \nLet $({\\cal A}, \\tau)$ be a ${\\cal C}^*$-probability space with unit $1_{\\cal A}$,\n equipped with a faithful tracial state and \n $x=(x_1,\\ldots,x_r)$ be a semi-circular system in $({\\cal A}, \\tau)$.\n Let $a_n=(a_n^{(1)},\\ldots,a_n^{(t)})$ be a t-tuple of noncommutative self-adjoint random variables which is free from $x$ in $({\\cal A},\\tau$) and such that the distribution of $a_n$ in $({\\cal A},\\tau)$ coincides with the distribution of $(A_n^{(1)},\\ldots, A_n^{(t)})$ in $({ M}_n(\\mathbb{C}),\\tr_n)$.\n\\begin{itemize}\n\n \\item[-] \n We define the random variable $S_n$ with values in $M_m(\\C) \\otimes M_n(\\C)$ by:\n \\begin{equation} \\label{defSn}\n S_n = \\gamma \\otimes I_n + \\sum_{v=1}^r \\alpha_v \\otimes \\frac{X^{(v)}_n}{\\sqrt{n}}+ \\sum_{u=1}^t \\beta_u \\otimes A_n^{(u)}\n \\end{equation}\nand $s_n\\in M_m(\\C) \\otimes {\\cal A}$ by \n$$s_n=\\gamma \\otimes 1_{\\cal A} + \\sum_{v=1}^r\\alpha_v \\otimes x_v+ \\sum_{u=1}^t\\beta_u \\otimes a_n^{(u)}.$$\n \\item[-] For any matrix $\\lambda$ in $ M_m(\\C) $ such that $ \\Im(\\lambda)$ is positive definite,\n we define the $M_m(\\C)\\otimes M_n(\\C)$-valued respectively $M_m(\\C) \\otimes {\\cal A}$-valued random variables:\n \\begin{equation}\\label{rn}\n\tR_n(\\lambda)=(\\lambda \\otimes I_n - S_n)^{-1},\n\t\\end{equation}\n\t$$r_{n}(\\lambda)= \\left(\\lambda \\otimes 1_{\\cal A} -s_n \\right)^{-1},$$\n\tand \n\tthe $M_m(\\C)$-valued random variables:\n \\begin{equation} \\label{defSt}\n H_n(\\lambda) = ({\\rm id}_m \\otimes \\tr_n) [ (\\lambda \\otimes I_n - S_n)^{-1}],\n \\end{equation}\n \\begin{equation} \\label{espSt}\nG_n(\\lambda) =\\mathbb{ E}[ H_n(\\lambda) ]\n\\end{equation}\nand\n\\begin{equation}\\label{defGntilde}\\tilde G_{n}(\\lambda)={\\rm id}_m \\otimes \\tau \\left(r_n (\\lambda ) \\right).\\end{equation}\nSince $\\sum_{v=1}^r\\alpha_v \\otimes x_v$ is an $ M_m(\\mathbb C)$-valued semicircular of variance $\\eta\\colon b\\mapsto\\sum_{v=1}^r\\alpha_vb\\alpha_v$ which is free over $ M_m(\\mathbb C)$ from $\\gamma\\otimes 1_{\\cal A}+\\sum_{u=1}^t\\beta_u \\otimes a_n^{(u)}$, we know from \\cite{ABFN} (see proof of Theorem 8.3) that $\\tilde G_{n}$ satisfies (see Section \\ref{free} Theorem \\ref{resusub} for $p=1$) \\begin{equation}\\label{subor}\\tilde G_{n}(\\lambda)= G_{\\sum_{u=1}^t\\beta_s \\otimes a_n^{(u)}}(\\omega_n(\\lambda))\n\\end{equation}\nwhere \\begin{equation}\\label{omegan}\n\\omega_n(\\lambda)=\\lambda-\\gamma -\\sum_{v=1}^r\\alpha_v \\tilde G_n(\\lambda)\\alpha_v\n\\end{equation} and $$G_{\\sum_{u=1}^t\\beta_u \\otimes a_n^{(u)}}(\\lambda)={\\rm id}_m \\otimes \\tau \\left(\\lambda\\otimes 1_{\\cal A} -\\sum_{u=1}^t\\beta_u \\otimes a_n^{(u)} \\right)^{-1}.$$\n\\noindent For $z\\in \\C \\setminus \\R$, we also define\n \\begin{equation}\\label{defpetitg}g_n(z) = \\tr_m(G_n(zI_m))\\end{equation}and \\begin{equation}\\label{defpetitgtilde}\\tilde g_n(z) = \\tr_m(\\tilde G_n(z I_m)).\\end{equation}\n\n\n\n\n\n\n\n\n\n\n \n\n \n \\end{itemize}\n\n In the sequel, we will say that a random term in some $M_p(\\C)$, depending on $n$, $\\lambda \\in M_m(\\mathbb{C}) $ such that $\\Im \\lambda$ is positive definite, the $\\{\\alpha_v\\}_{v=1,\\ldots,r}$, $\\{\\beta_u\\}_{u=1,\\ldots,t}$ and $\\gamma$, is $O\\left(\\frac{1}{n^k}\\right)$ if its operator norm is smaller than\n $\\frac{ Q\\left(\\Vert (\\Im \\lambda)^{-1} \\Vert\\right)}{n^k}$ for some deterministic polynomial $Q$\n whose coefficients are nonnegative real numbers and can depend on $m$, $\\{\\alpha_v\\}_{v=1,\\ldots,r}$, $\\{\\beta_u\\}_{u=1,\\ldots,t}$, $\\gamma$.\\\\\n For a family of random terms $I_{pq}$, $(p,q) \\in \\{1,\\ldots,n\\}^2$, we will set $I_{pq}=O_{p,q}^{(u)} \\left(\\frac{1}{n^k}\\right)$ if for each $(p,q)$, $I_{pq}=O \\left(\\frac{1}{n^k}\\right)$ and moreover one can find a bound of \n\nthe norm of each $I_{p,q}$ as above involving a common polynomial $Q$.\\\\\n\nThroughout the paper, $K$, $C$ denote some positive constants and $Q$ denotes some deterministic polynomial in one variable\n whose coefficients are nonnegative real numbers; they can depend on $m$, $\\{\\alpha_v\\}_{v=1,\\ldots,r}$, $\\{\\beta_u\\}_{u=1,\\ldots,t}$ and $\\gamma$ and they may vary from line to line.\n\n\n\n\n\n\n\n\n\n\\section{Operator-valued subordination}\\label{free}\n\n\nIn this section we introduce one of the main tools used in describing joint distributions of\nrandom variables that do not necessarily commute. It was a crucial insight of Voiculescu\n\\cite{V2000, FreeMarkov, V1} that J. L. Taylor's theory of free noncommutative functions \\cite{taylor} \nprovides the appropriate analogue of the classical Stieltjes transform for encoding\noperator-valued distributions \\cite{V1995}, and hence joint distributions of $q$-tuples of\nnoncommuting random variables. We shall only present below the case of relevance to our\npresent work, and refer the reader to \\cite{V1995,V2000,V1} for the general case and for the\nproofs of the main results.\n\nGiven a tracial $\\mathcal C^*$-probability space $(\\mathcal A,\\tau)$ and a trace and order preserving\nunital $\\mathcal C^*$ inclusion $M_m(\\mathbb C)\\subseteq\\mathcal A$ (i.e. such that $I_m\\in \nM_m(\\mathbb C)$ identifies with the unit of $\\mathcal A$ and ${\\rm tr}_m(b)=\\tau(b)$ for all $b\\in \nM_m(\\mathbb C)$), there exists a conditional expectation $E\\colon\\mathcal A\\to M_m(\\mathbb C)$, i.e. \na linear map sending the unit to itself and such that $E(b_1yb_2)=b_1E(y)b_2$ for all $b_1,b_2\\in M_m(\\mathbb C),y\n\\in\\mathcal A$ - see \\cite[Section II.6.10.13]{Bruce}. We will only be concerned with the trivial case\nof the canonical inclusion $M_m(\\mathbb C)\\subseteq M_m(\\mathbb C)\\otimes\\mathcal A$ \ngiven by $b\\mapsto b\\otimes 1_{\\cal A}$, when the conditional expectation is the partial trace:\n$E(b\\otimes y)=({\\rm id}_m\\otimes\\tau)(b\\otimes y)=\\tau(y)b$.\nThe $M_m(\\mathbb C)$-valued distribution of an element $y\\in\\mathcal A$ with respect to $E$ is by \ndefinition the family of multilinear maps \n$\\mu_y=\n\\{\\Psi_q\\colon\\underbrace{M_m(\\mathbb C)\\times\\cdots\\times M_m(\\mathbb C)}_{q-1\\text{ times}}\n\\to M_m(\\mathbb C)\\colon \\Psi_q(b_1,\\dots,b_{q-1})=E[yb_1y\\cdots b_{q-1}y],q\\in\\mathbb N\\}$. \nBy convention, $\\Psi_0=1\\in M_m(\\mathbb C)$, $\\Psi_1=E[y]$.\n\nFor a given $y=y^*\\in\\mathcal A$ with distribution $\\mu_y$, define its noncommutative \nStieltjes transform to be the {\\em countable family} of maps\n$G_{\\mu_y,p}(b)=(E\\otimes{\\rm id}_{p})\\left[(b-y\\otimes I_p)^{-1}\\right], p\\in \\mathbb{N}\\setminus\\{0\\}.$\nThus, $G_{\\mu_y,1}(b)=E\\left[(b-y)^{-1}\\right],b\\in M_m(\\mathbb C)$,\n$G_{\\mu_y,2}(b)=(E\\otimes{\\rm id}_{2})\\left[\\left(\\begin{bmatrix}\nb_{11} & b_{12}\\\\\nb_{21} & b_{22}\n\\end{bmatrix}-\\begin{bmatrix}\ny & 0\\\\\n0 & y\n\\end{bmatrix}\\right)^{-1}\\right],$ $b_{11}, b_{12},\nb_{21}, b_{22}\\in M_m(\\mathbb C)$ etc. These maps are clearly analytic on the open sets $\\{b\\in \nM_m(\\mathbb C)\\otimes M_p(\\mathbb C)\\colon b-y\\otimes I_p\\text{ invertible in }\\mathcal A\\otimes M_p(\\mathbb C)\\}$. Two \nsuch sets will be important in this paper: the noncommutative upper half-plane $H^+_p(M_m(\\mathbb \nC))=\\{b\\in M_m(\\mathbb C)\\otimes M_p(\\mathbb C)\\colon \\Im b:=(b-b^*)\/2i>0\\}$, $p\\in \\mathbb{N}\\setminus\\{0\\}$, \nand the ``ball around infinity'' $\\{b\\in M_m(\\mathbb C)\\otimes M_p(\\mathbb C) \\colon b\\text{ invertible, \n}\\|b^{-1}\\|<\\|y\\|^{-1}\\}$ (actually only for $p=1$). The maps $b\\mapsto G_{\\mu_y,p}(b^{-1})$ have thus an analytic extension \naround zero, which maps zero to zero and has the identity as first (Frechet) derivative. While \n$G_{\\mu_y,p}$ does not map these ``balls around infinity'' into themselves, it does map \n$H^+_p(M_m(\\mathbb C))$ into $-H^+_p(M_m(\\mathbb C))$, and moreover $G_{\\mu_y,p}(b)^{-1}$ maps \n$H^+_p(M_m(\\mathbb C))$ into itself (see \\cite[Section 3.6]{V2000}). (In addition, the family of maps \n$\\{G_{\\mu_y,p}\\}_{p\\in \\mathbb{N}\\setminus\\{0\\}}$ satisfy certain compatibility conditions that make them into free \nnoncommutative maps - see \\cite{KVV}. It is known \\cite{W} that there is a bijection between such families \nof maps $G$ that send for any $p \\in \\mathbb{N}\\setminus\\{0\\}$, $H^+_p(M_m(\\mathbb C))$ into $-H^+_p(M_m(\\mathbb C))$ and have the \nabove-described behavior on ``balls around infinity'' and $M_m(\\mathbb C)$-valued distributions of \nself-adjoint elements; however, since we will not make use of this correspondence, we chose to only \nmention it in order to illustrate the parallel to the case of classical Stieltjes transforms, and direct the \ninterested reader to \\cite{W} for details.)\n\n\n\nAs in scalar-valued free probability, one defines \\cite{V1995} {\\em freeness with amalgamation}\nover $M_m(\\mathbb C)$ via an algebraic relation similar to \\eqref{freeness}, but involving $E$\ninstead of $\\tau$ and noncommutative polynomials with coefficients in $M_m(\\mathbb C)$.\nSince it is not important for us here, we refer the interested reader to \\cite{V1995} for \nmore details. The essential result of Voiculescu that we will need in this paper is the following\nanalytic subordination result:\n\\begin{theoreme}\\label{resusub}\nWith the above notations, assume that $y_1=y_1^*,y_2=y_2^*\\in\\mathcal A$ are free with amalgamation\nover $M_m(\\mathbb C)$. For any $p\\in\\mathbb N$ there exist analytic maps $\\omega_{1,p},\n\\omega_{2,p}\\colon H^+_p(M_m(\\mathbb C))\\to H^+_p(M_m(\\mathbb C))$ such that:\n\\begin{enumerate}\n\\item For all $b\\in H^+_p(M_m(\\mathbb C)),$ $\\Im \\omega_{j,n}(b)\\ge\\Im b$, $j=1,2$;\n\\item For all $b\\in H^+_p(M_m(\\mathbb C))$\n$$G_{\\mu_{y_1+y_2},p}(b)=G_{\\mu_{y_1},p}(\\omega_{1,p}(b))=G_{\\mu_{y_2},p}(\\omega_{2,p}(b))\n=\\left[\\omega_{1,p}(b)+\\omega_{2,p}(b)-b\\right]^{-1}$$ \n\\item $\\omega_{1,p},\n\\omega_{2,p}$ are noncommutative maps in the sense of $\\cite{taylor}$ $($see $\\cite{KVV})$.\n\\end{enumerate}\n\\end{theoreme}\nThe result as phrased here is a combination of parts of \\cite[Theorem 3.8]{V2000} and\n\\cite[Theorem 2.7]{BMS}. We shall use this theorem in the particular case when $y_2=s$ is a centred \n{\\em operator-valued semicircular} random variable (and actually only for $p=1$). As for the scalar-valued semicircular\ncentred random variables, it is uniquely determined by its variance $\\eta\\colon b\\mapsto E(sbs)$,\nwhich is a completely positive self-map of $M_m(\\mathbb C)$. A characterization in terms of moments\nand cumulants via $\\eta$ is provided by Speicher in \\cite{SMem}. Given the context of our paper,\nwe find it more useful to provide a characterization in terms of the noncommutative \nStieltjes transform \\cite{HRS}: the functions $G_{\\mu_s,p}$ are the unique solutions mapping\n$H^+_p(M_m(\\mathbb C))$ into $-H^+_p(M_m(\\mathbb C))$ of the functional equations\n$$\nG_p(b)^{-1}=b-(\\eta\\otimes{\\rm id}_p)(G_p(b)),\\quad b\\in H^+_p(M_m(\\mathbb C)),\np\\in\\mathbb N.\n$$\nStarting from this equation, it can be shown (see \\cite{ABFN}) that the subordination function associated \nto a semicircular operator-valued random variable is particularly nice: if $y_1$ and $s$ are free with\namalgamation over $M_m(\\mathbb C)$, then \n\\begin{equation}\n\\omega_{1,p}(b)=b-(\\eta\\otimes{\\rm id}_p)(G_{\\mu_{y_1},p}(\\omega_{1,p}(b)))=b-\n(\\eta\\otimes{\\rm id}_p)(G_{\\mu_{y_1+s},p}(b)),\n\\end{equation}\nfor $b\\in H^+_p(M_m(\\mathbb C)),p\\in\\mathbb N,$ or, equivalently,\n\\begin{equation}\nG_{\\mu_{y_1},p}(b-(\\eta\\otimes{\\rm id}_p)(G_{\\mu_{y_1+s},p}(\\omega_{1,p}(b)))=G_{\\mu_{y_1+s},p}(b),\n\\quad b\\in H^+_p(M_m(\\mathbb C)),p\\in\\mathbb N.\n\\end{equation}\nThis indicates that the $\\omega_{1,p}$'s are injective maps on $H^+_p(M_m(\\mathbb C)),p\\in\\mathbb N.\n$ Their left inverses are defined as\n\\begin{equation}\\label{tauto}\n{\\Lambda}_{1,p}(w)=w+(\\eta\\otimes{\\rm id}_p)(G_{\\mu_{y_1},p}(w)),\n\\quad w\\in H^+_p(M_m(\\mathbb C)),p\\in\\mathbb N.\n\\end{equation}\n\nLet us explain how all this relates to the joint distributions of free\nrandom variables. It turns out (see, for example, \\cite{NSS}, but it can be easily verified directly)\nthat if $\\{x_1=x_1^*,\\dots,x_r=x_r^*\\}, \\{y_1=y_1^*,\\dots,y_t=y_t^*\\}\\subset\\mathcal A$ are free over \n$\\mathbb C$ and for $v=1,\\ldots,r$, $\\alpha_v=\\alpha_v^*$, for $u=1,\\ldots,t$, $\\beta_u=\\beta_u^*\\in M_m(\\mathbb C)$,\nthen $\\{\\alpha_1\\otimes x_1,\\dots,\\alpha_r\\otimes x_r\\}$ and $\\{\\beta_1\\otimes y_1,\\dots,\\beta_t\\otimes y_t\\}$ are free \nwith amalgamation over $M_m(\\mathbb C)$. Thus, they can be treated with the tools described above.\nMoreover, if $x_1,\\dots,x_r$ are free $\\mathbb C$-valued semicircular centred random variables \nof variance one and $\\alpha_1,\\dots,\\alpha_r$ are self-adjoint $m\\times m$ complex matrices, then\n$\\alpha_1\\otimes x_1+\\cdots+\\alpha_r\\otimes x_r$ is a centred $M_m(\\mathbb C)$-valued semicircular of \nvariance $b\\mapsto\\sum_{j=1}^r \\alpha_jb\\alpha_j$. These simple facts together with a linearization trick\n(see Section \\ref{linearisation} and Step 1 of Section \\ref{strategie}) will allow us in principle to treat, from the point of view of\nthe Stieltjes transform, an $r$-tuple of Wigner matrices and deterministic matrices as we would treat a \nsingle Wigner matrix together with a single deterministic matrix. \\\\\n~~\n\n\\noindent Let us conclude this section with the following invertibility property of matricial subordination maps related to semi-circular system that will fundamental in our approach.\n\n\\begin{lemme}\\label{inversion}\nUsing the notations of Section \\ref{Notations}, define for any $\\rho$ in $M_m(\\mathbb{C})$ such that $\\Im \\rho>0$,\n\\begin{equation}\\Lambda_n(\\rho)= \\gamma +\\rho + \\sum_{v=1}^r \\alpha_v G_{\\sum_{u=1}^t \\beta_u \\otimes a_n^{(u)}}(\\rho) \\alpha_v.\\end{equation}\nWith $\\omega_n$ defined in \\eqref{omegan},\nwhen $\\Im \\rho>0$ and $\\Im \\Lambda_n(\\rho)>0$ we have \n$$\\omega_n(\\Lambda_n(\\rho))=\\rho.$$\n\\end{lemme}\n\\begin{proof}\nThe equality $\\Lambda_n(\\omega_n(\\rho))=\\rho$ holds tautologically for all $\\rho$ with $\\Im \\rho>0$ (see \\eqref{tauto}).\nLet us first show that the equality $\\omega_n(\\Lambda_n(\\rho))=\\rho$ holds when $\\rho$ has \na small enough inverse. \nThe map $\\Lambda_n$ has a power series expansion\n$$\n\\Lambda_n(\\rho)=\\rho+\\gamma+\\sum_{v=1}^r\\alpha_v\\left(\n\\sum_{k=0}^\\infty({\\rm id}_m\\otimes\\tau)\n\\left(\\rho^{-1}\\left[\\sum_{u=1}^t(\\beta_u\\otimes a_n^{(u)})\\rho^{-1}\\right]^k\\right)\\right)\\alpha_v,\n$$\nconvergent when $\\|\\rho^{-1}\\|<\\left\\|\\sum_{u=1}^t\\beta_u\\otimes a_n^{(u)}\\right\\|^{-1}$. For simplicity\nwe let $h(\\lambda)=\\sum_{v=1}^r\\alpha_v\\left(\n\\sum_{k=0}^\\infty({\\rm id}_m\\otimes\\tau)\n\\left(\\lambda\\left[\\sum_{u=1}^t(\\beta_u\\otimes a_n^{(u)})\\lambda\\right]^k\\right)\\right)\\alpha_v,$ norm\nconvergent on a ball of radius $\\left\\|\\sum_{u=1}^t\\beta_u\\otimes a_n^{(u)}\\right\\|^{-1}$ and fixing zero. \nPerforming the change of variable $\\lambda=\\rho^{-1}$, we obtain $\\Lambda_n(\\rho)=\n\\Lambda_n(\\lambda^{-1})=\\lambda^{-1}+\\gamma+h(\\lambda)$. Then\n$(\\Lambda_n(\\lambda^{-1}))^{-1}=(\\lambda^{-1}+\\gamma+h(\\lambda))^{-1}=\n\\lambda(1+(\\gamma+h(\\lambda))\\lambda)^{-1}$, which is analytic on the set of all $\\lambda\\in\n M_m(\\mathbb C)$ such that $\\|\\lambda\\|<\\left\\|\\sum_{u=1}^t\\beta_u\\otimes a_n^{(u)}\\right\\|^{-1}$\nand $\\|\\gamma+h(\\lambda)\\|<\\|\\lambda\\|^{-1}.$ \n\nDefine ${\\check\\Lambda_n}(\\rho)=(\\Lambda_n(\\rho^{-1}))^{-1}$ and ${\\check\\omega_n}\n(\\rho)=(\\omega_n(\\rho^{-1}))^{-1}$. We have established above that ${\\check\\Lambda_n}$ is analytic\non a neighbourhood of zero, and a direct computation shows that ${\\check\\Lambda_n}(0)=0,\n{\\check\\Lambda_n}'(0)={\\rm id}$. The inverse function theorem for analytic maps allows us to\nconclude that there exists a neighbourhood of zero on which ${\\check\\Lambda_n}$ has a unique inverse\nwhich fixes zero and whose derivative at zero is equal to the identity. \nThe map ${\\check\\omega_n}$ is shown precisely the same way to satisfy the same properties as\n${\\check\\Lambda_n}$. In particular, for $\\|\\rho\\|$ small enough, \n${\\check\\Lambda_n}({\\check\\omega_n}(\\rho))=(\\Lambda_n({\\check\\omega_n}(\\rho)^{-1}))^{-1}=\n(\\Lambda_n(((\\omega_n(\\rho^{-1}))^{-1})^{-1}))^{-1}=(\\Lambda_n(\\omega_n(\\rho^{-1}))^{-1}=\n(\\rho^{-1})^{-1}=\\rho$ for any $\\rho$ with strictly positive imaginary part. Since zero is in the\nclosure of $\\{\\rho\\in M_m(\\mathbb C)\\colon\\Im \\rho>0\\}$, it follows that ${\\check\\omega_n}$\nand ${\\check\\Lambda_n}$ are compositional inverses to each other on a small enough neighbourhood of\nzero. We conclude that for all $\\rho$ such that the lower bound of the spectrum of $\\Im \\rho$ is \nsufficiently large, $\\omega_n(\\Lambda_n(\\rho))=\\rho$.\n\n\nLet now $\\rho$ be fixed in $ M_m(\\mathbb{C})$ such that $\\Im \\rho>0$ and $\\Im \\Lambda_n(\\rho)>0$.\nLet $\\phi$ be a positive linear functional on $ M_m(\\mathbb{C})$ such that\n$\\phi(1)=1$ (i.e. a state). Define $\\varphi_\\rho(\\cdot )=\\phi(\\cdot )\/\\phi(\\Im \\rho)$. It is linear and \npositive (well defined because $\\Im \\rho\\ge\\frac{1}{\\|(\\Im \\rho)^{-1}\\|}1$, so that $\\phi(\\Im \\rho)\\ge\n\\frac{1}{\\| (\\Im \\rho)^{-1}\\|}>0$). Define \n$$\nf_\\rho(z)=\\varphi_\\rho\\left(\\Lambda_n(\\Re \\rho+z\\Im \\rho)\\right),\\quad z\\in\\mathbb C^+.\n$$\nNote that $$f_\\rho(z)=z+ \\varphi_\\rho(\\gamma+\\Re \\rho))+F(z)$$\nwhere $$F(z)=\\frac{\\phi\\left[ \\sum_{v=1}^r \\alpha_v {\\rm id}_m\\otimes \\tau \\left\\{\\left( (\\Re \\rho +z \\Im \\rho)\\otimes 1_{\\cal A} -\\sum_{u=1}^t \\beta_u \\otimes a_n^{(u)} \\right)^{-1}\\right\\}\\alpha_v\\right] }{\\phi(\\Im \\rho)}.$$\n$F(z)$ is analytic on $\\mathbb{C}\\setminus \\mathbb{R}$ and satisfies $\\overline{F(z)}=F(\\bar{z}).$\nLet $z\\in \\mathbb{C}^+$.\nWe have \\\\\n\n\n\\noindent $\\Im F(z)$ $$= \\frac{\\phi\\left[ \\sum_{v=1}^r \\alpha_v{\\rm id}_m\\otimes \\tau \\left\\{ \\Im \\left\\{\\left( (\\Re \\rho +z \\Im \\rho)\\otimes 1_{\\cal A} -\\sum_{u=1}^t \\beta_u \\otimes a_n^{(u)} \\right)^{-1}\\right\\} \\right\\}\\alpha_v\\right] }{\\phi(\\Im \\rho)}$$\nwhere \\\\\n\n\\noindent $\\Im \\left\\{\\left( (\\Re \\rho +z \\Im \\rho)\\otimes 1_{\\cal A} -\\sum_{u=1}^t \\beta_u \\otimes a_n^{(u)} \\right)^{-1}\\right\\}$ \\begin{eqnarray*}&=&\n-\\Im z \\left(( \\Re \\rho +z \\Im \\rho)\\otimes 1_{\\cal A} -\\sum_{u=1}^t \\beta_u \\otimes a_n^{(u)} \\right)^{-1} (\\Im \\rho \\otimes 1_{\\cal A})\\\\&&~~~~~~~~~~~~~~~~~~~~~~~~~~~\\times \\left( (\\Re \\rho +\\bar{z} \\Im \\rho)\\otimes 1_{\\cal A} -\\sum_{u=1}^t \\beta_u \\otimes a_n^{(u)} \\right)^{-1}<0.\n\\end{eqnarray*}\nIt follows by the complete positivity of the trace $\\tau$ that $${\\rm id}_m\\otimes \\tau\\left\\{ \\Im \\left\\{\\left(( \\Re \\rho +z \\Im \\rho)\\otimes 1_{\\cal A} -\\sum_{u=1}^t \\beta_u \\otimes a_n^{(u)} \\right)^{-1}\\right\\}\\right\\}<0.$$\nNow, according to Remark \\ref{remarqueinversible}, we can assume the $\\alpha_v$'s invertible so that \n$\\sum_{v=1}^r \\alpha_v{\\rm id}_m\\otimes \\tau \\left\\{\\Im \\left\\{\\left( (\\Re \\rho +z \\Im \\rho) \\otimes 1_{\\cal A} -\\sum_{u=1}^t \\beta_u \\otimes a_n^{(u)} \\right)^{-1}\\right\\}\\right\\}\\alpha_v <0$ and then $\\Im F(z) <0$.\nThus for any $z\\in \\mathbb{C}\\setminus \\mathbb{R}$, we have $\\Im z \\Im F(z)<0.$\nFinally \n$$\\lim_{y \\rightarrow +\\infty} iy F(iy)= \\varphi_\\rho \\left( \\sum_{v=1}^r\\alpha_v (\\Im \\rho )^{-1} \\alpha_v \\right):=c_\\rho >0. $$\nThus, by Akhiezer-Krein's Theorem (\\cite{AK} page 93), there exists a probability measure $\\mu$ on $\\mathbb {R}$ such that\n$$\nF(z)= c_\\rho \\int_\\mathbb R\\frac{d\\mu(s)}{z-s}\n,\\quad z\\in\\mathbb C^+.\n$$\nThen\n$$\nf_{\\rho}(z)=z+\\varphi_\\rho(\\gamma+\\Re \\rho)+c_\\rho \\int_\\mathbb R\\frac{d\\mu(s)}{z-s}\n,\\quad z\\in\\mathbb C^+.\n$$\nThus $\\Im f_{\\rho}(u+iv)=v\\left(1-c_\\rho\\int_\\mathbb R\\frac{d\\mu(s)}{(u-s)^2+v^2}\\right)$. We observe \nthat what's under parenthesis is strictly increasing in $v$. Since by hypothesis, we have $\\Im \\Lambda_n(\\rho)>0$ and thus $\\Im f_{\\rho}(i)=\\Im \\varphi_\\rho\\left(\\Lambda_n(\\Re \\rho+i\\Im \\rho)\\right)\n>0$, we obtain \nimmediately that $\\Im f_{\\rho}(iv)>0$ for all $v\\ge1$. This means that $\\Im \n\\phi(\\Lambda_n(\\Re \\rho+iv\\Im \\rho))>0$ for all $v\\in[1,+\\infty)$ and all states $\\phi$, so that \\begin{equation}\\label{ray}\\Im \\Lambda_n (\\Re \\rho+iv\\Im \\rho)\n>0, {\\rm~ for~ all~} v\\ge1.\\end{equation}\nNow it is clear that $$\\Omega=\\{z \\in \\C^+, \\Im \\Lambda_n(\\Re \\rho + z\\Im \\rho) >0\\}$$\nis an open set which contains $d=\\{iv, v\\geq 1\\}$.\nLet $\\Omega_d$ be the connected component of $\\Omega$ which contains $d$. Note that $\\Omega_d $ is an open set.\n\nAs we have shown at the beginning of our proof, for given $\\rho,\\Im \\rho>0$, there exists an $M>0$ (possibly depending on $\\rho$) such that $\\omega_n(\\Lambda_n(\\Re \\rho + iv\\Im \\rho))=\\Re \\rho + iv\\Im \\rho$ for all $v>M.$\nBy the identity principle for analytic functions, we immediately obtain that \n$ \\omega_n(\\Lambda_n(\\Re \\rho +z \\Im \\rho))=\\Re \\rho +z \\Im \\rho$ for all $z\\in \\Omega_d$ and in particular for $z=i$. The \nproof of Lemma \\ref{inversion} is complete.\n\\end{proof}\n\n\n\\section{Proof of Lemma \\ref{inclu2}}\\label{lemmefonda} \\subsection{ Sharp estimates of Stieltjes transforms} The proof of \\eqref{spectre3} requires the sharp estimate \\eqref{estimdiffeqno} we are going to prove here.\n\n\n\\noindent According to Section \\ref{troncation}, from now on, we assume that the $X_{ij}^{(v)}$'s satisfy (H).\nNote that this assumption implies that for any $v\\in \\{1,\\ldots,r\\}$, \n$$\\forall i\\geq 1, \\forall j \\geq 1, \\;\\kappa_1^{i,j,v}=0, \\;\\kappa_2^{i,j,v}=1,$$\n $$\\forall i\\geq 1, \\forall j \\geq 1,\\;, i\\neq j, \\; \\tilde \\kappa_1^{i,j,v}=0,\\; \\tilde \\kappa_2^{i,j,v}=1$$ and \n for any $p\\in \\mathbb{N}\\setminus\\{0\\}$, \\begin{equation}\\label{cumulants}\\max_{v=1,\\ldots,r} \\sup_{i\\geq 1, j \\geq 1} \\vert \\kappa_p^{i,j,v}\\vert<+\\infty, \\; \\max_{v=1,\\ldots,r} \\sup_{i\\geq 1, j\\geq 1} \\vert \\tilde \\kappa_p^{i,j,v}\\vert<+\\infty, \\end{equation}\nwhere for $i\\neq j$, $(\\kappa_p^{i,j,v})_{p\\geq1}$ and $(\\tilde \\kappa_p^{i,j,v})_{p\\geq 1}$ denote the classical cumulants of $\\sqrt{2}\\Re X_{ij}^{(v)}$ and $\\sqrt{2}\\Im X_{ij}^{(v)}$ respectively and $(\\kappa_p^{i,i,v})_{p\\geq 1}$ denotes the classical cumulants of $ X_{ii}^{(v)}$ (we set $(\\tilde \\kappa_p^{i,i,v})_{p\\geq 1}\\equiv 0$).\\\\\n\n\n\n\\noindent Now, we present our main technical tool (see \\cite{KKP}):\n \\begin{lemme} \\label{lem1}\nLet $\\xi$ be a real-valued random variable such that $\\mathbb{E}(\\vert \\xi\n\\vert^{p+2})<\\infty$. Let $\\phi$ be a function from $\\R$ to $\\C$\nsuch that the first $p+1$ derivatives are continuous and bounded. Then,\n\\begin{equation}\\label{IPP}\\mathbb E(\\xi \\phi(\\xi)) = \\sum_{a=0}^p\n\\frac{\\kappa_{a+1}}{a!}\\mathbb{E}(\\phi^{(a)}(\\xi)) + \\epsilon\\end{equation}\nwhere $\\kappa_{a}$ are the classical cumulants of $\\xi$, $\\epsilon \\leq C\n\\sup_t \\vert \\phi^{(p+1)}(t)\\vert \\mathbb{E}(\\vert \\xi \\vert^{p+2})$, $C$\ndepends on $p$ only.\n\\end{lemme}\nIn the following, we shall apply this identity with a function\n$\\phi(\\xi)$ given by the entries of the resolvent of $S_n$. It\nfollows from Lemma \\ref{lem2} and (\\ref{resolvente}) below\nthat the conditions of Lemma \\ref{lem1} (bounded derivatives) are\nfulfilled.\nWe first need the following preliminary lemma.\n \n\\begin{lemme}\\label{inverseY}\nFor any $\\lambda \\in M_m(\\mathbb{C}) $ such that $\\Im \\lambda$ is positive definite, $(\\lambda -\\gamma -\\sum_{v=1}^r\\alpha_v G_n(\\lambda)\\alpha_v)\\otimes I_n - \\sum_{u=1}^t \\beta_u \\otimes A_n^{(u)}$ and $(\\lambda -\\gamma -\\sum_{v=1}^r\\alpha_v \\tilde G_n(\\lambda)\\alpha_v)\\otimes I_n - \\sum_{u=1}^t \\beta_u \\otimes A_n^{(u)}$ are invertible.\nSet \\begin{equation}\\label{y}Y_n(\\lambda)=\\left((\\lambda -\\gamma -\\sum_{v=1}^r\\alpha_v G_n(\\lambda)\\alpha_v)\\otimes I_n - \\sum_{u=1}^t \\beta_u \\otimes A_n^{(u)} \\right)^{-1}\\end{equation}\nand \\begin{equation}\\label{ytilde}\\tilde Y_n(\\lambda)=\\left((\\lambda -\\gamma -\\sum_{v=1}^r\\alpha_v \\tilde G_n(\\lambda)\\alpha_v)\\otimes I_n - \\sum_{u=1}^t \\beta_u \\otimes A_n^{(u)} \\right)^{-1}.\\end{equation}\nWe have \\begin{equation} \\label{Y}\\left\\| Y_n(\\lambda) \\right\\| \\leq \\Vert ( \\Im \\lambda)^{-1} \\Vert\\end{equation}\nand \\begin{equation} \\label{Y2}\\left\\| \\tilde Y_n(\\lambda) \\right\\| \\leq \\Vert ( \\Im \\lambda)^{-1} \\Vert.\\end{equation}\n\n\\end{lemme}\n\\begin{proof}\nWe only present the proof for $Y_n(\\lambda)$ since the proof is similar for $\\tilde Y_n(\\lambda)$.\nNote that \n\\begin{eqnarray*}\n\\Im \\left[ \\left( \\lambda\\otimes I_n -S_n\\right)^{-1}\\right]\n&=& \\frac{1}{2i}\\left[\\left( \\lambda\\otimes I_n -S_n\\right)^{-1}-\\left( \\lambda^*\\otimes I_n -S_n\\right)^{-1}\\right]\\\\\n&=& -\\left( \\lambda\\otimes I_n -S_n\\right)^{-1}\\left( \\Im \\lambda \\otimes I_n\\right) \\left( \\lambda^*\\otimes I_n -S_n\\right)^{-1}.\n\\end{eqnarray*}\nThis yields that $-\\Im R_n(\\lambda) $ is positive definite. Since the map ${\\rm id }_m\\otimes \\tr_n$ is positive we can deduce that $-\\Im H_n(\\lambda)$ is positive and then that $-\\Im G_n(\\lambda)$ is positive. It readily follows that\n\\begin{equation}\\label{image} \\Im \\left[\\lambda -\\gamma -\\sum_{v=1}^r\\alpha_v G_n(\\lambda)\\alpha_v \\right] \\geq \\Im \\lambda\\end{equation} and then\n$$\\Im \\left[\\left(\\lambda -\\gamma -\\sum_{v=1}^r\\alpha_v G_n(\\lambda)\\alpha_v\\right)\\otimes I_n - \\sum_{u=1}^t \\beta_u \\otimes A_n^{(u)}\\right] \\geq \\Im \\lambda\\otimes I_n.$$ Hence Lemma \\ref{inverseY} follows by lemma 3.1 in \\cite{HT}.\n\\end{proof}\n\\begin{theoreme}\\label{resolvante}\nFor any $\\lambda \\in M_m(\\mathbb{C}) $ such that $\\Im \\lambda$ is positive definite, we have \n\\begin{equation}\\label{mast}\\mathbb{E} \\left(R_n(\\lambda)\\right)=Y_n(\\lambda)+Y_n(\\lambda)\\Xi(\\lambda)\\end{equation}\nwhere $Y_n(\\lambda)$ is defined in Lemma \\ref{inverseY}\n\\noindent and $\\Xi(\\lambda)=\\sum_{l,j}\\Xi_{lj}(\\lambda)\\otimes E_{lj}$ satisfies that for all $l,j\\in \\{1, \\ldots,n\\}$, $$ \\Xi_{lj}(\\lambda)=\\Psi_{lj}(\\lambda) +O_{lj}^{(u)}( \\frac{1}{n^2})$$\nwhere\n \\begin{eqnarray}\\Psi_{lj}(\\lambda)& =&\\sum_{v=1}^r\\bigg\\{\\mathbb{E}\\left[ \\alpha_v [H_n(\\lambda) -\\mathbb{E}(H_n(\\lambda))] \\alpha_v [R_n(\\lambda ) ]_{lj} \\right]\\nonumber\n\\\\&&+ \\frac{1}{2\\sqrt{2}n\\sqrt{n}} \\sum_{i=1}^n \\left\\{1-\\delta_{il}\\left(1-\\frac{1}{\\sqrt{2}}\\right)\\right\\}M^{(3)}(v,i,l,j)\\nonumber\\\\&&+ \\frac{1}{4n^2} \\sum_{i=1}^n \\left(1-\\frac{1}{2}\\delta_{il}\\right) M^{(4)}(v,i,l,j)\\nonumber\\\\&&\\left.\n+ \\frac{1}{4\\sqrt{2}n^2\\sqrt{n}}\\sum_{i=1}^n \\left[1-\\delta_{il}\\left(1-\\frac{1}{2\\sqrt{2}}\\right)\\right]M^{(5)}(v,i,l,j)\\right\\},\\label{psi}\\end{eqnarray}\nwith \\\\\n\n\n\\noindent $M^{(3)}(v,i,l,j)$\n\\begin{eqnarray}=& \\mathbb{E} \\{(\\kappa_3^{i,l,v}+\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})\\alpha_v(R_n(\\lambda))_{ii}\\alpha_v(R_n(\\lambda))_{li}\\alpha_v \n (R_n(\\lambda))_{lj}\\nonumber \\\\\n &+(\\kappa_3^{i,l,v}-\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})\\alpha_v (R_n(\\lambda))_{ii}\\alpha_v (R_n(\\lambda))_{ll} \\alpha_v (R_n(\\lambda))_{ij}\\label{except}\n \\\\\n&+(\\kappa_3^{i,l,v}-\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})\\alpha_v (R_n(\\lambda))_{il}\\alpha_v (R_n(\\lambda))_{ii} \\alpha_v (R_n(\\lambda))_{lj} \\nonumber \\\\\n&+(\\kappa_3^{i,l,v}+\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})\\alpha_v (R_n(\\lambda))_{il}\\alpha_v (R_n(\\lambda))_{il} \\alpha_v (R_n(\\lambda))_{ij}\\}, \\nonumber \\end{eqnarray}\n\n\n\n\\noindent $M^{(4)}(v,i,l,j)$\n\\begin{eqnarray}=&(\\kappa_4^{i,l,v}+\\tilde \\kappa_4^{i,l,v}) \\mathbb{E} \\{\\alpha_v(R_n(\\lambda))_{il}\\alpha_v(R_n(\\lambda))_{il}\\alpha_v \n(R_n(\\lambda))_{il} \\alpha_v (R_n(\\lambda))_{ij}\\label{premierk4}\n\\\\&+\\alpha_v (R_n(\\lambda))_{il}\\alpha_v (R_n(\\lambda))_{ii} \\alpha_v (R_n(\\lambda))_{li}\\alpha_v (R_n(\\lambda))_{lj}\\label{deuxcas4} \\\\&+\\alpha_v (R_n(\\lambda))_{ii}\\alpha_v (R_n(\\lambda))_{ll} \\alpha_v(R_n(\\lambda))_{ii}\\alpha_v (R_n(\\lambda))_{lj}\\label{troiscas4}\\\\& + \\alpha_v (R_n(\\lambda))_{ii} \\alpha_v(R_n(\\lambda))_{li}\\alpha_v (R_n(\\lambda))_{ll}\\alpha_v (R_n(\\lambda))_{ij}\\} \\label{quatrecas4}\\\\\n&\\hspace*{-0.4cm}+(\\kappa_4^{i,l,v}~\\hspace*{-0.4cm}-\\tilde \\kappa_4^{i,l,v}) \\mathbb{E} \\{\\alpha_v(R_n(\\lambda))_{ii}\\alpha_v(R_n(\\lambda))_{li}\\alpha_v \n(R_n(\\lambda))_{li} \\alpha_v (R_n(\\lambda))_{lj} \\label{tilde1}\n\\\\&+\\alpha_v (R_n(\\lambda))_{il}\\alpha_v (R_n(\\lambda))_{ii} \\alpha_v (R_n(\\lambda))_{ll}\\alpha_v (R_n(\\lambda))_{ij} \\label{tilde2} \\\\&+\\alpha_v (R_n(\\lambda))_{il}\\alpha_v (R_n(\\lambda))_{il} \\alpha_v(R_n(\\lambda))_{ii}\\alpha_v (R_n(\\lambda))_{lj} \\label{tilde3}\\\\& + \\alpha_v (R_n(\\lambda))_{ii} \\alpha_v(R_n(\\lambda))_{ll}\\alpha_v (R_n(\\lambda))_{il}\\alpha_v (R_n(\\lambda))_{ij}\\}, \\label{tilde4}\n\\end{eqnarray}\n\n\\noindent $M^{(5)}(v,i,l,j)$\n\\begin{eqnarray*}=& \\mathbb{E} \\{(\\kappa_5^{i,l,v}+\\tilde\\kappa_5^{i,l,v}\\sqrt{-1}) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\\\\& \\times \\left[\\alpha_v(R_n(\\lambda))_{ii}\\alpha_v(R_n(\\lambda))_{li}\\alpha_v \n (R_n(\\lambda))_{li}\\alpha_v (R_n(\\lambda))_{ll} \\alpha_v (R_n(\\lambda))_{ij}\\right.\\\\\n &+\\alpha_v (R_n(\\lambda))_{ii}\\alpha_v (R_n(\\lambda))_{li} \\alpha_v (R_n(\\lambda))_{ll} \\alpha_v (R_n(\\lambda))_{ii} \\alpha_v (R_n(\\lambda))_{lj} \\\\\n&+\\alpha_v (R_n(\\lambda))_{ii}\\alpha_v (R_n(\\lambda))_{ll} \\alpha_v (R_n(\\lambda))_{ii}\\alpha_v(R_n(\\lambda))_{li}\\alpha_v \n (R_n(\\lambda))_{lj} \\\\\n&+\\alpha_v (R_n(\\lambda))_{ii}\\alpha_v (R_n(\\lambda))_{ll} \\alpha_v (R_n(\\lambda))_{il} \\alpha_v (R_n(\\lambda))_{il} \\alpha_v (R_n(\\lambda))_{ij} \\\\&+\\alpha_v (R_n(\\lambda))_{il}\\alpha_v (R_n(\\lambda))_{ii} \\alpha_v (R_n(\\lambda))_{li} \\alpha_v (R_n(\\lambda))_{li}\\alpha_v (R_n(\\lambda))_{lj} \\\\\n&+ \\alpha_v (R_n(\\lambda))_{il}\\alpha_v (R_n(\\lambda))_{ii}\\alpha_v (R_n(\\lambda))_{ll} \\alpha_v (R_n(\\lambda))_{il} \\alpha_v (R_n(\\lambda))_{ij} \\\\\n&+ \\alpha_v (R_n(\\lambda))_{il}\\alpha_v (R_n(\\lambda))_{il}\\alpha_v (R_n(\\lambda))_{ii} \\alpha_v (R_n(\\lambda))_{ll} \\alpha_v (R_n(\\lambda))_{ij} \\\\\n&+\\left. \\alpha_v (R_n(\\lambda))_{il}\\alpha_v (R_n(\\lambda))_{il}\\alpha_v (R_n(\\lambda))_{il} \\alpha_v (R_n(\\lambda))_{ii} \\alpha_v (R_n(\\lambda))_{lj} \\right]\\\\\n&+(\\kappa_5^{i,l,v}-\\tilde\\kappa_5^{i,l,v}\\sqrt{-1}) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\\\&\\left[\n\\alpha_v(R_n(\\lambda))_{ii}\\alpha_v(R_n(\\lambda))_{li}\\alpha_v \n (R_n(\\lambda))_{li}\\alpha_v (R_n(\\lambda))_{li} \\alpha_v (R_n(\\lambda))_{lj}\\right.\\\\\n&+\\alpha_v (R_n(\\lambda))_{ii}\\alpha_v (R_n(\\lambda))_{li} \\alpha_v (R_n(\\lambda))_{ll} \\alpha_v (R_n(\\lambda))_{il} \\alpha_v (R_n(\\lambda))_{ij} \\\\\n&+\\alpha_v (R_n(\\lambda))_{ii}\\alpha_v (R_n(\\lambda))_{ll} \\alpha_v (R_n(\\lambda))_{ii}\\alpha_v(R_n(\\lambda))_{ll}\\alpha_v \n (R_n(\\lambda))_{ij} \\\\\n&+\\left. \\alpha_v (R_n(\\lambda))_{ii}\\alpha_v (R_n(\\lambda))_{ll}\\alpha_v (R_n(\\lambda))_{il} \\alpha_v (R_n(\\lambda))_{ii} \\alpha_v (R_n(\\lambda))_{lj} \\right]\\\\\n&+\\alpha_v (R_n(\\lambda))_{il}\\alpha_v (R_n(\\lambda))_{ii} \\alpha_v (R_n(\\lambda))_{li} \\alpha_v (R_n(\\lambda))_{ll} \\alpha_v (R_n(\\lambda))_{ij} \\\\\n&+\\alpha_v (R_n(\\lambda))_{il}\\alpha_v (R_n(\\lambda))_{ii} \\alpha_v (R_n(\\lambda))_{ll} \\alpha_v (R_n(\\lambda))_{ii}\\alpha_v (R_n(\\lambda))_{lj} \\\\\n&+ \\alpha_v (R_n(\\lambda))_{il}\\alpha_v (R_n(\\lambda))_{il}\\alpha_v (R_n(\\lambda))_{ii} \\alpha_v (R_n(\\lambda))_{li} \\alpha_v (R_n(\\lambda))_{lj} \\\\\n&+\\left. \\alpha_v (R_n(\\lambda))_{il}\\alpha_v (R_n(\\lambda))_{il}\\alpha_v (R_n(\\lambda))_{il} \\alpha_v (R_n(\\lambda))_{il} \\alpha_v (R_n(\\lambda))_{ij}\\right]\\}.\n\\end{eqnarray*}\n\\end{theoreme}\n \n\n \\begin{proof}\n We shall apply formula \\eqref{IPP} to the ${ M}_m(\\C)$-valued function $\\phi(\\xi) = (R_n(\\lambda))_{ij}$ for $1 \\leq i,j \\leq n$ and $\\xi$ is one of the variable $\\frac{X^{(v)}_{kk}}{\\sqrt{n}}$,\n$\\sqrt{2} \\frac{Re(X^{(v)}_{kl})}{\\sqrt{n}}$, $\\sqrt{2} \\frac{Im(X^{(v)}_{kl})}{\\sqrt{n}}$ for $1 \\leq k0$ such that for every $k,l,i,j \\in\\{1,\\ldots,n\\}$ and every $v\\in \\{1,\\ldots,r\\}$, \\begin{equation}\\label{majuniv}\\left\\| O_{k,l,i,j}\\left(\\frac{1}{n^3}\\right)\\right\\|\\ \\leq \\frac{C\\Vert \\alpha_v \\Vert^5 \\Vert ( \\Im \\lambda)^{-1} \\Vert^6}{n^3},\\end{equation}\nwith the analogous equations with $f_{kl}$ and $e_{kk}$ replacing the $\\kappa_i^{k,l,v}$'s by the $\\tilde \\kappa_i^{k,l,v}$'s and $\\kappa_i^{k,k,v}$'s respectively. \\\\\n\n\\noindent\nNoticing that for $ k0$ such that for every $l,j \\in\\{1,\\ldots,n\\}$ and any $v \\in \\{1,\\ldots,r\\}$, \\begin{equation}\\label{majuniv2}\\left\\| O_{l,j,v}\\left(\\frac{1}{n^2}\\right)\\right\\|\\ \\leq \\frac{C\\Vert \\alpha_v \\Vert^5 \\Vert ( \\Im \\lambda)^{-1} \\Vert^6}{n^2}.\\end{equation}\n\n\\noindent Now,\n\\begin{eqnarray*}\n\\sum_{v=1}^r(\\alpha_v \\otimes X_n^{(v)})R_n (\\lambda)\n &=& (S_n - \\sum_{u=1}^t \\beta_u \\otimes A_n^{(u)}-\\gamma \\otimes I_n) (\\lambda \\otimes I_n - S_n)^{-1} \\\\\n &=& -I_m\\otimes I_n +\\left[ (\\lambda - \\gamma) \\otimes I_n -\\sum_{u=1}^t \\beta_u \\otimes A_n^{(u)} \\right] R_n(\\lambda)\n \\end{eqnarray*}\n implying\n\\begin{eqnarray}\\sum_{v=1}^r \\mathbb{E}[(\\alpha_v \\otimes \\frac{X_n^{(v)}}{\\sqrt{n}}) R_n(\\lambda ) ]_{lj} &=& -\\delta_{jl} I_m +(\\lambda-\\gamma) \\mathbb{E} (R_n(\\lambda))_{lj}\\nonumber\n\\\\&&- \\left[\\sum_{u=1}^t \\beta_u \\otimes A_n^{(u)} \\mathbb{E}( R_n(\\lambda))\\right]_{lj}. \\label{identiteresol}\\end{eqnarray}\nOn the other hand, we have \n\\begin{eqnarray}\\mathbb{E}\\left[ \\alpha_v H_n(\\lambda) \\alpha_v [R_n(\\lambda ) ]_{lj} \\right]&= & \\mathbb{E}\\left[ \\alpha_v [H_n(\\lambda) -\\mathbb{E}(H_n(\\lambda))] \\alpha_v [R_n(\\lambda ) ]_{lj} \\right]\\nonumber\\\\ &&\n+\n\\alpha_v G_n(\\lambda) \\alpha_v \\mathbb{E}\\left[ [R_n(\\lambda ) ]_{lj} \\right]\n.\\label{centrage}\\end{eqnarray}\nHence \\eqref{4}, \\eqref{identiteresol} and \\eqref{centrage} yield \\\\\n\n $ -\\delta_{jl}I_m +(\\lambda-\\gamma) \\mathbb{E} (R_n(\\lambda))_{lj}\n- \\left[\\sum_{u=1}^t \\beta_u \\otimes A_n^{(u)} \\mathbb{E}( R_n(\\lambda))\\right]_{lj}$ \\begin{equation}\\label{me} = \\sum_{v=1}^r\\alpha_v G_n(\\lambda) \\alpha_v \\mathbb{E}\\left[ [R_n(\\lambda ) ]_{lj} \\right] +\n\\Xi_{lj}(\\lambda) \\end{equation}\nwhere $$\\Xi_{lj}(\\lambda)=\\Psi_{lj}(\\lambda)\n+O^{(u)}_{l,\nj}(1\/n^2)$$ and $\\Psi_{lj}$ is defined in Theorem \\ref{resolvante}.\nThus, we have \n$$ \\left[\\left(\\lambda-\\gamma -\\sum_{v=1}^r\\alpha_v G_n(\\lambda) \\alpha_v\\right)\\otimes I_n - \\sum_{u=1}^t \\beta_u \\otimes A_n^{(u)} \\right] \\mathbb{E} (R_n(\\lambda))\n = I_n\\otimes I_m +\n\\Xi(\\lambda).$$\n\\eqref{mast} readily follows. \\end{proof}\n\n\n\\begin{proposition}\\label{55}\nFor any $p,q\\in \\{1,\\ldots,n\\}^2$, for any $mn\\times mn $ deterministic matrix $F_n(\\lambda)$ such that \n$ F_n(\\lambda)=O(1)$, setting $\\Psi(\\lambda)=\\sum_{l,j}\\Psi_{lj}(\\lambda)\\otimes E_{lj}$ where $\\Psi_{lj}$ is defined by \\eqref{psi}, we have\\\\\n\n$\\left\\{Y_n(\\lambda)\\Psi(\\lambda)F_n(\\lambda)\\right\\}_{pq}$\n\\begin{eqnarray} &=&\\frac{1}{2\\sqrt{2}n\\sqrt{n}} \\sum_{v=1}^r\\sum_{i,l=1}^n (\\kappa_3^{i,l,v} -\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})(Y_n)_{pl}\\nonumber \\\\&& ~~~~~~~~~~~~~~~~~~~~~~~~~\\times \\mathbb{E} \\{\\alpha_v(R_n(\\lambda))_{ii}\\alpha_v(R_n(\\lambda))_{ll}\\alpha_v \n (R_n(\\lambda)F_n(\\lambda))_{iq}\\} \\nonumber\\\\&&+O^{(u)}_{p,q}(\\frac{1}{{n}}). \\label{YPSIF}\\end{eqnarray}\n\\end{proposition}\n\\begin{proof}\nLet us fix $v \\in \\{1,\\ldots,r\\}$. Using \\eqref{cumulants}, \\eqref{Y} and \\eqref{norme}, one can easily deduce from \\eqref{Oderacine} (respectively from \\eqref{Oden}) that all the terms in $\\left\\{Y_n(\\lambda)\\Psi(\\lambda)F_n(\\lambda)\\right\\}_{pq}$ corresponding to the $M^{(3)}(v,i,l,j)$'s in \\eqref{psi} excluding $$(\\kappa_3^{i,l,v}-\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})\\alpha_v (R_n(\\lambda))_{ii}\\alpha_v (R_n(\\lambda))_{ll} \\alpha_v (R_n(\\lambda))_{ij} $$ (respectively all the terms corresponding to the $M^{(4)}(v,i,l,j)$'s) are equal to $O(1\/n)$.\n\n\n\n\n\n\n\n\nLet $C$ be some constant such that $\\sup_{i,l,v} \\{\\vert \\kappa_5^{i,l,v}\\vert +\\vert \\tilde \\kappa_5^{i,l,v} \\vert\\}\\leq C$.\nFor the terms in $\\left\\{Y_n(\\lambda)\\Psi(\\lambda)F_n(\\lambda)\\right\\}_{pq}$ corresponding to $M^{(5)}(v,i,l,j)$'s, note that using \\eqref{norme} they can be all obviously bounded by \n\\\\\n\n\\noindent $\n\\frac{C\\Vert F_n(\\lambda) \\Vert \\Vert \\alpha_v\\Vert^5 \\Vert (Im(\\lambda))^{-1}\\Vert^5}{n\\sqrt{n}}\\sum_{l=1}^n \\left\\| (Y_n(\\lambda))_{pl}\\right\\|$\n\\begin{eqnarray*}&\\leq &\n\\frac{C\\Vert F_n (\\lambda)\\Vert \\Vert \\alpha_v\\Vert^5 \\Vert (Im(\\lambda))^{-1}\\Vert^5}{n}\\left\\{\\sum_{l=1}^n \\left\\| (Y_n(\\lambda))_{pl}\\right\\|^2\\right\\}^{\\frac{1}{2}}\\\\&\\leq & \\frac{\\sqrt{m}C\\Vert F_n(\\lambda) \\Vert \\Vert \\alpha_v\\Vert^5 \\Vert (Im(\\lambda))^{-1}\\Vert^6}{n}\\\\&=&O(1\/n)\\end{eqnarray*}\nwhere we used \\eqref{l} and \\eqref{Y}.\\\\\n\n\\noindent Finally define\n$$\\hat {\\cal R}= \\left[ \\alpha_v [H_n(\\lambda) -\\mathbb{E}(H_n(\\lambda))] \\alpha_v\\right] \\otimes I_n $$\nso that there exists some constant $C>0$ such that \n\\\\\n\n\\noindent $\\sum_{j,l=1}^n (Y_n(\\lambda))_{pl} \\mathbb{E}\\left[ \\alpha_v [H_n(\\lambda) -\\mathbb{E}(H_n(\\lambda))] \\alpha_v [R_n(\\lambda ) ]_{lj}(F_n(\\lambda)_{jq} \\right]\n$\\begin{eqnarray*}&=& \\mathbb{E}\\left[ [Y_n (\\lambda)\\hat {\\cal R} R_n(\\lambda ) F_n(\\lambda)]_{pq} \\right]\\\\\n&\\leq&\\Vert F_n(\\lambda) \\Vert \\Vert (Im(\\lambda))^{-1}\\Vert^2 \\mathbb{E}\\left[ \\Vert \\hat {\\cal R} \\Vert \\right]\\\\\n&\n\\leq&\\frac{C \\sqrt{m} \\Vert F_n (\\lambda)\\Vert \\Vert \\alpha_v\\Vert^2}{n}\n \\Vert (Im(\\lambda))^{-1}\\Vert^4\\\\&=&O(1\/n).\\end{eqnarray*}\n where we used \\eqref{Y}, \\eqref{norme} and \\eqref{varhn} in the last lines.\\\\\n\\noindent It is moreover clear that one can find a common polynomial to bound the involved $nO_{p,q}(1\/n)$. \\eqref{YPSIF} follows.\n\\end{proof}\n\n\\begin{corollaire} \\label{estimenunsurn}\nFor any $mn\\times mn$ deterministic matrix $F_n(\\lambda)$ such that $F_n(\\lambda)=O(1)$, we have \\\\\n\n\\noindent \n$\\mathbb{E}\\left[ [ R_n(\\lambda ) F_n(\\lambda)]_{pq} \\right]$ \\begin{eqnarray*}&=& \\left(Y_n(\\lambda)F_n(\\lambda)\\right)_{pq} \n\\\\&&+\\ \\sum_{v=1}^r \\sum_{i,l=1}^n \\frac{(\\kappa_3^{i,l,v} -\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})}{2\\sqrt{2}n\\sqrt{n}} (Y_n(\\lambda))_{pl} \\alpha_v(Y_n(\\lambda))_{ii}\\alpha_v (Y_n(\\lambda))_{ll}\\alpha_v\\\\~&&~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\times \\mathbb{E}\\left[ (R_n(\\lambda)F_n(\\lambda))_{iq} \\right]\\\\&& +O^{(u)}_{p,q}(1\/n).\\end{eqnarray*}\n\\end{corollaire}\n\\begin{proof} \nNoticing that\n\n\\noindent $\\left\\|\n\\sum_{l,j=1}^n \\left(Y_n(\\lambda)\\right)_{pl} O^{(u)}_{l,j}\\left(1\/n^2\\right) \\left(F_n(\\lambda)\\right)_{jq} \\right\\|$\n \\begin{eqnarray*}&\\leq & \n\n O\\left(\\frac{1}{n}\\right)\\left\\{ \\sum_{l=1}^n \\Vert \\left(Y_n(\\lambda)\\right)_{pl} \\Vert^2 \\right\\}^{1\/2} \\left\\{ \\sum_{j=1}^n \\Vert \\left(F_n(\\lambda)\\right)_{jq} \\Vert^2 \\right\\}^{1\/2}\\\\\n\n &=&O_{p,q}^{(u)}(1\/n) ,\n\\end{eqnarray*}\n(using Lemma \\ref{majcarre} and \\eqref{Y} in the last line)\nit readily follows from Theorem \\ref{resolvante} and Proposition \\ref{55} that \\\\\n\n\n\n\\noindent $\\mathbb{E}\\left[ [ R_n(\\lambda ) F_n(\\lambda)]_{pq} \\right]$ \\begin{eqnarray*}&\\hspace*{-0.5cm}=& \\left(Y_n(\\lambda)F_n(\\lambda)\\right)_{pq} \n\\\\&&+ \\sum_{v=1}^r \\sum_{i,l=1}^n \\frac{(\\kappa_3^{i,l,v} -\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})}{2\\sqrt{2}n\\sqrt{n}} (Y_n(\\lambda))_{pl} \\alpha_v\\\\&&~~~~~~~~~~~~~~~~~~~~~~~~~~~\\times \\mathbb{E}\\left[(R_n(\\lambda))_{ii}\\alpha_v(R_n(\\lambda))_{ll}\\alpha_v \n (R_n(\\lambda)F_n(\\lambda))_{iq} \\right]\\\\&& +O^{(u)}_{p,q}(1\/n).\\end{eqnarray*}\n\nTo simplify the writing let us set $U_i=\\alpha_v (R_n(\\lambda))_{ii}$, $V_l=\\alpha_v(R_n(\\lambda))_{ll}$ and $W_i= \\alpha_v \n (R_n(\\lambda)F_n(\\lambda))_{iq}$.\nWe have \\\\\n\n$\\mathbb{E}\\left[U_i V_l W_i\\right]$\n\\begin{eqnarray}&=&\\mathbb{E}\\left[ (U_i-\\mathbb{E}(U_i)) (V_l-\\mathbb{E}(V_l))W_i\\right] \\nonumber\\\\&&+ \\mathbb{E}\\left[ (U_i-\\mathbb{E}(U_i))\\mathbb{E}(V_l) (W_i-\\mathbb{E}(W_i))\\right] \\nonumber\n\\\\&&+ \\mathbb{E}(U_i) \\mathbb{E}\\left[ (V_l-\\mathbb{E}(V_l))(W_i-\\mathbb{E}(W_i))\\right]+\\mathbb{E}\\left[U_i\\right]\n\\mathbb{E}\\left[V_l\\right]\\mathbb{E}\\left[W_i\\right]. \\label{decomposition}\n\\end{eqnarray}\n\n\\noindent Now,\\\\\n\n\\noindent $\\left\\|\\sum_{i,l=1}^n (\\kappa_3^{i,l,v} -\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})(Y_n(\\lambda))_{pl} \\mathbb{E}\\left[ (U_i-\\mathbb{E}(U_i)) (V_l-\\mathbb{E}(V_l))W_i\\right]\\right\\|$ \n\n\\begin{eqnarray*}&\\leq& C\\Vert \\alpha_v \\Vert \\Vert ( \\Im \\lambda)^{-1} \\Vert \\Vert F_n(\\lambda)\\Vert\n\\\\&&~~~~~~\\times \\sum_{i,l=1}^n \\left\\| \n(Y_n(\\lambda))_{pl} \\right\\|\\left\\{\n\\mathbb{E} \\left( \\left\\| U_{i} -\\mathbb{E}\\left( U_i\\right)\\right\\|^2\\right)\\right\\}^{1\/2}\n\\left\\{\\mathbb{E} \\left( \\left\\| V_l -\\mathbb{E}\\left( V_l\\right)\\right\\|^2\\right)\\right\\}^{1\/2}\\\\\n&\\leq &\\sqrt{n} C\\Vert \\alpha_v \\Vert \\Vert {( \\Im \\lambda)}^{-1} \\Vert \\Vert F_n(\\lambda)\\Vert \\left\\{\\sum_{l=1}^n \\left\\| (Y_n(\\lambda))_{pl} \\right\\|^2\\right\\}^{1\/2}\n\\\\&&~~~~~~~~~~~~~~~~~\\times \n\\left\\{\\sum_{l=1}^n \\mathbb{E} \\left( \\left\\|V_l -\\mathbb{E}\\left( V_l\\right)\\right\\|^2\\right)\\right\\}^{1\/2}\\left\\{\\sum_{i=1}^n \\mathbb{E} \\left( \\left\\|U_i -\\mathbb{E}\\left( U_i\\right)\\right\\|^2\\right)\\right\\}^{1\/2}.\\end{eqnarray*}\n\n\\noindent Moreover,\n$$\\left\\|\\sum_{i,l=1}^n (\\kappa_3^{i,l,v} -\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})(Y_n(\\lambda))_{pl} \\mathbb{E}\\left[ (U_i-\\mathbb{E}(U_i))\\mathbb{E}(V_l) (W_i-\\mathbb{E}(W_i))\\right]\\right\\|$$\n\\begin{eqnarray*}&\\leq& C\\Vert \\alpha_v \\Vert \\Vert ( \\Im \\lambda)^{-1} \\Vert \\sum_{i,l=1}^n \\left\\| (Y_n(\\lambda))_{pl} \\right\\|\\\\&&~~~~~~~~~~~~~~~~~\\times \\left\\{\n\\mathbb{E} \\left( \\left\\| U_{i} -\\mathbb{E}\\left( U_i\\right)\\right\\|^2\\right)\\right\\}^{1\/2}\n\\left\\{\\mathbb{E} \\left( \\left\\| W_i -\\mathbb{E}\\left( W_i\\right)\\right\\|^2\\right)\\right\\}^{1\/2}\\\\\n&\\leq &\\sqrt{n} C\\Vert \\alpha_v \\Vert \\Vert {( \\Im \\lambda)}^{-1} \\Vert \n \\left\\{\\sum_{l=1}^n \\left\\| (Y_n(\\lambda))_{pl} \\right\\|^2\\right\\}^{1\/2}\n\\\\&& \\times \\left\\{\\sum_{l=1}^n \\mathbb{E} \\left( \\left\\|W_i -\\mathbb{E}\\left( W_i\\right)\\right\\|^2\\right)\\right\\}^{1\/2} \\left\\{\\sum_{i=1}^n \\mathbb{E} \\left( \\left\\|U_i -\\mathbb{E}\\left( U_i\\right)\\right\\|^2\\right)\\right\\}^{1\/2}.\\end{eqnarray*}\nFinally\n$$\\left\\|\\sum_{i,l=1}^n (\\kappa_3^{i,l,v} -\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})(Y_n(\\lambda))_{pl} \\mathbb{E}\\left[ U_i\\right]\\mathbb{E}\\left[ (V_l-\\mathbb{E}(V_l)) (W_i-\\mathbb{E}(W_i))\\right]\\right\\|$$\n\\begin{eqnarray*}&\\leq& C\\Vert \\alpha_v \\Vert \\Vert ( \\Im \\lambda)^{-1} \\Vert \\sum_{i,l=1}^n \\left\\| (Y_n(\\lambda))_{pl} \\right\\|\n\\\\&& \\times \\left\\{\n\\mathbb{E} \\left( \\left\\| V_{l} -\\mathbb{E}\\left( V_l\\right)\\right\\|^2\\right)\\right\\}^{1\/2}\n\\left\\{\\mathbb{E} \\left( \\left\\| W_i -\\mathbb{E}\\left( W_i\\right)\\right\\|^2\\right)\\right\\}^{1\/2}\\\\\n&\\leq &\\sqrt{n} C\\Vert \\alpha_v \\Vert \\Vert {( \\Im \\lambda)}^{-1} \\Vert \n \\left\\{\\sum_{l=1}^n \\left\\| (Y_n(\\lambda))_{pl} \\right\\|^2\\right\\}^{1\/2}\n\\\\&& \\times \\left\\{\\sum_{l=1}^n \\mathbb{E} \\left( \\left\\|V_l -\\mathbb{E}\\left( V_l\\right)\\right\\|^2\\right)\\right\\}^{1\/2}\\left\\{\\sum_{i=1}^n \\mathbb{E} \\left( \\left\\|W_i -\\mathbb{E}\\left( W_i\\right)\\right\\|^2\\right)\\right\\}^{1\/2}.\\end{eqnarray*}\n\n\n\n\n \\noindent Using Lemma \\ref{var}, (\\ref{l}) and \\eqref{Y}, we readily deduce that \\\\\n\n\n\\noindent $\\mathbb{E}\\left[ [ R_n(\\lambda ) F_n(\\lambda)]_{pq} \\right]$ \\begin{eqnarray}&=& \\left(Y_n(\\lambda)F_n(\\lambda)\\right)_{pq} \\nonumber\n\\\\&&+\\frac{1}{2\\sqrt{2}n\\sqrt{n}}\\sum_{v=1}^r \\sum_{i,l=1}^n (\\kappa_3^{i,l,v} -\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})(Y_n(\\lambda)_{pl} \\alpha_v\\\\&&~~~~~~~~~~~~~~~~~~~~~~~\\times\\mathbb{E}\\left[(R_n(\\lambda))_{ii}\\right]\n\\alpha_v\\mathbb{E}\\left[(R_n(\\lambda))_{ll}\\right]\\alpha_v \\mathbb{E} \\left[ (R_n(\\lambda)F_n(\\lambda))_{iq} \\right] \\nonumber \\\\&& +O^{(u)}_{p,q}(1\/{n}). \\label{eq}\\end{eqnarray}\n\\noindent Now, define \n$${\\cal R}= \\sum_{v=1}^r\\sum_{i,l=1}^n (\\kappa_3^{i,l,v} -\\tilde \\kappa_3^{i,l,v}\\sqrt{-1}) \\alpha_v\\mathbb{E}\\left[(R_n(\\lambda))_{ii}\\right]\\alpha_v\\mathbb{E}\n\\left[(R_n(\\lambda))_{ll}\\right]\\alpha_v\\otimes E_{li},$$\nIt is easy to see that $\\Vert {\\cal R}\\Vert\\leq C \\Vert (\\Im \\lambda)^{-1}\\Vert^2 n.$\nWe have \\\\\n\n\\noindent $ \\sum_{v=1}^r \\sum_{i,l=1}^n(\\kappa_3^{i,l,v} -\\tilde \\kappa_3^{i,l,v}\\sqrt{-1}) (Y_n(\\lambda))_{pl} \\alpha_v\\mathbb{E}\\left[(R_n(\\lambda))_{ii}\\right] $ $$ ~~~~~~~~~~\\times \\alpha_v\\mathbb{E}\\left[(R_n(\\lambda))_{ll}\\right]\\alpha_v\\mathbb{E} \\left[ (R_n(\\lambda)F_n(\\lambda))_{iq} \\right]= \\left[ Y_n(\\lambda) {\\cal R} \\mathbb{E} (R_n(\\lambda) F_n(\\lambda))\\right]_{pq}.$$\nSo that if we define $$T=\\sum_{p,q=1}^n T_{pq}\\otimes E_{pq}$$ where \n$$T_{pq}\n= \\frac{1}{2\\sqrt{2}n\\sqrt{n}}\\sum_{v=1}^r \\sum_{l,i=1}^n (\\kappa_3^{i,l,v} -\\tilde \\kappa_3^{i,l,v}\\sqrt{-1}) (Y_n(\\lambda))_{pl} \\alpha_v\\mathbb{E}\\left[(R_n(\\lambda))_{ii}\\right]$$ $$~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\times\\alpha_v\\mathbb{E}\\left[(R_n(\\lambda))_{ll}\\right]\n\\alpha_v\\mathbb{E} \\left[ (R_n(\\lambda)F_n(\\lambda))_{iq} \\right],$$\nwe have \\begin{equation}\\label{Q}\\Vert T\\Vert=\\left\\| \\frac{1}{2\\sqrt{2}n\\sqrt{n}} Y_n(\\lambda) {\\cal R} \\mathbb{E} (R_n(\\lambda) F_n(\\lambda)) \\right\\|=O(1\/\\sqrt{n}).\\end{equation}\nHence, in particular we have \\begin{equation}\\label{estimenracinen} \\mathbb{E}\\left[ [ R_n(\\lambda ) F_n]_{pq} \\right]= \\left(Y_n(\\lambda)F_n(\\lambda)\\right)_{pq} +O^{(u)}_{p,q}(1\/\\sqrt{n}). \\end{equation}\n\n \n\\noindent Now, \nwe have \n$$\\left\\| \\sum_{i,l=1}^n \\frac{ (\\kappa_3^{i,l,v} -\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})}{2\\sqrt{2}n\\sqrt{n}} (Y_n(\\lambda))_{pl} \\alpha_vO^{(u)}_{i,i}(\\frac{1}{\\sqrt{n}})\\alpha_v(Y_n(\\lambda))_{ll}\\alpha_v\\mathbb{E} \\left[ (R_n(\\lambda)F_n(\\lambda))_{iq} \\right] \\right\\|$$\n\\begin{eqnarray}& \\leq& \\frac{C \\Vert(\\Im \\lambda)^{-1} \\Vert }{n} \\left( \\sum_{l=1}^n \\Vert (Y_n(\\lambda))_{pl} \\Vert^2 \\right)^{1\/2} \\left\\{\\sum_{i=1}^n \n\\left\\| \\mathbb{E} \\left[(R_n(\\lambda)F_n(\\lambda))_{iq}\\right] \\right\\|^2 \\right\\}^{1\/2}\\nonumber \\\\&&~~~~~~~~~~~~~~~~~~~~~~~~~\\times \\left\\{\\sum_{i=1}^n \\Vert O^{(u)}_{i,i}(1\/{\\sqrt{n}})\\Vert^2\\right\\}^{1\/2} \\nonumber \\\\ & =& O^{(u)}_{p,q}(1\/n) \\label{eq2} \n\\end{eqnarray}\nwhere we used (\\ref{l}) twice and \\eqref{Y} and \\eqref{norme} in the last line.\nSimilarly $$\n\\left\\| \\sum_{i,l=1}^n \\frac{(\\kappa_3^{i,l,v} -\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})}{2\\sqrt{2}n\\sqrt{n}} (Y_n(\\lambda))_{pl} \\alpha_v(Y_n(\\lambda))_{ii}\\alpha_vO_{l,l}(\\frac{1}{\\sqrt{n}})\\alpha_v\\mathbb{E} \\left[ (R_n(\\lambda)F_n(\\lambda))_{iq} \\right] \\right\\|$$ \\begin{equation}\\label{eq3}=O^{(u)}_{p,q}(\\frac{1}{{n} }), \\end{equation} \n$$\n\\left\\| \\sum_{i,l=1}^n \\frac{ (\\kappa_3^{i,l,v} -\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})}{2\\sqrt{2}n\\sqrt{n}}(Y_n(\\lambda))_{pl} \\alpha_vO_{i,i}(\\frac{1}{\\sqrt{n}})\\alpha_vO_{l,l}(1\/\\sqrt{n})\\alpha_v\\mathbb{E} \\left[ (R_n(\\lambda)F_n(\\lambda))_{iq} \\right] \\right\\|$$ \\begin{equation}\\label{eq4}=O^{(u)}_{p,q}(\\frac{1}{{n \\sqrt{n}} }) \\end{equation} \n\n\n(\\ref{eq}), (\\ref{estimenracinen}) and (\\ref{eq2}), (\\ref{eq3}), \\eqref{eq4} readily yields Corollary \\ref{estimenunsurn}.\n\\end{proof}\n\\begin{corollaire} \\label{ME} With the notations of Section \\ref{Notations},\n \n \\begin{eqnarray*}G_n(\\lambda) &=&\\mathbb{E} \\left({\\rm id}_m\\otimes tr_n R_n(\\lambda)\\right)\\\\\n&=&G_{\\sum_{u=1}^t\\beta_u \\otimes a_n^{(u)}}\\left(\\lambda -\\gamma -\\sum_{v=1}^r \\alpha_v G_n(\\lambda)\\alpha_v \\right)\n\\\\&&+L_n(\\lambda)\n+\\epsilon_n(\\lambda)\n \\end{eqnarray*}\n where \n$$L_n(\\lambda)=\\frac{1}{n}\\sum_{p=1}^n \\left[Y_n(\\lambda)\n\\Psi(\\lambda) \\right]_{pp},$$\n(with $\\Psi(\\lambda)$ defined in Theorem \\ref{resolvante} and $Y_n(\\lambda)$ defined in Lemma \\ref{inverseY})\n and $$ \\epsilon_n(\\lambda) =O \\left( \\frac{1}{n\\sqrt{n}}\\right).$$\n Moreover \\begin{equation} \\label{L} L_n(\\lambda) =O \\left( \\frac{1}{\\sqrt{n}}\\right).\\end{equation}\n\n\n \\end{corollaire}\n\\begin{proof}\nFirst note that, since the distribution of $a_n=(a_n^{(1)},\\ldots,a_n^{(t)})$ in $({\\cal A},\\tau)$ coincides with the distribution of $(A_n^{(1)},\\ldots, A_n^{(t)})$ in $({ M}_n(\\mathbb{C}),\\tr_n)$, we have \\begin{eqnarray*}{\\rm id}_m\\otimes tr_n Y_n(\\lambda)&= &{\\rm id}_m\\otimes \\tau \\left((\\lambda -\\gamma -\\sum_{v=1}^r \\alpha_v G_n(\\lambda)\\alpha_v)\\otimes 1_{\\cal A}-\\sum_{u=1}^t\\beta_u \\otimes a_n^{(u)} \\right)^{-1}\n\\\\&=& G_{\\sum_{u=1}^t\\beta_u \\otimes a_n^{(u)}}\\left(\\lambda -\\gamma -\\sum_{v=1}^r \\alpha_v G_n(\\lambda)\\alpha_v \\right).\\end{eqnarray*}\nThen, the corollary readily follows from Theorem \\ref{resolvante} and Proposition \\ref{55} by noting that \n\\begin{itemize}\n\\item \n\n\\noindent \n$\\frac{1}{n}\\Vert \\sum_{p,l=1}^n (Y_n(\\lambda))_{pl}O_{lp}^{(u)}(1\/n^2)\\Vert$ \\begin{eqnarray*} & \\leq &\\frac{1}{n}\\left(\\sum_{p,l=1}^n \\Vert (Y_n(\\lambda))_{pl}\\Vert ^2 \\right)^{1\/2}\\left( \\sum_{p,l=1}^n \\Vert O^{(u)}_{lp}(1\/n^2)\\Vert ^2 \\right)^{1\/2}\\\\&=&O(\\frac{1}{n\\sqrt{n}})\\end{eqnarray*}\nwhere we used Lemma \\ref{majcarre} and \\eqref{Y} in the last line.\n\\item $$ \\Vert \\sum_{i,l,p=1}^n \\frac{(\\kappa_3^{i,l,v} -\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})}{n^2\\sqrt{n}}(Y_n(\\lambda))_{pl} \\mathbb{E} \\{\\alpha_v(R_n(\\lambda))_{ii}\\alpha_v(R_n(\\lambda))_{ll}\\alpha_v \n (R_n(\\lambda))_{ip} \\Vert $$\n$$\\leq \\frac{C\\Vert \\alpha_v \\Vert^3 \\Vert (\\Im \\lambda) ^{-1}\\Vert^2}{n^2} \\sum_{l,p=1}^n \\Vert (Y_n(\\lambda))_{pl} \\Vert \\left(\\sum_{i=1}^n \\Vert(R_n(\\lambda))_{ip} \\Vert^2\\right)^{1\/2}$$\n$$ \\leq \\frac{ C\\sqrt{m}\\Vert \\alpha_v \\Vert^3 \\Vert (\\Im \\lambda )^{-1}\\Vert^3}{n} \\left( \\sum_{p,l=1}^n \\Vert (Y_n(\\lambda))_{pl}\\Vert ^2 \\right)^{1\/2} =O(1\/\\sqrt{n})$$\nwhere we used Lemma \\ref{majcarre}, \\eqref{norme} and \\eqref{Y} in the two last lines.\n\\end{itemize}\n\\end{proof}\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\\begin{theoreme}\\label{difftilde}\nLet $\\lambda$ be in $M_m(\\mathbb{C})$ such that \n $\\Im \\lambda>0$, and $\\tilde G_n(\\lambda)$ as defined in \\eqref{defGntilde}. We have \n\\begin{equation} \\label{prediff}\nG_n(\\lambda)-\\tilde G_n(\\lambda)+{E_n(\\lambda)}= O(\\frac{1}{n\\sqrt{n}}),\n\\end{equation}\nwhere $E_n(\\lambda)$ is given by\\\\\n\n\\noindent $E_n(\\lambda) =$\n\\begin{equation}\n \\sum_{v=1}^r \\tilde G_n'(\\lambda) \\cdot \\alpha_v L_n(\\lambda) \\alpha_v -\\frac{1}{2} \\tilde G_n''(\\lambda) \\cdot\\left( \\sum_{v=1}^r \\alpha_v L_n(\\lambda) \\alpha_v, \\sum_{v=1}^r \\alpha_v L_n(\\lambda) \\alpha_v\\right) -L_n(\\lambda) \n\\end{equation}\nwith $L_n(\\lambda)$ defined in Corollary \\ref{ME}.\n\\end{theoreme}\n\\begin{proof}\nLet $\\lambda$ be in $M_m(\\mathbb{C})$ such that \n $\\Im \\lambda>0$. Note that according to \\eqref{image}, we have $\\Im \\left( \\lambda-\\gamma - \\sum_{v=1}^r \\alpha_v G_n(\\lambda)\\alpha_v\\right)>0.$\nDefine (using the notations of Section \\ref{Notations})\n\\begin{equation}\\label{lambdan}\\Lambda_n(\\lambda)= \\gamma +\\lambda + \\sum_{v=1}^r \\alpha_v G_{\\sum_{u=1}^t \\beta_u \\otimes a_n^{(u)}}(\\lambda) \\alpha_v\\end{equation}\nand $\\lambda'=\\Lambda_n(\\lambda-\\gamma - \\sum_{v=1}^r \\alpha_v G_n(\\lambda)\\alpha_v).$\nUsing Corollary \\ref{ME}, we have \n\\begin{eqnarray}\\lambda'-\\lambda &= & - \\sum_{v=1}^r \\alpha_v G_n(\\lambda) \\alpha_v + \\sum_{v=1}^r \\alpha_v G_{\\sum_{u=1}^t \\beta_u \\otimes a_n^{(u)}}(\\lambda -\\gamma -\\sum_{v=1}^r \\alpha_v G_n(\\lambda ) \\alpha_v) \\alpha_v \\nonumber \\\\&=& -\\sum_{v=1}^r \\alpha_v L_n(\\lambda) \\alpha_v +O(\\frac{1}{n\\sqrt{n}})\\label{lambdaprimemoinslambdaavant}\\\\&=& O(1\/\\sqrt{n}). \\label{lambdaprimemoinslambda}\n\\end{eqnarray}\n\n\n\n\\noindent Thus there exists a polynomial $Q$ with nonnegative coefficients \nsuch that $$\\left\\|\\lambda'-\\lambda\\right\\|\\leq \\frac{Q(\\Vert (\\Im \\lambda )^{-1}\\Vert)}{\\sqrt{n}}.$$\n-On the one hand, if $$\\frac{Q(\\Vert (\\Im \\lambda )^{-1}\\Vert)}{\\sqrt{n}}\\geq \\frac{1}{2\\Vert (\\Im \\lambda )^{-1}\\Vert},$$ \nor equivalently \n\\begin{equation} \\label{1=O(1\/n)}\n1\\leq \\frac{2\\Vert (\\Im \\lambda )^{-1}\\Vert Q(\\Vert (\\Im \\lambda )^{-1}\\Vert)}{\\sqrt{n}},\n\\end{equation}\nto prove \\eqref{prediff}\nit is enough to prove that \n\\begin{equation} \\label{O(1)}\nG_n(\\lambda)-\\tilde G_n(\\lambda)+E_n(\\lambda) = O(1).\n\\end{equation}\nIndeed, if we assume that \\eqref{1=O(1\/n)} and \\eqref{O(1)} hold, \nthen there exists a polynomial $\\tilde Q$ with nonnegative coefficients \nsuch that \n\\begin{eqnarray*}\n\\left\\|G_n(\\lambda)-\\tilde G_n(\\lambda)+E_n(\\lambda) \\right\\|&\\leq &\\tilde Q(\\Vert (\\Im \\lambda )^{-1}\\Vert)\\\\\n&\\leq &\\tilde Q(\\Vert ( \\Im \\lambda )^{-1}\\Vert)\\frac{2\\Vert ( \\Im \\lambda )^{-1}\\Vert Q(\\Vert( \\Im \\lambda )^{-1}\\Vert)}{\\sqrt{n}}\\\\\n&\\leq &\\tilde Q(\\Vert (\\Im \\lambda )^{-1}\\Vert)(\\frac{2\\Vert (\\Im \\lambda) ^{-1}\\Vert Q(\\Vert (\\Im \\lambda )^{-1}\\Vert)}{\\sqrt{n}})^4.\n\\end{eqnarray*}\nHence, $$G_n(\\lambda)-\\tilde G_n(\\lambda)+E_n(\\lambda) = O(\\frac{1}{n^2})$$\nso that \\eqref{prediff} holds.\nTo prove \\eqref{O(1)}, one can notice that, using \\eqref{norme} and \\eqref{normeG},\n both $G_n(\\lambda)$ and $\\tilde G_n(\\lambda)$ \nare bounded by $\\Vert (\\Im \\lambda) ^{-1}\\Vert$, and that \\\\\n\n\\noindent \n$\\left\\|E_n(\\lambda)\\right\\|$ $$\\leq \\left\\{r \\max_{v=1}^r\\Vert \\alpha_v\\Vert^2 \\Vert (\\Im \\lambda )^{-1}\\Vert^2 +1\\right\\} \\left\\|L_n(\\lambda)\\right\\|+r^2 \\max_{v=1}^r\\Vert \\alpha_v\\Vert^4 \\Vert (\\Im \\lambda )^{-1}\\Vert^3 \\left\\|L_n(\\lambda)\\right\\|^2 ,$$\nwhere $L_n(\\lambda)=O(1\/\\sqrt{n})$ according to \\eqref{L}.\\\\\n~~\n\n\\noindent -On the other hand, if $$\\frac{Q(\\Vert (\\Im \\lambda )^{-1}\\Vert)}{\\sqrt{n}}\\leq \\frac{1}{2\\Vert (\\Im \\lambda )^{-1}\\Vert},$$ \none has : \n\\begin{equation}\\label{lambdaprime}\\left\\|\\Im \\lambda'-\\Im \\lambda\\right\\|\\leq \\left\\|\\lambda'-\\lambda \\right\\|\\leq \\frac{1}{2\\Vert ( \\Im \\lambda) ^{-1}\\Vert}\\end{equation}\nDenoting for any Hermitian matrix $H$ by $l_1(H)$ the smallest eigenvalue of $H$, we readily deduce from \\eqref{lambdaprime} that \n $l_1(\\Im \\lambda')\\geq \\frac{l_1(\\Im \\lambda)}{2}$ and \ntherefore \\begin{equation}\\label{lambdaprimepositif}\\Im \\lambda' >0. \\end{equation}\nThen, it makes sense to consider $\\tilde G_{n}(\\lambda')$ which satisfies according to \\eqref{subor}\n\\begin{eqnarray}\\tilde G_{n}(\\lambda')&= &G_{\\sum_{u=1}^t \\beta_u \\otimes a_n^{(u)}}(\\lambda'-\\gamma -\\sum_{v=1}^r\\alpha_v \\tilde G_n(\\lambda')\\alpha_v) \\nonumber\\\\&=&\nG_{\\sum_{u=1}^t \\beta_u \\otimes a_n^{(u)}}\\left(\\omega_n(\\lambda')\\right) \\nonumber\\\\& =&G_{\\sum_{u=1}^t \\beta_u \\otimes a_n^{(u)}}\\left(\\omega_n(\\Lambda_n(\\lambda -\\gamma -\\sum_{v=1}^r \\alpha_v G_n(\\lambda)\\alpha_v ))\\right). \\label{subordin}\\end{eqnarray}\n\n\n\n\n\n\n\n\n\nApplying Lemma \\ref{inversion} to $\\rho=\\lambda -\\gamma -\\sum_{v=1}^r\\alpha_v G_n(\\lambda)\\alpha_v $ ( using \\eqref{image} and \\eqref{lambdaprimepositif}) we obtain that \n since $\\Im \\lambda'=\\Im \\Lambda_n(\\rho)>0$ we have $\\omega_n(\\Lambda_n(\\rho))=\\rho$ and according to \\eqref{subordin}\n$$ \\tilde G_{n}\\left(\\lambda'\\right)\n=G_{\\sum_{u=1}^t \\beta_u \\otimes a_n^{(u)}}\\left(\\lambda -\\gamma -\\sum_{v=1}^r \\alpha_v G_n(\\lambda)\\alpha_v \\right). $$\nHence, by Corollary \\ref{ME}, we have\n\\begin{equation} \\label{termone}\nG_n(\\lambda)-\\tilde G_n(\\lambda')-{L_n(\\lambda)}=O(\\frac{1}{n\\sqrt{n}}).\n\\end{equation}\nNow, we have \\\\\n\n\\noindent $\\tilde G_n(\\lambda')-\\tilde G_n(\\lambda)$\n\\begin{eqnarray*}\n&=& {\\rm id}_m \\otimes \\tau \\left\\{ r_n(\\lambda')\\left[ (\\lambda-\\lambda') \\otimes 1_{\\cal A}\\right] r_n(\\lambda)\\right\\}\\\\\n&=& {\\rm id}_m\\otimes \\tau \\left\\{ r_n(\\lambda)\\left[(\\lambda-\\lambda') \\otimes 1_{\\cal A}\\right] r_n(\\lambda)\\right\\} \\\\&&+ \n{\\rm id}_m \\otimes \\tau \\left\\{ r_n(\\lambda)\\left[ (\\lambda-\\lambda') \\otimes1_{\\cal A}\\right] r_n(\\lambda) \\left[ (\\lambda-\\lambda') \\otimes 1_{\\cal A}\\right] r_n(\\lambda) \\right\\}\n\\\\&&+ \n{\\rm id}_m \\otimes \\tau \\left\\{ r_n(\\lambda')\\left[ (\\lambda-\\lambda') \\otimes1_{\\cal A}\\right] r_n(\\lambda)\\left[ (\\lambda-\\lambda') \\otimes1_{\\cal A}\\right] r_n(\\lambda) \\left[(\\lambda-\\lambda') \\otimes 1_{\\cal A}\\right] r_n(\\lambda) \\right\\}\\\\\n&=&\\sum_{v=1}^r {\\rm id}_m \\otimes \\tau \\left\\{ r_n(\\lambda)\\left[\\alpha_v L_n(\\lambda)\\alpha_v \\otimes 1_{\\cal A}\\right] r_n(\\lambda)\\right\\} \\\\&&+\\sum_{v,v'=1}^r {\\rm id}_m \\otimes \\tau \\left\\{ r_n(\\lambda)\\left[\\alpha_v L_n(\\lambda)\\alpha_v \\otimes 1_{\\cal A}\\right] r_n(\\lambda)\\left[\\alpha_{v'} L_n(\\lambda)\\alpha_{v'} \\otimes 1_{\\cal A}\\right] r_n(\\lambda)\\right\\}\n\\\\&& \n+O(\\frac{1}{n\\sqrt{n}})\n\\end{eqnarray*}\nwhere we used \\eqref{lambdaprimemoinslambdaavant}, \\eqref{lambdaprimemoinslambda} and \\eqref{normeG} in the last line.\nHence we have \n$$\n\\tilde G_n(\\lambda')-\\tilde G_n(\\lambda)+\\sum_{v=1}^r\\tilde G_n'(\\lambda) \\cdot \\alpha_v L_n(\\lambda) \\alpha_v\n-\\frac{1}{2}\\tilde G_n''(\\lambda) \\cdot\\left(\\sum_{v=1}^r \\alpha_v L_n(\\lambda) \\alpha_v, \\sum_{v=1}^r \\alpha_v L_n(\\lambda) \\alpha_v\\right)$$\n\\begin{equation} \\label{termtwo}~~~~~~~~~~~~~~~~~~~~~~~~~~=O(\\frac{1}{n\\sqrt{n}}).\n\\end{equation}\n(\\ref{prediff}) follows from \\eqref{termone} and \\eqref{termtwo} since \\\\\n\n\\noindent \n$\\left\\|G_n(\\lambda)-\\tilde G_n(\\lambda)+{E_n(\\lambda)}\\right\\|\\leq \n\\left\\|G_n(\\lambda)-\\tilde G_n(\\lambda')-{L_n(\\lambda)}\\right\\|$\n$$+\\left\\|\\tilde G_n(\\lambda')-\\tilde G_n(\\lambda)+\\sum_{p=1}^r\\tilde G_n'(\\lambda)\\cdot \\alpha_v{L_n(z)}\\alpha_v -\\frac{1}{2}\\tilde G_n''(\\lambda) \\cdot\\left(\\sum_{p=1}^r \\alpha_v L_n(\\lambda) \\alpha_v, \\sum_{p=1}^r \\alpha_v L_n(\\lambda) \\alpha_v\\right)\\right\\|.\n$$\n\n\\end{proof}\n\\begin{remarque}\n\\eqref{L} and \\eqref{normeG} readily yield that $E_n(\\lambda)=O(\\frac{1}{\\sqrt{n}})$. Thus, we can deduce from (\\ref{prediff}) that \\begin{equation}\\label{difgngntilde}G_n( \\lambda)-\\tilde G_n(\\lambda) =O(\\frac{1}{\\sqrt{n}}).\\end{equation}\n\\end{remarque}\n\n\n\n\\begin{proposition} \\label{estimdiff}\n For $z \\in \\C \\setminus \\R$, let $g_n(z)$ and $\\tilde g_n(z)$ as defined in \\eqref{defpetitg} and \\eqref{defpetitgtilde} respectively. We have \n\\begin{equation} \\label{estimdiffeqn}\ng_n(z)-\\tilde g_n(z)+{\\tilde{E}_n(z)} = O(\\frac{1}{n\\sqrt{n}}),\n\\end{equation}\nwhere $\\tilde{E}_n(z)$ is given by\n\\begin{equation}\\label{defentilde}\n\\tilde{E}_n(z) = \\sum_{v=1}^r tr_m\\left( \\tilde G_n'(zI_m)\\cdot \\alpha_v \\tilde{L}_n(z)\\alpha_v\\right) - \\tr_m \\tilde L_n(z)\n\\end{equation}\n{with }\\\\\n\n$\\displaystyle{\\tilde{L}_n(z)=\\sum_{v=1}^r}$ \\begin{eqnarray*} && \\bigg\\{\n\\sum_{i,l,p=1}^n \\frac{(\\kappa_4^{i,l,v} +\\tilde \\kappa_4^{i,l,v})}{4n^3} \\tilde Y_n(zI_m)_{pl} \\alpha_v (\\tilde Y_n(zI_m))_{ii} \\alpha_v (\\tilde Y_n(zI_m))_{ll} \\alpha_v (\\tilde Y_n(zI_m))_{ii} \\alpha_v \\left(\\tilde Y_n(zI_m)\\right)_{lp}\\\\\n&&+ \\sum_{i,l,p=1}^n \\frac{(\\kappa_3^{i,l,v} +\\tilde \\kappa_3^{i,l,v}\\sqrt{-1}) }{2\\sqrt{2} n^2 \\sqrt{n} } \\tilde Y_n(zI_m)_{pl} \\alpha_v (\\tilde Y_n(zI_m))_{ii} \\alpha_v (\\tilde Y_n(zI_m))_{li} \\alpha_v \\left( \\tilde Y_n(zI_m)\\right)_{lp} \\\\\n&&+\\sum_{i,l,p=1}^n \\frac{ (\\kappa_3^{i,l,v} -\\tilde \\kappa_3^{i,l,v}\\sqrt{-1}) }{2\\sqrt{2} n^2 \\sqrt{n} } \\tilde Y_n(zI_m)_{pl} \\alpha_v (\\tilde Y_n(zI_m))_{ii} \\alpha_v (\\tilde Y_n(zI_m))_{ll} \\alpha_v \\left(\\tilde Y_n(zI_m)\\right)_{ip}\\\\\n&&+\\sum_{i,l,p=1}^n \\frac{(\\kappa_3^{i,l,v} -\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})}{2\\sqrt{2} n^2 \\sqrt{n} } \\tilde Y_n(zI_m)_{pl} \\alpha_v (\\tilde Y_n(zI_m))_{il} \\alpha_v (\\tilde Y_n(zI_m))_{ii} \\alpha_v \\left (\\tilde Y_n(zI_m)\\right)_{lp}\\bigg\\}\n\\end{eqnarray*}\nwhere $\\tilde Y_n$ and $\\tilde G_n$ were defined in \\eqref{ytilde} and \\eqref{defGntilde} respectively so that \n \\begin{eqnarray*}\\tilde Y_n(zI_m)&= &\\left((zI_m -\\gamma -\\sum_{v=1}^r\\alpha_v \\tilde G_n(zI_m)\\alpha_v)\\otimes I_n - \\sum_{u=1}^t \\beta_u \\otimes A_n^{(u)} \\right)^{-1}\\\\&=&\\left(\\omega_n(zI_m)\\otimes I_n - \\sum_{u=1}^t \\beta_u \\otimes A_n^{(u)} \\right)^{-1}\\end{eqnarray*}\nand \\begin{eqnarray*}\\tilde G_n(zI_m)&=&{\\rm id}_m\\otimes \\tau\\left( (zI_m-\\gamma) \\otimes 1_{\\cal A} - \\sum_{v=1}^r \\alpha_v \\otimes x_v-\\sum_{u=1}^t \\beta_u \\otimes a_n^{(u)}\\right)^{-1}\\\\&=&{\\rm id}_m \\otimes \\tau\\left( zI_m\\otimes 1_{\\cal A} - s_n\\right)^{-1} .\\end{eqnarray*}\n\\end{proposition}\n\\begin{proof}\nLet $z\\in \\mathbb{C}\\setminus \\mathbb{R}$ such that $\\Im z >0$.\nTheorem \\ref{difftilde} yields $$ g_n(z)- \\tilde g_n(z)+\\sum_{v=1}^r tr_m\\tilde G_n'(zI_m) \\cdot \\alpha_v L_n(zI_m) \\alpha_v -tr_mL_n(zI_m)\n$$ \\begin{equation}\\label{doubleetoile}-\\frac{1}{2} \\tr_m \\tilde G_n''(zI_m) \\cdot\\left( \\sum_{v=1}^r \\alpha_v L_n(zI_m) \\alpha_v, \\sum_{v=1}^r \\alpha_v L_n(zI_m) \\alpha_v\\right)=O(\\frac{1}{n\\sqrt{n}}).\\end{equation}\nFirst note that, by Riesz-Fr\\'echet's Theorem (and using \\eqref{normeG} and \\eqref{L}), there exists $B_n^{(1)}(z)$ and $B_n^{(2)}(z)$ in $M_m(\\C)$ such that $$\\Vert B_n^{(1)}(z) \\Vert_2 \\leq \\vert \\Im z \\vert^{-2} \\left( \\sum_{v=1}^r \\Vert \\alpha_v\\Vert^2 \\right)=O(1), $$ \\begin{equation}\\label{B2}\\Vert B_n^{(2)}(z) \\Vert_2=O(1\/\\sqrt{n}), \\end{equation} and \n$$\\sum_{v=1}^r \\tr_m\\tilde G_n'(zI_m) \\cdot \\alpha_v L_n(zI_m) \\alpha_v = \\Tr_m \\left[ B_n^{(1)}(z) L_n(zI_m) \\right],$$\n\\begin{equation}\\label{etoilehat}\\tr_m \\tilde G_n''(zI_m) \\cdot\\left( \\sum_{v=1}^r \\alpha_v L_n(zI_m) \\alpha_v, \\sum_{v=1}^r \\alpha_v L_n(zI_m) \\alpha_v\\right)= \\Tr_m \\left[ B_n^{(2)}(z) L_n(zI_m) \\right].\\end{equation}\n\n\\noindent Recall that for $\\lambda \\in M_m(\\C)$ such that $\\Im \\lambda>0$, $$L_n(\\lambda)=\\frac{1}{n}\\sum_{p=1}^n \\left[Y_n(\\lambda)\n\\Psi(\\lambda) \\right]_{pp},$$ where $\\Psi$ is defined in \\eqref{psi}.\nFirst, note that according to \\eqref{YPSIF}, we have (setting $c_{l,i,v}= \\frac{(\\kappa_3^{i,l,v} -\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})}{2\\sqrt{2}} $)\\\\\n~~\n\n\\noindent $\\Tr_m \\left[ B_n^{(2)}(z) L_n(zI_m) \\right]$\n\\begin{eqnarray*} \n\\hspace*{-1.6cm}=&\\hspace*{-0.3cm}\n\\displaystyle{ \\sum_{v=1}^r \\sum_{i,l=1}^n} \\frac{c_{l,i,v}}{n^2\\sqrt{n}} \\mathbb{E} \\Tr_m \\left\\{\\alpha_v(R_n(zI_m))_{ii}\\alpha_v(R_n(zI_m))_{ll}\\alpha_v \n \\left(R_n(zI_m) (B_n^{(2)}(z)\\otimes I_n)Y_n(zI_m)\\right)_{il}\\right\\}\n\\\\&+O(\\frac{1}{n\\sqrt{n}}).\n\\end{eqnarray*}\nMoreover, we have \n $$ \\sum_{v=1}^r\\sum_{i,l=1}^n \\frac{ c_{l,i,v}}{n^2\\sqrt{n}} \\mathbb{E} \\Tr_m \\left\\{\\alpha_v(R_n(zI_m))_{ii}\\alpha_v(R_n(zI_m))_{ll}\\alpha_v \n \\left(R_n(zI_m) (B_n^{(2)}(z)\\otimes I_n)Y_n(zI_m)\\right)_{il}\\right\\}$$ \n$$= O(\\frac{1}{n\\sqrt{n}}),$$\nwhere we used \\eqref{Odenracinepas}, \\eqref{norme}, \\eqref{Y} and \\eqref{B2}. \nHence \\begin{equation}\\label{etoiletilde}\\Tr_m \\left[ B_n^{(2)}(z) L_n(zI_m) \\right]= O(\\frac{1}{n\\sqrt{n}}).\\end{equation}\n\\eqref{doubleetoile}, \\eqref{etoilehat} and \\eqref{etoiletilde} yield \\begin{equation}\\label{doubleetoilehat}g_n(z)- \\tilde g_n(z)+\\sum_{v=1}^r tr_m\\tilde G_n'(zI_m) \\cdot \\alpha_v L_n(zI_m) \\alpha_v -tr_mL_n(zI_m)=O(\\frac{1}{n\\sqrt{n}}).\\end{equation}\n Thus, in the following we will consider $\\frac{1}{n}\\sum_{p=1}^n \\tr_m B_n(\\lambda) \\left[Y_n(\\lambda)\nT(\\lambda) \\right]_{pp}$ for any $\\lambda\\in M_m\\left( \\mathbb{C}\\right)$ such that $\\Im \\lambda >0$, for each term $T(\\lambda)$ involving in \\eqref{psi} and any $m\\times m$ matrix $B_n(\\lambda)=O(1)$ (in the interests of simplifying notations, we deal with any $\\lambda\\in M_m\\left( \\mathbb{C}\\right)$ such that $\\Im \\lambda >0$ instead of $zI_m$).\nWe set ${\\cal B}(\\lambda)= B_n(\\lambda) \\otimes I_{n}$. \n\n\n\\noindent First, for any fixed $v \\in \\{1,\\ldots,r\\}$,\\\\\n \n$\\left|\\frac{1}{n}\\sum_{p,l=1}^n \\tr_m B_n(\\lambda)(Y_n(\\lambda))_{pl} \\mathbb{E}\\left[ \\alpha_v [H_n(\\lambda) -\\mathbb{E}(H_n(\\lambda))] \\alpha_v [R_n(\\lambda ) ]_{lp} \\right]\\right|$\n\\begin{eqnarray*}\n&=& \\left|\\tr_m \\mathbb{E}\\left[ \\alpha_v [H_n(\\lambda) -\\mathbb{E}(H_n(\\lambda))] \\alpha_v id\\otimes tr_n [R_n(\\lambda ) {\\cal B}(\\lambda)Y_n(\\lambda)] \\right]\\right|\\\\\n&=& \\left|\\tr_m \\mathbb{E}\\left\\{ \\alpha_v [H_n(\\lambda) -\\mathbb{E}(H_n(\\lambda))] \\alpha_v\\right. \\right.\\\\&&\\left.\\left.~~~~~~\\times \\left[id\\otimes tr_n [R_n(\\lambda ) {\\cal B}(\\lambda)Y_n(\\lambda)]\n-\\mathbb{E}(id\\otimes tr_n [R_n(\\lambda ) {\\cal B}(\\lambda)Y_n(\\lambda)]) \\right]\\right\\}\\right|\\\\&\n\\leq&\\frac{\\Vert B_n(\\lambda) \\Vert \\Vert \\alpha_v\\Vert^2 C m}{n^2}\n \\Vert (Im(\\lambda))^{-1}\\Vert^5\\\\&=& O(1\/n^2).\\end{eqnarray*}\nwhere we used Cauchy Schwarz's inequality, \\eqref{norme}, \\eqref{Y} and Lemma \\ref{var} in the last line.\\\\\nWe also have\n$$\\left|\\sum_{i,p,l=1}^n \\frac{ (\\kappa_3^{i,l,v} +\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})}{n^2\\sqrt{n}} \\tr_m B_n(\\lambda)(Y_n(\\lambda))_{pl} \\mathbb{E} \\{\\alpha_v(R_n(\\lambda))_{il}\\alpha_v(R_n(\\lambda))_{il}\\alpha_v \n (R_n(\\lambda))_{ip}\\}\\right|$$\n\\begin{eqnarray*}&=&\\frac{1}{n^2\\sqrt{n}} \\left| \\sum_{i,l=1}^n (\\kappa_3^{i,l,v} +\\tilde \\kappa_3^{i,l,v}\\sqrt{-1}) \\mathbb{E}\\tr_m \\{\\alpha_v(R_n(\\lambda))_{il}\\alpha_v(R_n(\\lambda))_{il}\\alpha_v \n (R_n(\\lambda){\\cal B}(\\lambda) Y_n(\\lambda))_{il}\\}\\right|\n\\\\&=&O(\\frac{1}{n\\sqrt{n}})\n\\end{eqnarray*}\nwhere we used \\eqref{Odenpas}, \\eqref{norme} and \\eqref{Y}.\n\n\n\\noindent Now, let us investigate the terms corresponding to the the $M^{(4)}(v,i,l,j)$'s in \\eqref{psi}. We have \n$$ \\frac{1}{4n^3} \\sum_{i,p,l=1}^n (\\kappa_4^{i,l,v} +\\tilde \\kappa_4^{i,l,v})\\mathbb{E} \\tr_m B_n(\\lambda)(Y_n(\\lambda))_{pl} \\alpha_v (R_n(\\lambda))_{il} \\alpha_v (R_n(\\lambda))_{il} \\alpha_v (R_n(\\lambda))_{il}\\alpha_v (R_n(\\lambda))_{ip}$$\n\\begin{eqnarray*}\n&=& \\frac{1}{4n^3} \\sum_{i,l=1}^n (\\kappa_4^{i,l,v} +\\tilde \\kappa_4^{i,l,v}) \\mathbb{E}\\tr_m \\alpha_v(R_n(\\lambda))_{il}\\alpha_v(R_n(\\lambda))_{il}\\alpha_v(R_n(\\lambda))_{il} \\alpha_v \\left(R_n(\\lambda){\\cal B}(\\lambda)Y_n(\\lambda)\\right)_{il}\n\\\\&=&O(1\/n^2) \\end{eqnarray*}\nwhere we used \\eqref{Odenpas}, \\eqref{norme} and \\eqref{Y}.\nSimilarly the terms corresponding to \\eqref{deuxcas4}, \\eqref{quatrecas4}, \\eqref{tilde1}, \\eqref{tilde2}, \\eqref{tilde3} and \\eqref{tilde4}\nare $O(1\/n^2).$\\\\\n\n\n\n\\noindent Since moreover each term in \\eqref{psi} corresponding to the $M^{(3)}(v,i,i,j)$, $M^{(4)}(v,i,i,j)$ and $M^{(5)}(v,i,l,j)$ leads obviously to a term which is a $O(\\frac{1}{n\\sqrt{n}})$, it readily follows from \\eqref{doubleetoilehat} that $$ g_n(z)- \\tilde g_n(z)+ \\sum_{v=1}^r tr_m\\tilde G_n'(zI_m) \\cdot \\alpha_v \\hat L_n(zI_m) \\alpha_v -tr_m \\hat L_n(zI_m)=O(\\frac{1}{n\\sqrt{n}}),$$\nwhere \\\\\n\n\\noindent $\\hat L_n (zI_m)=\\sum_{v=1}^r \\sum_{i,p,l=1}^n $ $$\\bigg\\{ \\frac{(\\kappa_4^{i,l,v} +\\tilde \\kappa_4^{i,l,v})}{4n^3} ( Y_n(zI_m)) _{pl} \\alpha_v \\mathbb{E} \\left\\{(R_n(zI_m))_{ii} \\alpha_v (R_n(zI_m))_{ll} \\alpha_v(R_n(zI_m))_{ii}\\alpha_v (R_n(zI_m))_{lp}\\right\\}$$\n$$+\\frac{(\\kappa_3^{i,l,v}+\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})}{2\\sqrt{2} n^2 \\sqrt{n} } ( Y_n(zI_m)) _{pl} \\alpha_v \\mathbb{E} \\left\\{(R_n(zI_m))_{ii} \\alpha_v (R_n(zI_m))_{li} \\alpha_v (R_n(zI_m))_{lp}\\right\\}$$\n$$+ \\frac{(\\kappa_3^{i,l,v}-\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})}{2\\sqrt{2} n^2 \\sqrt{n} } ( Y_n(zI_m)) _{pl} \\alpha_v \\mathbb{E} \\left\\{ (R_n(zI_m))_{ii} \\alpha_v (R_n(zI_m))_{ll} \\alpha_v (R_n(zI_m))_{ip}\\right\\}$$\n$$+ \\frac{(\\kappa_3^{i,l,v}-\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})}{2\\sqrt{2} n^2 \\sqrt{n} } ( Y_n(zI_m)) _{pl} \\alpha_v \\mathbb{E} \\left\\{(R_n(zI_m))_{il} \\alpha_v (R_n(zI_m))_{ii} \\alpha_v (R_n(zI_m))_{lp}\\right\\}\\bigg\\}$$\nFor any $m\\times m$ deterministic matrix $B_n(z)$, \\\\\n~~\n\n\\noindent $\\tr_m B_n (z)\\hat L_n (zI_m)=\\sum_{v=1}^r\\sum_{i,l=1}^n$ $$\\bigg\\{\\frac{(\\kappa_4^{i,l,v} +\\tilde \\kappa_4^{i,l,v}) }{4n^3} \\mathbb{E} \\left\\{\\tr_m \\alpha_v (R_n(zI_m))_{ii} \\alpha_v (R_n(zI_m))_{ll}\\alpha_v (R_n(zI_m))_{ii}\\alpha_v (R_n(zI_m){\\cal B}(z)Y_n(zI_m))_{ll}\\right\\}$$\n$$+ \\frac{(\\kappa_3^{i,l,v}+\\tilde \\kappa_3^{i,l,v}\\sqrt{-1}) }{2\\sqrt{2} n^2 \\sqrt{n} } \\mathbb{E} \\tr_m \\left\\{ \\alpha_v (R_n(zI_m))_{ii} \\alpha_v (R_n(zI_m))_{li} \\alpha_v \\left (R_n(zI_m){\\cal B}(z)Y_n(zI_m)\\right)_{ll}\\right\\}$$\n$$+ \\frac{ (\\kappa_3^{i,l,v}-\\tilde \\kappa_3^{i,l,v}\\sqrt{-1}) }{2\\sqrt{2} n^2 \\sqrt{n} } \\mathbb{E} \\tr_m\\left\\{ \\alpha_v (R_n(zI_m))_{ii} \\alpha_v (R_n(zI_m))_{ll} \\alpha_v \\left (R_n(zI_m){\\cal B}(z)Y_n(zI_m)\\right)_{il}\\right\\}$$\n$$+\\frac{ (\\kappa_3^{i,l,v}-\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})}{2\\sqrt{2} n^2 \\sqrt{n} } \\mathbb{E} \\tr_m\\left\\{ \\alpha_v (R_n(zI_m))_{il} \\alpha_v (R_n(zI_m))_{ii} \\alpha_v\\left (R_n(zI_m){\\cal B}(z)Y_n(zI_m)\\right)_{ll}\\right\\}\\bigg\\}$$\n\n\\noindent Hence (using a decomposition similar to \\eqref{decomposition}) Lemma \\ref{var} readily yields that \\\\\n\n\n\\hspace*{-0.8cm} $\\tr_m B_n \\hat L_n (zI_m) + O(\\frac{1}{n\\sqrt{n}})= \\sum_{v=1}^r \\sum_{i,l=1}^n \\tr_m$ $$\\hspace*{-0.8cm}\\bigg\\{\\frac{(\\kappa_4^{i,l,v} +\\tilde \\kappa_4^{i,l,v}) }{4n^3} \\alpha_v \\mathbb{E}\\left[ (R_n(zI_m))_{ii} \\right] \\alpha_v \\mathbb{E}\\left[(R_n(zI_m))_{ll}\\right] \\alpha_v\\mathbb{E}\\left[ (R_n(zI_m))_{ii} \\right]\\alpha_v [ \\mathbb{E}\\left[\\left (R_n(zI_m){\\cal B}(z)Y_n(zI_m)\\right)_{ll}\\right]$$\n$$\\hspace*{-0.8cm}+ \\frac{(\\kappa_3^{i,l,v}+\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})}{2\\sqrt{2} n^2 \\sqrt{n} } \\alpha_v \\mathbb{E}\\left[(R_n(zI_m))_{ii}\\right] \\alpha_v \\mathbb{E}\\left[(R_n(zI_m))_{li}\\right] \\alpha_v \\mathbb{E}\\left[\\left(R_n(zI_m){\\cal B}(z)Y_n(zI_m)\\right)_{ll}\\right] $$\n$$\\hspace*{-0.8cm}+ \\frac{(\\kappa_3^{i,l,v}-\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})}{2\\sqrt{2} n^2 \\sqrt{n} } \\alpha_v \\mathbb{E}\\left[(R_n(zI_m))_{ii}\\right] \\alpha_v \\mathbb{E}\\left[(R_n(zI_m))_{ll}\\right] \\alpha_v \\mathbb{E}\\left[ \\left(R_n(zI_m){\\cal B}(z)Y_n(zI_m)\\right)_{il}\\right]$$\n$$\\hspace*{-0.8cm}+ \\frac{(\\kappa_3^{i,l,v}-\\tilde \\kappa_3^{i,l,v}\\sqrt{-1})}{2\\sqrt{2} n^2 \\sqrt{n} } \\alpha_v \\mathbb{E}\\left[(R_n(zI_m))_{il} \\right] \\alpha_v \\mathbb{E}\\left[(R_n(zI_m))_{ii}\\right] \\alpha_v \\mathbb{E}\\left[\\left (R_n(zI_m){\\cal B}(z)Y_n(zI_m)\\right)_{ll}\\right]\\bigg\\}.$$\nNote that, with $Y_n$ and $\\tilde Y_n$ defined in \\eqref{y} and \\eqref{ytilde}, we have $$Y_n(zI_m)-\\tilde Y_n(zI_m) =\\sum_{v=1}^r Y_n(zI_m) \\left[\\alpha_v (G_n(zI_m)-\\tilde G_n(zI_m)) \\alpha_v \\otimes I_n\\right] \\tilde Y_n(zI_m)$$ so that, using (\\ref{difgngntilde}), \\eqref{Y} and \\eqref{Y2}, we can deduce that \\begin{equation}\\label{YmoinsYtilde} \\Vert Y_n(zI_m) -\\tilde Y_n(zI_m)\\Vert =O(1\/\\sqrt{n}). \\end{equation}\nNow, (\\ref{estimenracinen}) and (\\ref{YmoinsYtilde}) obviously yield that, up to a $O(\\frac{1}{n\\sqrt{n}})$ correction term, one can replace any $R_n(zI_m)$ and $Y_n(zI_m)$ by $\\tilde Y_n(zI_m)$ in any term in the sum corresponding to the fourth cumulants. Now, using (\\ref{lp}), (\\ref{estimenracinen}) and (\\ref{YmoinsYtilde}) also yield that, up to a $O(\\frac{1}{n\\sqrt{n}})$ correction term, for any $p=1,\\ldots,n$, one can replace $(R_n(zI_m))$ and $(Y_n(zI_m))$ by $\\tilde Y_n(zI_m)$ in any diagonal term $(R_n(zI_m))_{pp}$ or $(R_n(zI_m){\\cal B}(z)Y_n(zI_m))_{pp} $ in the sums corresponding to the third cumulants.\\\\\nFinally, \nassume that for $i=1,2,$ $Q^{(i)}= \\tilde Y_n(zI_m) $ or $\\tilde Y_n(zI_m){\\cal B}(z)\\tilde Y_n(zI_m)$. Let us consider any $ Q^{(3)}= \\sum_{i,l=1}^nQ^{(3)}_{il}\\otimes E_{il}$.\nIt is clear that if there exists some polynomial $Q$ with nonnegative coefficients such that for any $i,l \\in \\{1,\\ldots,n\\}^2$, $\\Vert Q^{(3)}_{il}\\Vert \\leq \\frac{Q(\\vert \\Im z \\vert^{-1})}{n}$, then \n$$\\frac{1}{n^2 \\sqrt{n}} \\sum_{i,l=1}^n\\Vert Q^{(1)}_{ii}\\Vert \\Vert Q^{(2)}_{ll}\\Vert \\Vert Q^{(3)}_{il}\\Vert =O(1\/n\\sqrt{n}).$$\nNow, if $\\Vert Q^{(3)}\\Vert =O(1\/\\sqrt{n})$,\nwe have $$\\frac{1}{n^2 \\sqrt{n}} \\sum_{i,l=1}^n\\Vert Q^{(1)}_{ii}\\Vert \\Vert Q^{(2)}_{ll}\\Vert \\Vert Q^{(3)}_{il}\\Vert $$ $$\\leq \\frac{1}{n \\sqrt{n}} \\vert (\\Im z)^{-1}\\vert^q\\left(\\sum_{i,l=1}^n\\Vert Q^{(3)}_{il}\\Vert^2 \\right)^{1\/2} \\leq \\frac{\\sqrt{m}}{n } \\vert (\\Im z)^{-1}\\vert^q\\Vert Q^{(3)}\\Vert =O(1\/n\\sqrt{n}) $$ for some $q\\in \\mathbb{N}\\setminus{\\{0\\}},$ where we used \\eqref{lp}.\n\\\\\nIt is then clear that using Corollary \\ref{estimenunsurn}, \\eqref{Q} and (\\ref{YmoinsYtilde}), \nup to a $0(\\frac{1}{n\\sqrt{n}})$ correction term, for any $(i,l)\\in \\{1,\\ldots,n\\}^2$, one can replace $R_n(zI_m)$ and $Y_n(zI_m)$ by $\\tilde Y_n(zI_m)$ in any non-diagonal term $(R_n(zI_m))_{il}$, $(R_n(zI_m))_{li}$ or $(R_n(zI_m){\\cal B}Y_n)_{il}$ in the sums corresponding to the third cumulants.\nHence \\eqref{estimdiffeqn} is proved for any $z\\in \\mathbb{C}$ such that $\\Im z >0$. \\\\Set $\\alpha=(\\alpha_1,\\ldots \\alpha_r)$, $\\beta=(\\beta_1,\\ldots, \\beta_t)$. Let us denote for a while $g_n=g_n^{\\alpha,\\beta,\\gamma}$, $\\tilde g_n=\\tilde g_n^{\\alpha,\\beta,\\gamma}$ and $\\tilde E_n=\\tilde E_n^{\\alpha,\\beta,\\gamma}$. Note that we have similarly for any $z\\in \\mathbb{C}$ such that $\\Im z >0$, \n\\begin{equation} \\label{estimdiffeqnmoins}\ng_n^{-\\alpha,-\\beta,-\\gamma}(z)-\\tilde g^{-\\alpha,-\\beta,-\\gamma}_n(z)+{\\tilde{E}^{-\\alpha,-\\beta,-\\gamma}_n(z)} = O(\\frac{1}{n\\sqrt{n}}).\n\\end{equation} Thus, since $g_n^{-\\alpha,-\\beta,-\\gamma}(z)=-g_n^{\\alpha,\\beta,\\gamma}(-z)$, $\\tilde g_n^{-\\alpha,-\\beta,-\\gamma}(z)=-\\tilde g_n^{\\alpha,\\beta,\\gamma}(-z)$ and $\\tilde E_n^{-\\alpha,-\\beta,-\\gamma}(z)=-\\tilde E_n^{\\alpha,\\beta,\\gamma}(-z)$, it readily follows that \n\\eqref{estimdiffeqn} is also valid for any $z \\in \\mathbb{C}$ such that $\\Im z <0$.\n\\end{proof}\n\n\n\n\\subsection{ From Stieltjes transform estimates to spectra.}\nWe start with the following key lemma.\n\\begin{lemme}\\label{LSt}\nFor any fixed large n, $ \\tilde E_n$ defined in Proposition \\ref{estimdiff} is the Stieltjes transform of a compactly supported distribution $\\nabla_n$ on $\\mathbb{R}$ whose support is\nincluded in the spectrum of $s_n=\\gamma \\otimes 1_{\\cal A} + \\sum_{v=1}^r\\alpha_v \\otimes x_v +\\sum_{u=1}^t\\beta_u \\otimes a_n^{(u)}$ and such that $\\nabla_n(1)=0$ .\n\\end{lemme}\n\n\nThe proof relies on the following characterization already used in \\cite{Schultz05}. \n\n\\begin{theoreme}\\label{TS}\\cite{Tillmann53}\n\\begin{itemize}\n\\item Let $\\Lambda $ be a distribution on $\\R$ with compact support. \nDefine the Stieltjes transform of $\\Lambda $, \n$ l:\\C\\setminus \\R \\rightarrow \\C$ by \n$$l(z)=\\Lambda \\left( \\frac{1}{z-x}\\right) .$$\n\\noindent Then $l$ is analytic on $\\C\\setminus \\R$\nand has an analytic continuation to $\\C\\setminus {\\rm supp}(\\Lambda )$. \nMoreover\n\\begin{itemize}\n\\item[($c_1$)] $l(z)\\rightarrow 0$ as $|z|\\rightarrow \\infty ,$\n\\item[($c_2$)] there exists a constant $C > 0$, \nan integer $q\\in \\N$ and a compact set $K\\subset \\R$ containing ${\\rm supp}(\\Lambda )$, \nsuch that for any $z\\in \\C\\setminus \\R$, \n$$|l(z)|\\leq C\\max \\{ {\\rm dist}(z,K)^{-q}, 1\\} ,$$\n\\item[($c_3$)] for any $\\phi \\in \\cal C^\\infty (\\R, \\R)$ with compact support\n$$\\Lambda (\\phi )=\\frac{i}{2\\pi }\\lim _{y\\rightarrow 0^+} \\int _\\R\\phi (x)[l(x+iy)-l(x-iy)]dx.$$\n\\end{itemize}\n\\item Conversely, if $K$ is a compact subset of $\\R$ \nand if $l:\\C \\setminus K\\rightarrow \\C$ is an analytic function\nsatisfying ($c_1$) and ($c_2$) above, \nthen $l$ is the Stieltjes transform of a compactly supported distribution $\\Lambda $ on $\\R$. \nMoreover, ${\\rm supp}(\\Lambda )$ is exactly the set of singular points of $l$ in $K$. \n\\end{itemize}\n\\end{theoreme}\n\\begin{lemme} The singular points of $\\tilde{E}_N$ defined in \\eqref{defentilde} are included in the \nspectrum of $s_n=\\gamma \\otimes 1_{\\cal A}+\\sum_{v=1}^r \\alpha_v \\otimes x_v +\\sum_{u=1}^t\\beta_u \\otimes a_n^{(u)}$. \n\\end{lemme}\n\\begin{proof}\nLet us start by noting that, as it follows from the definition \\eqref{defentilde} of $\\tilde{E}_N$,\nit is enough to show that $\\omega_n(zI_m)\\otimes I_n-\\sum_{u=1}^t\\beta_u \\otimes A_n^{(u)}\n\\in GL_{nm}(\\mathbb C)$ (the group of invertible $nm\\times nm$ complex matrices) for any $z$\nin the domain of definition of $\\mathbb C\\ni z\\mapsto\\omega_n(zI_m)\\in M_{m}(\\mathbb C).$\n\nAssume towards contradiction that $x_0\\in\\mathbb C$ is in the domain of $\\omega_n(\\cdot I_m)$, and \nyet $\\omega_n(x_0I_m)\\otimes I_n-\\sum_{u=1}^t\\beta_u \\otimes A_n^{(u)}$ is not invertible. First,\nobserve that a point $x_0$ with this property must be isolated and real. Indeed, otherwise the zeros of\nthe analytic map $\\mathbb C\\ni z\\mapsto\\det\\left(\\omega_n(zI_m)\\otimes I_n-\n\\sum_{u=1}^t\\beta_u \\otimes A_n^{(u)}\\right)\\in\\mathbb C$ would have $x_0$ as a cluster point\nin the interior of its domain (which coincides with the domain of $\\omega_n(\\cdot I_m)$), and thus\nit would be identically equal to zero. However, \n$\\omega_n(zI_m)\\otimes I_n-\\sum_{u=1}^t\\beta_u \\otimes A_n^{(u)}$ is invertible when $\\Im z\\neq0$,\nproviding us with a contradiction. Consider now such an isolated $x_0$. Recall \n\\begin{eqnarray*}\n\\tilde{g}_n(z) & = & (\\tr_m\\otimes\\tau)\\left(\\left(\n\\omega_n(zI_m)\\otimes1_\\mathcal A-\\sum_{u=1}^t\\beta_u \\otimes a_n^{(u)}\\right)^{-1}\\right)\\\\\n& = & (\\tr_m\\otimes\\tr_n)\\left(\\left(\n\\omega_n(zI_m)\\otimes I_n-\\sum_{u=1}^t\\beta_u \\otimes A_n^{(u)}\\right)^{-1}\\right),\n\\end{eqnarray*}\nfrom before (the second equality is justified by the hypothesis that the distribution of $(A_n^{(1)},\n\\dots,A_n^{(t)})$ with respect to $\\tr_n$ coincides with the distribution of $(a_n^{(1)},\\dots,a_n^{(t)})$\nwith respect to $\\tau$). We have seen that $\\tilde{g}_n$ is defined exactly on the complement of the \nspectrum of $s_n$. Thus, it is enough to show that, given an analytic function \n$f\\colon\\mathbb C^+\\to H^{-}(M_p(\\mathbb C))=\\{b\\in M_p(\\mathbb{C}), \\Im b <0\\}$, and $x_0\\in\\mathbb R$ with the property that there\nexists some $\\epsilon>0$ such that $f$ extends analytically through $(x_0-\\epsilon,x_0)\\cup(x_0,\nx_0+\\epsilon)$ with self-adjoint values, then either both or none of $f$ and $\\tr_p\\circ f$ extend \nanalytically to $x_0$. We shall then apply this to $p=mn$ and $f(z)=\\left(\\omega_n(zI_m)\\otimes I_n-\n\\sum_{u=1}^t\\beta_u \\otimes A_n^{(u)}\\right)^{-1}$ to conclude.\n \nIt is clear that if $f$ extends analytically through $x_0$, then so does $\\tr_p\\circ f$. Assume \ntowards contradiction that $\\tr_p\\circ f$ extends analytically through $x_0$, but $f$ does not.\nConsider an arbitrary system $\\{e_1,\\dots,e_p\\}$ of minimal mutually orthogonal projections\nin $M_p(\\mathbb C)$. It is clear that $z\\mapsto e_jf(z)e_j\\in e_jM_p(\\mathbb C)e_j\\simeq\\mathbb C$ is \nanalytic wherever $f$ is. Moreover, $\\Im e_jf(z)e_j\\leq0$ whenever $z\\in\\mathbb C^+$, and \n$e_jf(z)e_j\\in\\mathbb R$ whenever $f(z)$ is self-adjoint. If $z\\mapsto e_jf(z)e_j$ extends analytically\nthrough $x_0$ for any $e_j\\in\\{e_1,\\dots,e_p\\}$ and all systems $\\{e_1,\\dots,e_p\\}$, then \n$z\\mapsto\\varphi(f(z))$ extends analytically through $x_0$ for all linear functionals $\\varphi\\colon\nM_p(\\mathbb C)\\to\\mathbb C$. This implies that $f$ itself is analytic around $x_0$. On the other hand,\nif there exists a system $\\{e_1,\\dots,e_p\\}$ of minimal mutually orthogonal projections\nin $M_p(\\mathbb C)$ which contains an $e_j$ such that $z\\mapsto e_jf(z)e_j$ does not \nextend analytically through $x_0$, then, as $z\\mapsto e_jf(z)e_j$ does extend analytically \nwith real values through $(x_0-\\epsilon,x_0)\\cup(x_0,x_0+\\epsilon)$ and maps $\\mathbb C^+$\ninto $\\mathbb C^-\\cup\\mathbb R$, it follows that $x_0$ is a simple pole of \n$z\\mapsto e_jf(z)e_j$ and $\\lim_{y\\to0}iye_jf(x_0+iy)e_j\\in(0,+\\infty)$ by the Julia-Carath\\'eodory\nTheorem applied to $1\/e_jf(z)e_j$ (see (2) Theorem 2.1 in \\cite{Serban}). But then\n$\\tr_p(f(z))=\\sum_{k=1}^pe_kf(z)e_k$, so that \n$$\n\\lim_{y\\to0}iy\\tr_p(f(x_0+iy))=\\sum_{k=1}^p\\lim_{y\\to0}iye_kf(x_0+iy)e_k>0,\n$$\nwhich contradicts the assumption that $\\tr_p\\circ f$ extends analytically through $x_0$.\n\n\\end{proof}\n\n\nNow, we are going to show that for any fixed large $n$, \n$\\tilde{E}_n$ satisfies ($c_1$) and ($c_2$) of Theorem \\ref{TS}. \n\n\n\n\\noindent First note that there exists a polynomial Q in two variables with positive coefficients such that \n\\begin{equation}\\label{normedetildeEn} \\vert \\tilde {E}_n(z)\\vert \\leq \\Vert \\tilde Y(zI_m))\\Vert^4 Q(\\Vert \\tilde Y(zI_m))\\Vert, \\Vert r_n(zI_m)\\Vert ).\n\\end{equation}\n\n\\noindent Let $C > 0$ be such that, for all $n$, $\\mbox{sp}(\\sum_{u=1}^t\\beta_u \\otimes A_n^{(u)})\\subset [-C;C]$ and\n$\\mbox{sp}(\\gamma \\otimes 1_{\\cal A} +\\sum_{v=1}^r \\alpha_v \\otimes x_v +\\sum_{u=1}^t\\beta_u \\otimes a_n^{(u)})\\subset [-C;C]$.\n\n\\noindent Let $d > C + \\sqrt{r}\\max_{v=1}^r\\Vert \\alpha_v \\Vert $. \nFor any $z\\in \\C$ such that $|z| > \\Vert \\gamma \\Vert +d $, \n\\begin{eqnarray*}\\Vert \\gamma +\\sum_{v=1}^r \\alpha_v \\tilde G_n(zI_m) \\alpha_v \\Vert &\\leq& \\Vert \\gamma \\Vert + \\frac{r \\max_{v=1}^r\\Vert \\alpha_v \\Vert ^2}{\\vert z\\vert -C}\\\\&\\leq &\\Vert \\gamma \\Vert + \\frac{r \\max_{v=1}^r\\Vert \\alpha_v \\Vert ^2}{d-C} \\\\&\n<& \\Vert \\gamma \\Vert + \\frac{(d -C)^2}{d -C} \\\\&=& \\Vert \\gamma \\Vert + d-C\\end{eqnarray*}\n Thus, $$\\Vert \\gamma+\\sum_{v=1}^r\\alpha_v\\tilde G_n(zI_m) \\alpha_v +\\sum_{u=1}^t \\beta_u \\otimes A_n^{(u)}\\Vert \\leq\\Vert \\gamma \\Vert +d $$\nso that we get that for any $z\\in \\C$ such that $|z| > \\Vert \\gamma\\Vert +d $, \n\\begin{eqnarray*}\\Vert \\tilde Y_n(zI_m)\\Vert &=&\n\\Vert ((zI_m- \\gamma -\\sum_{v=1}^r \\alpha_v \\tilde G_n(zI_m) \\alpha_v)\\otimes I_n-\\sum_{u=1}^t\\beta_u \\otimes A_n^{(u)})^{-1}\\Vert\\\\\n& \\leq &\\frac{1}{\\vert z\\vert -\\Vert \\gamma \\Vert -d }.\n\\end{eqnarray*}\nWe get readily from \\eqref{normedetildeEn} that, for $|z| > \\Vert \\gamma\\Vert +d $, \n\\begin{equation}\\label{bound} \\vert \\tilde{E}_n(z)\\vert \\leq \\frac{1} {(\\vert z\\vert -\\Vert \\gamma \\Vert -d )^4}Q\\left(\\frac{1} {(\\vert z\\vert -\\Vert \\gamma \\Vert -d )},\\frac{1}{(|z|-C)} \\right) .\\end{equation}\nThen, it is clear than $\\vert\\tilde{E}_n(z)\\vert \\rightarrow 0$ \nwhen $|z|\\rightarrow +\\infty $ and ($c_1$) is satisfied.\\\\\n\\noindent \nNow we are going to prove ($c_2$) using the approach of \\cite{Schultz05}(Lemma 5.5). \nDenote by $\\mathcal {E}_n$ the convex envelope of the spectrum of $s_n=\\gamma \\otimes 1_{\\cal A} +\\sum_{v=1}^r\\alpha_v \\otimes x_v +\\sum_{u=1}^t\\beta_u \\otimes a_n^{(u)}$\nand define $$K_n:=\\left\\{ x\\in \\R; {\\rm dist}(x, \\mathcal {E}_n)\\leq 1\\right\\} $$\n\\noindent \nand $$D_n=\\left\\{ z\\in \\C; 0 < {\\rm dist}(z, K_n)\\leq 1\\right\\} .$$\n\\begin{itemize}\n\\item Let $z\\in D_n\\cap (\\C\\setminus \\R)$ with $\\Re (z)\\in K_n$. \nWe have ${\\rm dist}(z, K_n)=|\\Im z|\\leq 1$. \nWe have from \\eqref{normedetildeEn}, (\\ref{normeG}) and \\eqref{Y2} that \n$$\\vert \\tilde{E}_n(z)\\vert \\leq {\\vert \\Im z \\vert^{-4}} Q\\left(\\vert \\Im z \\vert^{-1},\\vert \\Im z \\vert^{-1}\\right).$$\nNoticing that $1\\leq {\\vert \\Im z\\vert^{-1}}$, \nwe easily deduce that there exists some constant $C_0$ and some number $q_0 \\in \\mathbb{N}\\setminus \\{0\\}$ such that \nfor any $z\\in D_n\\cap \\C\\setminus \\R$ with $\\Re (z)\\in K_n$, \n\\begin{eqnarray*}\n\\vert \\tilde{E}_n(z)\\vert &\\leq &C_0|\\Im z|^{-q_0}\\\\\n&\\leq &C_0{\\rm dist}(z, K_n)^{-q_{0}}\\\\\n&\\leq &C_0\\max ({\\rm dist}(z, K_n)^{-q_{0}}; 1)\n\\end{eqnarray*}\n\\item Let $z\\in D_n\\cap (\\C\\setminus \\R)$ with $\\Re (z)\\notin K_n$. \nThen ${\\rm dist}(z,{\\rm sp}(s_n))\\geq 1$. \nSince $\\tilde{E}_n$ is bounded on compact subsets of $\\C\\setminus {\\rm sp}(\\gamma \\otimes 1_{\\cal A} +\\sum_{v=1}^r\\alpha_v \\otimes x_v +\\sum_{u=1}^t\\beta_u \\otimes a_n^{(u)})$,\nwe easily deduce that there exists some constant $C_1(n)$ \nsuch that for any $z\\in D_n$ with $\\Re (z)\\notin K_n$, \n$$\\vert \\tilde{E}_n(z)\\vert \\leq C_1(n)\\leq C_1(n)\\max ({\\rm dist}(z, K_n)^{-q_0}; 1).$$\n\\item Since $\\vert \\tilde{E}_n(z)\\vert \\rightarrow 0$ when $|z|\\rightarrow +\\infty $, \n$\\tilde{E}_n$ is bounded on $\\C\\setminus \\overline{D_n}$. \nThus, there exists some constant $C_2(n)$ such that for any $z\\in \\C\\setminus \\overline{D_n}$, \n$$\\vert \\tilde{E}_n(z)\\vert \\leq C_2(n)=C_2(n)\\max ({\\rm dist}(z, K_n)^{-q_0}; 1).$$\n\\end{itemize}\nHence ($c_2$) is satisfied with $C(n)=\\max (C_0, C_1(n), C_2(n))$ and $l=q_0$. Thus, Theorem \\ref{TS} implies that for any fixed large n, $ \\tilde E_n$ defined in Proposition \\ref{estimdiff} is the Stieltjes transform of a compactly supported distribution $\\nabla_n$ on $\\mathbb{R}$ whose support is\nincluded in the spectrum of $s_n=\\gamma \\otimes 1_{\\cal A} +\\sum_{v=1}^r\\alpha_v \\otimes x_v +\\sum_{u=1}^t\\beta_u \\otimes a_n^{(u)}$. Following the proof of Lemma 5.6 in \\cite{Schultz05} and using (\\ref{bound}), \none can show that $\\nabla _n(1)=0$. \nThe proof of Lemma \\ref{LSt} is complete. $\\Box$\\\\\n\n\n\n\n\n\n\n\n\n\n\n\n\\noindent Now, \\eqref{spectre3} can be deduced from \\eqref{estimdiffeqno} by an approach inspired by \\cite{HT} and \\cite{Schultz05} as follows. \\\\\nUsing the inverse Stieltjes tranform, we get respectively that, \nfor any $\\varphi _n $ in ${\\cal C}^\\infty (\\R, \\R)$ with compact support, \n$$\\mathbb{E} [\\tr_m \\otimes \\tr _n(\\varphi _n(S_n))]-\\tr_m\\otimes \\tau(\\varphi_n(s_n))\n+{\\nabla _{n}(\\varphi _n)}$$\n$$=\\frac{1}{\\pi }\\lim _{y\\rightarrow 0^+}\\Im \\int _\\R\\varphi _n(x)\\epsilon_n(x+iy)dx,$$\nwhere $\\epsilon_n(z)=\\tilde g_n(z)-g_n(z)-\\tilde{E}_n(z)$ satisfies, \naccording to Proposition \\ref{estimdiff}, for any $z\\in \\C\\setminus \\R$, \n\\begin{equation*}\\label{estimgdif}\n\\vert \\epsilon_n(z)\\vert \\leq \\frac{1}{n\\sqrt{n}}P(\\vert \\Im z \\vert ^{-1}) .\n\\end{equation*} \nWe refer the reader to the Appendix of \\cite{CD07} \nwhere it is proved using the ideas of \\cite{HT} that if $h$ is an analytic function on $\\C\\setminus \\R$ which satisfies\n\\begin{equation*}\\label{nestimgdif}\n\\vert h(z)\\vert \\leq P(\\vert \\Im z\\vert ^{-1})\n\\end{equation*} \n\\noindent for some polynomial $P$ with nonnegative coefficients and degree $k$, then\nthere exists a polynomial $Q$ such that\n$$\\limsup _{y\\rightarrow 0^+}\\vert \\int _\\R\\varphi _n(x)h(x+iy)dx\\vert $$\n$$\\leq \\int _\\R\\int _0^{+\\infty }\\vert (1+D)^{k+1}\\varphi _n(x)\\vert Q(t)\\exp(-t)dtdx$$\nwhere $D$ stands for the derivative operator.\nHence, if there exists $K > 0$ such that, for all large $n$, \nthe support of $\\varphi _n$ is included in $[-K, K]$ and \n$\\sup _n\\sup _{x \\in [-K, K]}\\vert D^p\\varphi _n(x)\\vert =C_p < \\infty$ for any $p\\leq k+1$, \ndealing with $h(z) =n\\sqrt{n}\\epsilon_n(z)$, we deduce that there exists $C>0$ such that for all large $n$,\n\\begin{equation*} \\label{majlimsup1} \n\\limsup _{y\\rightarrow 0^+}\\vert \\int _\\R \\varphi _n(x)\\epsilon_n(x+iy)dx\\vert \\leq \\frac{C}{n\\sqrt{n}}\n\\end{equation*} \nand then \n\\begin{equation}\\label{StS} \n\\mathbb{E} [\\tr_m \\otimes \\tr _n(\\varphi _n(S_n))]-\\tr_m\\otimes \\tau(\\varphi_n(s_n))\n+{\\nabla _{n}(\\varphi _n)}=O(\\frac{1}{n\\sqrt{n}}). \n\\end{equation}\nLet $\\rho \\geq 0$ be in ${\\cal C}^\\infty (\\R, \\R)$ \nsuch that its support is included in $[-1;1]$ and $\\int \\rho (x)dx=1$. \nLet $0 < \\epsilon < 1$. \nDefine for any $x\\in \\mathbb{R}$, $$\\rho _{\\frac{\\epsilon }{2}}(x)=\\frac{2}{\\epsilon }\\rho(\\frac{2x}{\\epsilon }).$$\nSet $$K_n(\\epsilon )=\\{ x, {\\rm dist}(x, {\\rm sp}(s_n))\\leq \\epsilon \\}$$ \nand define for any $x\\in \\mathbb{R}$, $$f_n(\\epsilon )(x)=\\int _\\mathbb{R} \\1 _{K_n(\\epsilon )}(y)\\rho _{\\frac{\\epsilon }{2}}(x-y)dy.$$\nThe function $f_{n}(\\epsilon )$ is in ${\\cal C}^\\infty (\\mathbb{R}, \\mathbb{R})$, \n$f_{n}(\\epsilon )\\equiv 1$ on $K_n(\\frac{\\epsilon }{2})$; \nits support is included in $K_n(2\\epsilon )$. \nSince there exists $K$ such that, for all large $n$, the spectrum of $s_n$ \nis included in $[-K;K]$, for all large $n$ the support of $f_n(\\epsilon )$ is included in $[-K-2;K+2]$ \nand for any $p > 0$, \n$$\\sup _{x\\in [-K-2;K+2]}\\vert D^pf_n(\\epsilon )(x)\\vert \\leq \n\\sup _{x\\in [-K-2;K+2]}\\int _{-K-1}^{K+1} \\vert D^p \\rho _{\\frac{\\epsilon }{2}}(x-y)\\vert dy \\leq C_p(\\epsilon ).$$\nThus, according to \\eqref{StS}, \n\\begin{equation} \n\\mathbb{E} [\\tr_m\\otimes \\tr _n(f_n(\\epsilon )(S_n))]-\\tr_m \\otimes \\tau f_n(\\epsilon )(s_n)\n+{\\nabla _n(f_n(\\epsilon ))}=O_{\\epsilon }(\\frac{1}{n\\sqrt{n}})\n\\end{equation}\nand \n\\begin{equation}\\label{prime} \n\\mathbb{E} [\\tr_m\\otimes \\tr _n((f_n'(\\epsilon ))^2(S_n))]-\\tr_m \\otimes \\tau (f_n'(\\epsilon )(s_n))^2\n+{\\nabla _n((f_n'(\\epsilon ))^2)}=O_{\\epsilon }(\\frac{1}{n\\sqrt{n}}).\n\\end{equation}\nAccording to Lemma \\ref{LSt}, we have $\\nabla _n(1)=0$. \nThen, the function $\\psi _n(\\epsilon )\\equiv 1-f_n(\\epsilon )$ also satisfies\n\\begin{equation} \n\\mathbb{E} [\\tr_m\\otimes \\tr _n(\\psi _n(\\epsilon )(S_n))]-\\tr_m \\otimes \\tau \\left(\\psi _n(\\epsilon )(s_n)\\right)\n+\\nabla _n(\\psi _n(\\epsilon ))=O_{\\epsilon }(\\frac{1}{n\\sqrt{n}}). \n\\end{equation}\nMoreover, since $\\psi _n'(\\epsilon )=-f_n'(\\epsilon )$, \nit comes readily from \\eqref{prime} that \n$$\\mathbb{E} [\\tr _n((\\psi _n'(\\epsilon ))^2(S_n))]-\\tr_m \\otimes \\tau (\\psi _n'(\\epsilon )(s_n))^2\n+{\\nabla_n((\\psi _n'(\\epsilon ))^2)}=O_{\\epsilon }(\\frac{1}{n\\sqrt{n}}).$$\nNow, since $\\psi _n(\\epsilon )\\equiv 0$ on the spectrum of $s_n$, \nwe deduce that \n\\begin{equation}\\label{psi2} \n\\mathbb{E} [\\tr_m\\otimes \\tr _n(\\psi _n(\\epsilon )(S_n))]=O_{\\epsilon }\\left(\\frac{1}{n\\sqrt{n}}\\right)\n\\end{equation}\nand \n\\begin{equation} \\label{psiprime} \\mathbb{E} [\\tr_m\\otimes \\tr _n((\\psi _n'(\\epsilon ))^2(S_n))]=O_{\\epsilon }\\left(\\frac{1}{n\\sqrt{n}}\\right).\n\\end{equation}\nBy Lemma \\ref{variance} (sticking to the proof of Proposition 4.7 in \\cite{HT} with $\\varphi=f_n(\\epsilon)$), \nwe have\n$$\\mathbf{V}{\\left[ \\tr_m \\otimes \\tr _n(\\psi _n(\\epsilon )(S_n))\\right] }\n\\leq \\frac{C }{n^2}\\mathbb{E} \\left[\\tr_m \\otimes \\tr _n\\{ (\\psi _n'(\\epsilon )(S_n))^2\\} \\right] .$$\nHence, using \\eqref{psiprime}, one can deduce that \n\\begin{equation} \\label{variancepsi}\n\\mathbf{V}{\\left[ \\tr_m \\otimes \\tr _n(\\psi _n(\\epsilon )(S_n))\\right] }=O_{\\epsilon }\\left(\\frac{1}{n^3\\sqrt{n}}\\right).\n\\end{equation}\nFix $0<\\delta<\\frac{1}{4}$.\nSet $$Z_{n, \\epsilon }:=\\tr_m \\otimes \\tr _n(\\psi _n(\\epsilon )(S_n))$$ \nand $$\\Omega _{n, \\epsilon }=\\{ \\left| Z_{n, \\epsilon }-\\mathbb{E}\\left(Z_{n, \\epsilon }\\right)\\right| > n^{-(1+\\delta)}\\}.$$\nHence, using \\eqref{variancepsi}, we have $$\\mathbb{P}(\\Omega _{n, \\epsilon })\\leq n^{2+2\\delta}\\mathbf{V}\\{ Z_{n, \\epsilon }\\}\n=O_{\\epsilon }\\left(\\frac{1}{n^{1+\\frac{1}{2}-2\\delta}}\\right).$$\nBy Borel-Cantelli lemma, we deduce that, almost surely for all large $n$, \\begin{equation}\\label{concentrezn} \\left|Z_{n, \\epsilon }-\n\\mathbb{E}\\left(Z_{n, \\epsilon }\\right)\\right| \\leq n^{-(1+\\delta)}.\\end{equation}\nFrom \\eqref{psi2} and \\eqref{concentrezn}, we deduce that there exists some constant $C_\\epsilon$ such that, almost surely for all large $n$,\n$$\\left| Z_{n, \\epsilon }\\right|\\leq n^{-1}\\left(n^{-\\delta}+C_\\epsilon n^{-1\/2}\\right).$$\nSince $\\psi_{n}( \\epsilon )\\geq \\1 _{\\mathbb{R}\\setminus K_n({2\\epsilon })}$, \nit readily follows that, almost surely for all large $n$, \nthe number of eigenvalues of $S_n$ which are in $\\R\\setminus K_n({2\\epsilon })$ \nis lower than $m\\left(n^{-\\delta}+C_\\epsilon n^{-1\/2}\\right)$ and thus obviously, almost surely for all large $n$, the number of eigenvalues of $S_n$ which are in $\\R\\setminus K_n({2\\epsilon })$ \n has \nto be equal to zero. Thus we have the following\n\\begin{theoreme} Let $\\epsilon>0$.\nAlmost surely for all large $n$, the spectrum of $S_n$ is included in $K_n(\\epsilon )=\\{ x, {\\rm dist}(x, {\\rm sp}(s_n))\\leq \\epsilon \\}$. \n\\end{theoreme}\nSince the above theorem holds for any $m\\times m$ Hermitian matrices $\\gamma$, $\\{\\alpha_v\\}_{v=1,\\ldots,r}$, $\\{\\beta_u\\}_{u=1,\\ldots,t}$, the proof of Lemma \\ref{inclu2} is complete.\n\n\\section{Proof of Theorem \\ref{noeigenvalue}}\\label{sectionnoeigenvalue}\n\\subsection{Linearization}\\label{linearisation}\n\nLinearization procedures are by no means unique, and no agreed upon definition of what a linearization\nis exists in the literature. We use the procedure introduced in \\cite[Proposition 3]{A}, which has several \nadvantages, to be described below.\n\n\n\n\nIt is shown in \\cite{A} that, given a polynomial $P\\in\\mathbb C\\langle X_1,\\dots,X_k\\rangle$,\nthere exist $m\\in\\mathbb N$ and matrices $\\zeta_1,\\dots,\\zeta_k,\\gamma\\in M_m(\\mathbb C)$\nsuch that $(z-P(X_1,\\dots,X_k))^{-1}=\\left[\\left(z\\hat{E}_{11}\\otimes1-\\gamma\\otimes 1-\\sum_{j=1}^k\n\\zeta_j\\otimes X_j\\right)^{-1}\\right]_{11}$. Moreover, if $P=P^*$, then $\\gamma$ and $\\zeta_1,\\dots,\n\\zeta_k$ can be chosen to be self-adjoint. We denote $L_P=\\gamma\\otimes1+\\sum_{j=1}^k\\zeta_j\n\\otimes X_j\\in M_m(\\mathbb C\\langle X_1,\\dots,X_k\\rangle)$ and call it a {\\em linearization} of $P$. \nThe size $m$ and the matrix coefficients $\\gamma,\\zeta_1,\\dots,\\zeta_k$ aren't unique. Following \\cite{BMS} (see also \\cite{Mai}), we provide \na very brief outline of a recursive construction for a linearization $L_P$ such that $$L_P := \\begin{pmatrix} 0 & u\\\\v & Q \\end{pmatrix} \\in M_m(\\mathbb{C}) \\otimes \\mathbb{C} \\langle X_1,\\ldots, X_k \\rangle$$\nwhere\n\\begin{enumerate}\n\\item $ m \\in \\mathbb{N}$,\n\\item $ Q \\in M_{m-1}(\\mathbb{C})\\otimes \\mathbb{C} \\langle X_1,\\ldots, X_k \\rangle$ is invertible,\n\\item \n u is a row vector and v is a column vector, both of size $m-1$ with\nentries in $\\mathbb{C} \\langle X_1,\\ldots, X_k \\rangle$,\n\\item the polynomial entries in $Q, u$ and $v$ all have degree $\\leq 1$,\\\\\n\\item\n$${P=-uQ^{-1}v} ,$$\n\\item and moreover, if $P$ is self-adjoint, $L_P$ is self-adjoint.\n\\end{enumerate}\nThus, to linearize a monomial $P=X_{i_1}X_{i_2}X_{i_3}\\cdots X_{i_{k-1}}X_{i_l}$, write\n$$\nL_P=-\\begin{bmatrix}\n0 & 0 & \\cdots & 0 & 0 & X_{i_1}\\\\\n0 & 0 & \\cdots & 0 & X_{i_2} & -1\\\\\n0 & 0 & \\cdots & X_{i_3} & -1 & 0\\\\\n\\vdots&\\vdots& \\cdots&\\vdots&\\vdots&\\vdots\\\\\n0 & X_{i_{l-1}}&\\cdots&0&0&0\\\\\nX_{i_l}&-1&\\cdots&0&0&0\n\\end{bmatrix},\n$$\nwith the obvious adaptations if $l=1,2$. The $(l-1)\\times(l-1)$ lower right corner of the \nabove matrix is invertible in the algebra $M_{l-1}(\\mathbb C\\langle X_1,\\dots,X_k\\rangle)$ and \nits inverse has as entries polynomials of degree up to $l-1$ (again with the obvious modifications when \n$l\\leq 2$). The constant term in the inverse's formula is simply the matrix having $-1$ on its second \ndiagonal, and its spectrum included in $\\{-1,1\\}$.\n If the matrices $\\begin{bmatrix}\n0 & u_j\\\\\nv_j & Q_j\\end{bmatrix},$ \nwith $u_j\\in M_{1\\times n_j}(\\mathbb C\\langle X_1,\\dots,X_k\\rangle),v_j\\in M_{n_j\\times1}\n(\\mathbb C\\langle X_1,\\dots,X_k\\rangle),Q_j\\in M_{n_j}(\\mathbb C\\langle X_1,\\dots,X_k\\rangle)$\nare linearizations for $P_j,$ $j=1,2$, then \n$$\nL_{P_1+P_2}=\\begin{bmatrix}\n0 & u_1 & u_2\\\\\nv_1 & Q_1 & 0\\\\\nv_2 & 0 &Q_2\n\\end{bmatrix}\\in M_{n_1+n_2+1}(\\mathbb C\\langle X_1,\\dots,X_k\\rangle).\n$$\nIn particular, we have again that $\\begin{bmatrix} Q_1 & 0\\\\ 0 & Q_2\\end{bmatrix}^{-1}\\in\nM_{n_1+n_2}(\\mathbb C\\langle X_1,\\dots,X_k\\rangle),$ with entries of degrees no more $\\max\\{n_1,n_2\\}$. \nAgain, its constant term has all its eigenvalues of absolute value equal to one.\nThe above construction does not necessarily provide a self-adjoint $L_P$, even if\n$P=P^*$. However, any self-adjoint polynomial $P$ is written as a sum $P=P_0+P_0^*$ for some \nother polynomial $P_0$ (self-adjoint or not) of the same degree. Let $L_{P_0}=\\begin{bmatrix}\n0 & u_0\\\\\nv_0 & Q_0\n\\end{bmatrix}.$ To insure that the linearization we obtain is self-adjoint, we write\n$$\nL_P=L_{P_0+P_0^*}=\\begin{bmatrix}\n0 & u_0 & v_0^*\\\\\nu_0^* & 0 & Q_0^* \\\\\nv_0 & Q_0 & 0\n\\end{bmatrix},\n$$\nwhich satisfies $L_P=L_P^*$ and linearizes $P$. Moreover, since $\\begin{bmatrix} 0 & Q_0^* \\\\\nQ_0 & 0\n\\end{bmatrix}^{-1}=\\begin{bmatrix} 0 & Q_0^{-1} \\\\\n(Q_0^*)^{-1} & 0\n\\end{bmatrix}$, the matrix $\\begin{bmatrix} 0 & Q_0^* \\\\\nQ_0 & 0\n\\end{bmatrix}^{-1}$ has entries which are polynomials in $X_1,\\dots,X_k$, all of them of degree \nmajorized by the degree of $P$, and its constant term is a complex matrix having \nspectrum included in the unit circle of the complex plane. These remarks, which the reader can find in\n\\cite{Mai}, will be most useful in our analysis below.\n\n\n\nIt follows from the above that if $X_1,\\dots,X_k$ are elements in some complex algebra $\\mathcal R$ \nwith unit $1$, then $z1-P(X_1,\\dots,X_k)$ is invertible in $\\mathcal R$ if and only if $z\\hat{E}_{11}-L_{P}\n(X_1,\\dots,X_k)$ is invertible in $M_m(\\mathcal R)$ ($m$ being the size of the matrix $L_P$). Moreover, \nthe construction above guarantees that the matrix $Q$ in the linearization $L_{P}(X_1,\\dots,X_k)\n=\\begin{bmatrix}\n0 & u\\\\\nv & Q\n\\end{bmatrix}$ is invertible independently of the elements $X_1,\\dots,X_k\\in\\mathcal R$.\nWe apply this to the case when $\\mathcal R\\subseteq\\mathcal B(\\mathcal H)$ is \na unital ${\\cal C}$${}^*$-algebra of bounded linear operators on the Hilbert space $\\mathcal H$, \nfor various separable Hilbert spaces $\\mathcal H$. We formalize this result in the following\n\n\n\\begin{lemme}\\label{inversible}\nLet $P=P^*\\in\\mathbb{C}\\langle X_1,\\ldots,X_k\\rangle$ and let\n$L_P \\in M_m(\\mathbb{C}\\langle X_1,\\ldots,X_k\\rangle)$\nbe a linearization of P with the properties outlined above. Let $y = (y_1,\\ldots, y_k)$ be a k-tuple of self-adjoint operators in a ${\\cal C}^*$-algebra ${\\cal A}$. Then, for any $z\\in \\mathbb{C}$, $z\\hat E_{11}\\otimes 1_{\\cal A}-L_P(y)$\nis invertible if and only if $z 1_{\\cal A}-P(y)$ is invertible.\n\\end{lemme}\n\nBeyond the property described above, we want also to compare the norms of the inverses of \n$z\\hat E_{11}\\otimes 1_{\\cal A}-L_P(y)$ and $z 1_{\\cal A}-P(y)$ when one (and hence the other) exists.\n\n\\begin{lemme}\\label{distanceauspectre} Let $P=P^*\\in\\mathbb{C}\\langle X_1,\\ldots,X_k\\rangle$ and let\n$L_P \\in M_m(\\mathbb{C}\\langle X_1,\\ldots,X_k\\rangle)$\nbe a linearization of P constructed as above. Let $y_n = (y_1^{(n)},\\ldots, y_k^{(n)})$ be a k-tuple of self-adjoint operators in a ${\\cal C}^*$-algebra ${\\cal A}$ such that $\\sup_n \\max_{i=1}^k \\Vert y_n^{(i)}\\Vert=C<+\\infty$. Let $z_0 \\in \\mathbb{C}$ be such that, for all large $n$, the distance from $z_0$\nto $sp(P(y_n))$ is greater than $\\delta$. Then, there exists a constant $\\epsilon > 0$, depending only on $ \\delta$, $L_P$ and $C$ such that the distance from $0$ to $sp(z_0\\hat E_{11}\\otimes 1_{\\cal A}-L_P(y_n))$\nis at least $\\epsilon$.\n\\end{lemme}\n\\begin{proof}\nIn this proof we only consider $P,u$ and $Q$ evaluated in $y_n$, so we will suppress $y_n$ from the \nnotation without any risk of confusion. Let $L_P=\\begin{bmatrix} 0 & u^*\\\\ u & Q\\end{bmatrix}$,\nso that $z_0\\hat E_{11}\\otimes 1_{\\cal A}-L_P=\\begin{bmatrix} z_0 &- u^*\\\\ -u & -Q\\end{bmatrix}$, as \nabove. We seek an $\\epsilon>0$ such that \n$z_0\\hat E_{11}\\otimes 1_{\\cal A}-L_P-z(I_m\\otimes 1_{\\cal A})=\\begin{bmatrix} z_0-z &- u^*\\\\ -u & -Q-\nz\\end{bmatrix}$ is invertible for all $|z|<\\epsilon$. We naturally require first that $-Q-z$ remains\ninvertible. As $Q=Q^*$, it follows by functional calculus that the spectrum of $-Q$ is at \ndistance equal to $\\|Q^{-1}\\|^{-1}$ from zero. As noted before, $Q^{-1}\\in M_{m-1}(\\mathbb C\n\\langle y_1^{(n)},\\dots,y_k^{(n)}\\rangle)$, with entries depending only on $P$, so that we can \nmajorize $\\|Q^{-1}\\|$ by a constant $\\kappa>0$ depending only on $C$ and $L_P$ (and independent of\nthe particular $y_n$). Thus, our first condition on $\\epsilon$ is $\\epsilon\\leq\\kappa^{-1}\/2$. \nNote that it follows that $\\|(Q+z)^{-1}\\|< 2 \\kappa.$\nNext,\nwe require that in addition $(z_0-z-u^*(-Q-z)^{-1}u)$ is invertible for all $|z|<\\epsilon$. By the openness\nof the resolvent set, we do know that such an $\\epsilon>0$ exists. More precisely, by a geometric series \nargument, if $a$ is invertible, then $b=((b-a)a^{-1}+1)a$ is invertible whenever $\\|b-a\\|<\\|a^{-1}\\|^{-1}\n$. We apply this to $a=z_0-P=z_0+u^*Q^{-1}u$ (so that $\\|a^{-1}\\|^{-1}>\\delta$) and \n$b=z_0-z-u^*(-Q-z)^{-1}u$.\nWe have \\\\\n\n\\noindent $\\|z_0+u^*Q^{-1}u-z_0+z+u^*(-Q-z)^{-1}u\\|$ \n\\begin{eqnarray*}\n& \\leq &|z|+ \\|u^*[(-Q-z)^{-1}+Q^{-1}]u\\|\\\\\n& \\leq &|z|+ |z|\\|u\\|^2\\|Q^{-1}\\|\\|(Q+z)^{-1}\\|\\\\\n&\\leq & |z|(1+ 2\\kappa^2 \\|u\\|^2).\n\\end{eqnarray*}\nSince the norm of $u$ is majorized in terms of $C$ and $L_P$ only by \nsome constant $\\ell>0$, we deduce that \n $\\|z_0+u^*Q^{-1}u-z_0+z+u^*(-Q-z)^{-1}u\\|\\leq |z|(1+ 2\\kappa^2 \\ell^2).$\nThus, we require $|z|(1+ 2\\kappa^2 \\ell^2)<\\delta$. \n This yields that , if $|z|<\\min\\{\\kappa^{-1}\/2,\\frac{\\delta}{(1+2\n\\kappa^2\\ell^2)}\\}$, then $z_0\\hat E_{11}\\otimes 1_{\\cal A}-L_P-z(I_m\\otimes 1_{\\cal A})$ is invertible. This\nconcludes the proof of our lemma.\n\n\\end{proof}\n\n\\subsection{From Lemma \\ref{inclu2} to Theorem \\ref{noeigenvalue}}\n\nLet $a_n=(a_n^{(1)},\\ldots,a_n^{(t)})$ be a t-tuple of noncommutative self-adjoint random variables which \nis free with the semicircular system $x=(x_1,\\ldots,x_r)$ in $({\\cal A},\\tau)$, such that the distribution of \n$a_n$ in $({\\cal A},\\tau)$ coincides with the distribution of $(A_n^{(1)},\\ldots, A_n^{(t)})$ in $({ M}_n(\\mathbb{C}),\\tr_n)$.\nLet $P$ be a Hermitian polynomial in t+r noncommutative indeterminates.\nLet\n$L_P\\in M_m\\left(\\mathbb{C}\\langle X_1,\\ldots,X_{t+r}\\rangle\\right)$ be a linearization of $P$ as constructed in Section \\ref{linearisation}. Fix $\\delta>0$ and let $z\\in \\mathbb{R}$ be such that for all large $n$, the distance from $z$ to the spectrum of $\\left(P\\left(x_1,\\ldots,x_r, a_n^{(1)},\n\\ldots,a_n^{(t)}\\right)\\right)$ is greater than $\\delta$. According to Lemma \\ref{distanceauspectre} (and \n\\eqref{normeAn}), there exists a constant $\\epsilon > 0$, depending only on $\\delta,L_P$ and $\\sup_{n}\n\\max_{1\\le u\\le t}\\|A_n^{(u)}\\|$ such that the distance from 0 to $sp(z\\hat E_{11}\\otimes 1_{\\cal A}-\nL_P(x_1,\\ldots,x_r, a_n^{(1)},\\ldots,a_n^{(t)}))$ is as least $\\epsilon$. Now, according to Lemma \n\\ref{inclu2}, almost surely, for all large $n$, the distance from 0 to the spectrum of $(z\\hat E_{11}\\otimes I_n-L_P(\\frac{X_n^{(1)}}{\\sqrt{n}}, \\ldots, \\frac{X_n^{(r)}}{\\sqrt{n}}, A_n^{(1)},\\ldots,A_n^{(t)}))$ is as least \n$\\epsilon\/2$. Hence, \nfor any $z'\\in ]z-\\epsilon\/4;z+\\epsilon\/4[$, 0 is not in the spectrum of $(z' \\hat E_{11}\\otimes I_n-\nL_P(\\frac{X_n^{(1)}}{\\sqrt{n}}, \\ldots, \\frac{X_n^{(r)}}{\\sqrt{n}}, A_n^{(1)},\\ldots,A_n^{(t)}))$.\n Finally, according to Lemma \\ref{inversible}, almost surely, for large $n$, $]z-\\epsilon\/4;z+\\epsilon\/4[$ lies outside the spectrum of $P(\\frac{X_n^{(1)}}{\\sqrt{n}}, \\ldots, \\frac{X_n^{(r)}}{\\sqrt{n}}, A_n^{(1)},\\ldots,A_n^{(t)})$.\nA compacity argument readily yields Theorem \\ref{noeigenvalue}.\n \n \n \n \n \n \n \n \n\\section{Proof of \\eqref{safbis}}\\label{strategie}\n\n Our approach is then very similar to that of \\cite{HT} and \\cite{Schultz05}. Therefore, we will recall the main steps.\\\\ First, the almost sure minoration\n$$ \\liminf_{n \\vers +\\infty} \\left\\| P\\left(\\frac{X_n^{(1)}}{\\sqrt{n}}, \\ldots, \\frac{X_n^{(r)}}{\\sqrt{n}},A_n^{(1)},\\ldots,A_n^{(t)}\\right) \\right\\| \\geq \\left\\| P(x_1, \\ldots x_r,a_1,\\ldots,a_t)\\right\\| \\ $$\ncomes rather easily from \\eqref{af}; this can be proved by closely following the proof of Lemma 7.2 in \\cite{HT}. So, the main\ndifficulty is the proof of the almost sure reverse inequality:\n\\begin{equation}\\label{limsup}\n\\limsup_{n \\vers +\\infty} \\left\\| P\\left(\\frac{X_n^{(1)}}{\\sqrt{n}}, \\ldots, \\frac{X_n^{(r)}}{\\sqrt{n}},,A_n^{(1)},\\ldots,A_n^{(t)}\\right) \\right\\|\\leq \\left\\| P(x_1, \\ldots x_r,,a_1,\\ldots,a_t)\\right\\|_{\\cal A}. \\ \n\\end{equation}\nThe proof of\n(\\ref{limsup}) consists in two steps.\\\\\n\n\n \\noindent\n{\\bf Step 1: A linearization trick} (see Section 2 and the proof of Proposition 7.3 in \\cite{HT}) \\\\\nIn order to prove (\\ref{limsup}), it is sufficient to prove:\n\\begin{lemme} \\label{inclu} For all $m \\in \\N$, all self-adjoint matrices $\\gamma, \\alpha_1, \\ldots,\\alpha_r, \\beta_1, \\ldots, \\beta_t$ of size $m\\times m$ and\nall $\\epsilon >0$, almost surely for all large $n$,\n$$ \nsp(\\gamma \\otimes I_n + \\sum_{v=1}^r \\alpha_v \\otimes \\frac{X_n^{(v)}}{\\sqrt{n}}+ \\sum_{u=1}^t \\beta_u \\otimes A_n^{(u)})$$ \\begin{equation} \\label{spectre}\\subset\nsp(\\gamma \\otimes 1_{\\cal A} + \\sum_{v=1}^r \\alpha_v \\otimes x_v + \\sum_{u=1}^t \\beta_u \\otimes a_u) + ]-\\epsilon, \\epsilon[.\n\\end{equation}\n\n\\end{lemme}\n\\noindent {\\bf Step 2: An intermediate inclusion}\nLet $a_n=(a_n^{(1)},\\ldots,a_n^{(t)})$ be a t-tuple of noncommutative self-adjoint random variables which is free with the semicircular system $x=(x_1,\\ldots,x_r)$ in $({\\cal A},\\tau$), such that the distribution of $a_n$ coincides with the distribution of $(A_n^{(1)},\\ldots, A_n^{(t)})$ in $({ M}_n(\\mathbb{C}),\\tr_n)$.\nMale \\cite{CamilleM} proved that if $(A_n^{(1)},\\ldots, A_n^{(t)})$ converges strongly to $(a_1,\\ldots, a_t)$, then, for any $\\epsilon >0$, for all large $n$,\\\\\n\n\\noindent \n$\nsp(\\gamma \\otimes 1_{\\cal A} + \\sum_{v=1}^r \\alpha_v \\otimes x_v + \\sum_{u=1}^t \\beta_u \\otimes a_n^{(u)})$ \\begin{equation} \\label{spectre2} \\subset\nsp(\\gamma \\otimes 1_{\\cal A} + \\sum_{v=1}^r \\alpha_v \\otimes x_i + \\sum_{u=1}^t \\beta_u \\otimes a_u) + ]-\\epsilon, \\epsilon[.\n\\end{equation}\nTherefore, Lemma \\ref{inclu} can be deduced from Lemma \\ref{inclu2}.\n\n \n\n\\section{Appendix}\n\\subsection{Basic identities and inequalities}\n\\begin{lemme}\\label{majcarre}\nFor any matrix $M \\in M_m(\\mathbb{C})\\otimes M_n(\\mathbb{C})$ \nand for any fixed $k$, we have \\begin{equation}\\label{O} \\sum_{l =1}^n ||M_{lk} ||^2 \\leq m ||M ||^2\\end{equation}\n(or equivalently \\begin{equation}\\label{l} \\sum_{l =1}^n ||M_{kl} ||^2 \\leq m ||M ||^2.)\\end{equation}\nTherefore, we have \n \\begin{equation}\\label{lp}\n \\frac{1}{n} \\sum_{k,l =1}^n ||M_{kl} ||^2 \\leq m ||M ||^2.\n \\end{equation}\n\\end{lemme}\n\\begin{proof}\nNote that \\begin{eqnarray*} \\sum_{l =1}^n ||M_{lk} ||^2 &\\leq& \\sum_{l =1}^n ||M_{lk} ||_2^2\\\\&=& \\Tr M ( I_m\\otimes E_{kk}) M^*\\\\&=&\\Tr ( I_m\\otimes E_{kk}) M^*M ( I_m\\otimes E_{kk})\\\\&\\leq& \n \\Vert M \\Vert^2 \\Tr ( I_m\\otimes E_{kk})=m \\Vert M \\Vert^2 .\\end{eqnarray*}\nNow, since $$ \\sum_{l =1}^n ||M_{kl} ||^2=\\sum_{l =1}^n ||M_{kl}^* ||^2 = \\sum_{l =1}^n ||(M^*)_{lk} ||^2 \\; \\mbox{and} \\; \\Vert M^* \\Vert= \\Vert M \\Vert,$$\n\\eqref{O} and \\eqref{l} can be deduced from each other thanks to conjugate transposition.\nFinally \\eqref{l} readily yields \\eqref{lp}.\n\\end{proof}\n\\begin{lemme}\nLet $k\\geq 1$. Let $M^{(0)}, M^{(1)},\\ldots, M^{(k)}, M^{(k+1)}$, be $nm\\times nm$ matrices depending on $\\lambda \\in \\{\\rho\\in M_m(\\mathbb{C}), \\Im \\rho>0\\}$ such that \n$\\forall w=0, \\ldots, k+1, \\; \\left\\| M^{(w)} \\right\\| =O(1).$ Assume that for any $ (i,l)\\in \\{1,\\ldots,n\\}^2$,\n$z_{i,l}$ are complex numbers such that $\\sup_{i,l} \\vert z_{i,l} \\vert \\leq C$ for some constant $C$ \nand $\\{ i_w(i,l) \\}_{w=1,\\ldots,k+1}$ and \n $\\{j_w(i,l)\\}_{w=0,\\ldots,k}$ are equal to either $i$ or $l$.\\\\\n Then, \n \\begin{itemize} \\item for any $(p,q)\\in \\{1,\\ldots,n\\}^2,$ \\begin{equation}\\label{Oden}\\sum_{i,l=1}^n z_{i,l} M^{(0)}_{pj_0} M^{(1)}_{i_1 j_1}\\cdots M^{(k)}_{i_k j_k} M^{(k+1)}_{i_{k+1}q}=O_{p,q}^{(u)}(n),\\end{equation}\n\\item if there exists $w_0\\in \\{1,\\ldots,k\\}$ such that $(i_{w_{0}}, j_{w_0})\\in \\{(i,l),(l,i)\\}$, then for any $(p,q)\\in \\{1,\\ldots,n\\}^2,$ \\begin{equation}\\label{Oderacine}\\sum_{i,l=1}^n z_{i,l} M^{(0)}_{pj_0} M^{(1)}_{i_1 j_1}\\cdots M^{(k)}_{i_k j_k} M^{(k+1)}_{i_{k+1}q} =O_{p,q}^{(u)}(\\sqrt{n}),\\end{equation}\n\\item if there exists $w_0\\in \\{1,\\ldots,k\\}$ such that $(i_{w_{0}}, j_{w_0})\\in \\{(i,l),(l,i)\\}$, then\n \\begin{equation}\\label{Odenracinepas}\\sum_{i,l=1}^n z_{i,l} M^{(1)}_{i_1 j_1}\\cdots M^{(k)}_{i_k j_k} =O(n\\sqrt{n}),\\end{equation}\n \\item if there exists $(w_0, w_1)\\in \\{1,\\ldots,k\\}^2$, $w_0\\neq w_1$, such that \n $\\{(i_{w_{0}}, j_{w_0}), (i_{w_{1}}, j_{w_1})\\}$ is a subset of $\\{(i,l),(l,i)\\}^2$\n then \n \\begin{equation}\\label{Odenpas}\\sum_{i,l=1}^n z_{i,l} M^{(1)}_{i_1 j_1}\\cdots M^{(k)}_{i_k j_k} =O(n).\\end{equation}\n\\end{itemize}\n\\end{lemme}\n\\begin{proof}\nIf $(j_0, i_{k+1}) \\in \\{(i,l),(l,i)\\}$, noticing (using Lemma \\ref{majcarre}) that \\begin{eqnarray*}\\sum_{i,l=1}^n \\left\\|M^{(0)}_{pi} \\right\\| \\left\\| M^{(k+1)}_{lq}\\right\\|\n&\\leq& \\left( \\sum_{i=1}^n \\left\\|M^{(0)}_{pi} \\right\\|^2 \\right)^{1\/2} \\sqrt{n} \\left( \\sum_{l=1}^n \\left\\|M^{(k+1)}_{lq} \\right\\|^2 \\right)^{1\/2} \\sqrt{n} \\\\&=&O_{p,q}^{(u)}(n),\n\\end{eqnarray*}\n\\eqref{Oden} follows. \\\\\nNow, if $(j_0, i_{k+1}) \\in \\{(i,i),(l,l)\\}$, noticing (using Lemma \\ref{majcarre}) that \\begin{eqnarray*}\\sum_{i,l=1}^n \\left\\|M^{(0)}_{pi} \\right\\| \\left\\| M^{(k+1)}_{iq}\\right\\|\n&\\leq& n \\left( \\sum_{i=1}^n \\left\\|M^{(0)}_{pi} \\right\\|^2 \\right)^{1\/2} \\left( \\sum_{i=1}^n \\left\\|M^{(k+1)}_{iq} \\right\\|^2 \\right)^{1\/2} \n \\\\&=&O_{p,q}^{(u)}(n),\n\\end{eqnarray*}\n\\eqref{Oden} follows. The proof of \\eqref{Oden} is complete.\\\\\n\n\\noindent Now, assume that there exists $w_0\\in \\{1,\\ldots,k\\}$ such that $(i_{w_{0}}, j_{w_0})\\in \\{(i,l),(l,i)\\}$. Let $\\tilde M^{(w_0)}$ be either $ M^{(w_0)}$ or $ (M^{(w_0)})^*$.\\\\\nIf $(j_0, i_{k+1}) \\in \\{(i,l),(l,i)\\}$, noticing (using Lemma \\ref{majcarre}) that\n\\\\\n\n$\\sum_{i,l=1}^n \\left\\|M^{(0)}_{pi} \\right\\|\\left\\| \\tilde M^{(w_0)}_{il}\\right\\| \\left\\| M^{(k+1)}_{lq}\\right\\|$ \\begin{eqnarray*}\n&\\leq& \\sum_{l=1}^n \\left\\|M^{(k+1)}_{lq} \\right\\| \\left( \\sum_{i=1}^n \\left\\|M^{(0)}_{pi} \\right\\|^2 \\right)^{1\/2} \\left( \\sum_{i=1}^n \\left\\|\\tilde M^{(w_0)}_{il} \\right\\|^2 \\right)^{1\/2} \\\\&\\leq & \\left( \\sum_{i=1}^n \\left\\|M^{(0)}_{pi} \\right\\|^2 \\right)^{1\/2} \n\\left( \\sum_{i=1}^n \\left\\|M^{(k+1)}_{lq} \\right\\|^2 \\right)^{1\/2} \\left( \\sum_{i,l=1}^n \\left\\|\\tilde M^{(w_0)}_{il} \\right\\|^2 \\right)^{1\/2}\n \\\\&=&O_{p,q}^{(u)}(\\sqrt{n}),\n\\end{eqnarray*}\n\\eqref{Oderacine} follows. \\\\\nNow, if $(j_0, i_{k+1}) \\in \\{(i,i),(l,l)\\}$, noticing (using Lemma \\ref{majcarre}) that \\\\\n\n\\noindent $\\sum_{i,l=1}^n \\left\\|M^{(0)}_{pi} \\right\\| \\left\\| \\tilde M^{(w_0)}_{il}\\right\\| \\left\\| M^{(k+1)}_{iq}\\right\\|$ \\begin{eqnarray*}\n&\\leq& n \\left\\| \\tilde M^{(w_0)}\\right\\| \\left( \\sum_{i=1}^n \\left\\|M^{(0)}_{pi} \\right\\|^2 \\right)^{1\/2} \\left( \\sum_{i=1}^n \\left\\|M^{(k+1)}_{iq} \\right\\|^2 \\right)^{1\/2} \n \\\\&=&O_{p,q}^{(u)}(n),\n\\end{eqnarray*}\n\\eqref{Oderacine} follows. The proof of \\eqref{Oderacine} is complete.\\\\\n\n\\noindent Assume that there exists $w_0\\in \\{1,\\ldots,k\\}$ such that $(i_{w_{0}}, j_{w_0})\\in \\{(i,l),(l,i)\\}$; then noticing (using Lemma \\ref{majcarre}) that \\begin{eqnarray*} \n\\sum_{i,l=1}^n \\left\\| \\tilde M^{(w_0)}_{il} \\right\\|& \\leq& n \\left( \\sum_{i,l=1}^n \\left\\|\\tilde M^{(w_0)}_{il} \\right\\|^2 \\right)^{1\/2}\\\\&=& O(n\\sqrt{n}),\n\\end{eqnarray*}\n\\eqref{Odenracinepas} follows.\\\\\n\n\\noindent Now, assume that there exists $(w_0, w_1)\\in \\{1,\\ldots,k\\}^2$, $w_0\\neq w_1$, such that \n $\\{(i_{w_{0}}, j_{w_0}), (i_{w_{1}}, j_{w_1})\\}$ is a subset of $\\{(i,l),(l,i)\\}^2$. Let for $h=0,1$, $\\tilde M^{(w_h)}$ be either $ M^{(w_h)}$ or $ (M^{(w_h)})^*$; then noticing (using Lemma \\ref{majcarre}) that \\begin{eqnarray*} \n\\sum_{i,l=1}^n \\left\\| \\tilde M^{(w_0)}_{il} \\right\\| \\left\\| \\tilde M^{(w_1)}_{il} \\right\\|& \\leq& \\left( \\sum_{i,l=1}^n \\left\\|\\tilde M^{(w_0)}_{il} \\right\\|^2 \\right)^{1\/2} \\left( \\sum_{i,l=1}^n \\left\\| \\tilde M^{(w_1)}_{il} \\right\\|^2 \\right)^{1\/2}\\\\&=& O(n),\n\\end{eqnarray*}\n\\eqref{Odenpas} follows.\\\\\n\\end{proof}\n We end by recalling some properties of resolvents.\n First, one can easily see that for any $\\lambda$ and $\\lambda^{'}$ in $M_m(C)$ such that $\\Im(\\lambda)$ and $\\Im(\\lambda^{'})$ are positive\ndefinite,\n \\begin{equation}\\label{difStieljes}(\\lambda \\otimes 1_{{\\cal A}} - s)^{-1}-(\\lambda^{'} \\otimes 1_{{\\cal A}} -\n s)^{-1}=(\\lambda \\otimes 1_{{\\cal A}} - s)^{-1}(\\lambda^{'} -\n \\lambda)(\\lambda^{'} \\otimes 1_{{\\cal A}} - s)^{-1}.\n \\end{equation}\nFor a Hermitian matrix $M$, the derivative w.r.t $M$ of the resolvent $R(z) = (z-M)^{-1}$ satisfies:\n \\begin{equation} \\label{resolvente}\n R'_M(z) . H = R(z) H R(z) \\mbox{ for all Hermitian matrix $H$}. \\end{equation}\n\\begin{lemme} \\label{G}\nLet $\\lambda$ in $M_m(C)$ such that $\\Im(\\lambda)$ is positive\ndefinite and $h$ be a self-adjoint element in $M_m(\\C)\\otimes {\\cal A}$ where ${\\cal A}$ is a ${\\cal C}^*$-algebra endowed with some state $\\tau$. Then\n \\begin{equation}\\label{normeG}\n \\Vert (\\lambda \\otimes 1_{{\\cal A}} - h)^{-1}\\Vert \\leq ||\n \\Im(\\lambda)^{-1}\\Vert \\mbox{~~and~~~}\n || G(\\lambda ) || \\leq || \\Im(\\lambda)^{-1}\n ||,\\end{equation}\nwhere $G(\\lambda) = ({\\rm id}_m \\otimes \\tau) [ (\\lambda \\otimes 1_{{\\cal A}} - h)^{-1}].$\n\\end{lemme}\n\n\n\n \\begin{lemme} \\label{lem2}\n Let $\\lambda$ in $M_m(C)$ such that $\\Im(\\lambda)$ is positive definite, then for any $mn\\times mn$ Hermitian matrix $H$\n \\begin{equation}\\label{norme}\n || (\\lambda \\otimes I_n - H)^{-1} || \\leq || \\Im(\\lambda)^{-1}\n ||,\\end{equation}\n $$ \\forall 1 \\leq k,l \\leq n, || (\\lambda \\otimes I_n - H)^{-1}_{kl} || \\leq || \\Im(\\lambda)^{-1} ||,$$\n and for $p \\geq 2$,\n \\begin{equation}\\label{pplusgrand}\n \\frac{1}{n} \\sum_{k,l =1}^n ||(\\lambda \\otimes I_n - H)^{-1}_{kl} ||^p \\leq m ||\\Im(\\lambda)^{-1} ||^p.\n \\end{equation}\n\n\n\n \\end{lemme}\n \\subsection{Variance estimates}\nWe refer the reader to the book \\cite{Tou}. \nA probability measure $\\mu$ on $\\mathbb{R}$ is said to satisfy the Poincar\\' e inequality with constant $C_{PI}$ if\n for any\n${\\cal C}^1$ function $f: \\R\\rightarrow \\C$ such that $f$ and\n$f' $ are in $L^2(\\mu)$,\n$$\\mathbf{V}(f)\\leq C_{PI}\\int \\vert f' \\vert^2 d\\mu ,$$\n\\noindent with $\\mathbf{V}(f) = \\int \\vert\nf-\\int f d\\mu \\vert^2 d\\mu$. \\\\\n\n\n\\begin{remarque}\\label{multiple} If the law of a random variable $X$ satisfies the Poincar\\'e inequality with constant $C_{PI}$ then, for any fixed $\\alpha \\neq 0$, the law of $\\alpha X$ satisfies the Poincar\\'e inequality with constant $\\alpha^2 C_{PI}$.\\\\\nAssume that probability measures $\\mu_1,\\ldots,\\mu_M$ on $\\mathbb{R}$ satisfy the Poincar\\'e inequality with constant $C_{PI}(1),\\ldots,C_{PI}(M)$ respectively. Then the product measure $\\mu_1\\otimes \\cdots \\otimes \\mu_M$ on $\\mathbb{R}^M$ satisfies the Poincar\\'e inequality with constant $\\displaystyle{C_{PI}^*=\\max_{i\\in\\{1,\\ldots,M\\}}C_{PI}(i)}$ in the sense that for any differentiable function $f$ such that $f$ and its gradient ${\\rm grad} f$ are in $L^2(\\mu_1\\otimes \\cdots \\otimes \\mu_M)$,\n$$\\mathbf{V}(f)\\leq C_{PI}^* \\int \\Vert {\\rm grad} f \\Vert_2 ^2 d\\mu_1\\otimes \\cdots \\otimes \\mu_M$$\n\\noindent with $\\mathbf{V}(f) = \\int \\vert\nf-\\int f d\\mu_1\\otimes \\cdots \\otimes \\mu_M \\vert^2 d\\mu_1\\otimes \\cdots \\otimes \\mu_M$ (see Theorem 2.5 in \\cite{GuZe03}) .\n\\end{remarque}\n\n\n\n\n\n\n\n\n\\begin{lemme}\\label{zitt}[Theorem 1.2 in \\cite{BGMZ}]\nAssume that the distribution of a random variable $X$ is supported in $[-C;C]$ for some constant $C>1$. Let $g$ be an independent standard real Gaussian random variable. Then $X+\\delta g$ satisfies a Poincar\\'e inequality with constant \n$C_{PI}\\leq \\delta^2 \\exp \\left( 4C^2\/\\delta^2\\right)$.\n\\end{lemme}\n\nConsider the linear isomorphism $\\Psi_0$ between $M_n(\\C)_{sa}$ and $ \\mathbb{R}^{n^2}$ defined for any $[b_{kl}]_{k,l=1}^{n} \\in M_n(\\C)_{sa}$ by $$\\Psi_0([b_{kl}]_{k,l=1}^{n}) = ((b_{kk})_{1\\leq k\\leq n}, (\\sqrt{2} \\Re (b_{kl}))_{1\\leq k0$ such that, for any $n\\geq 1$ and for any $\\lambda$ in $M_m(\\mathbb{C})$ such that $\\Im \\lambda$ is positive definite,\nwe have, \n for any deterministic $nm\\times nm$ matrices $F_n^{(1)}$ and $F_n^{(2)}$ such that $\\Vert F_n^{(1)}\\Vert \\leq K$ and $ \\Vert F_n^{(2)}\\Vert \\leq K$, for any $(p,q)\\in \\{1,\\ldots,n\\}^2$,\\\\\n \n \\noindent \n$ \\mathbb{E}\\{\\Vert{\\rm id}_m\\otimes tr_n( F_n^{(1)}R_n(\\lambda)F_n^{(2)})-\\mathbb{E}({\\rm id}_m\\otimes tr_n(F_n^{(1)}R_n(\\lambda)F_n^{(2)}))\\Vert^2\\}$ \\begin{equation}\\label{varhn}\\leq\\frac{K^4C m^3}{n^2}\n \\Vert (\\Im (\\lambda))^{-1}\\Vert^4,\\end{equation}\n $ \\mathbb{E}\\{\\Vert (F_n^{(1)}R_n(\\lambda)F_n^{(2)})_{pq}-\\mathbb{E}((F_n^{(1)}R_n(\\lambda)F_n^{(2)})_{pq})\\Vert^2\\}$ $$~~~~~~~~~~\\leq\\frac{K^4C m^3}{n}\n \\Vert (\\Im(\\lambda))^{-1}\\Vert^4.$$\n\n \\end{lemme}\n\n{\\bf Acknowledgements.} The authors wish to thank an anonymous referee for pertinent comments\nwhich led to an improvement of this paper.\n","meta":{"redpajama_set_name":"RedPajamaArXiv"}} +{"text":"\\section{Introduction}\n\nThere are several reasons to increase the still rather\nmeagre data on very high-$z$, powerful radio galaxies (e.g.\nMcCarthy \\cite{mcc}, Pariskij et al. \\cite{pari:kop}). \nHigh-$z$ radio galaxies are unique laboratories for investigating the early\nstages of galaxy and AGN evolution at look-back times corresponding to \nmore than 90\\% of the age of the universe derived from\nFriedmann models. \nThey may be used as tracers of the first generation of galaxy clusters\n(Peacock \\& Nicholson \\cite{pea:nic}; Peacock \\cite{peac}) and of the\nphysical state of the intergalactic space\n(Parijskij at al. \\cite{pari:goss}). By high-resolution \none may study important morphological\nfeatures related to e.g. \nmerging activity and ``star burst'' regions. \n\nAt redshift $z>2$, 120 radio galaxies are known at present\n(de Brueck et al. \\cite{debr:br}),\nin comparison with about 250 radio loud quasars\n($S_{5GHz}>$0.03 Jy in Veron-Cetty \\& Veron \\cite{ver:ver}),\n though the former are intrinsically more abundant. \nAccording to the popular unified scheme, both classes\nare the same thing. One can study the host galaxies and \nclose environments of radio galaxies, but this is difficult\nfor QSOs at a similar redshift.\n\n\n\n\nOne aspect, where even a single galaxy may be decisive, is the question of\nhow close in time to the cosmological singularity it is possible\nto find galaxies, with normal stellar population\nand supermassive compact objects in their nuclei.\nThough the use of high-$z$ objects\nin classical cosmological tests is hampered by\nsevere problems, development of such tests is still\none aim of observational cosmology.\nTo identify selection effects and\nevolution, large samples are required.\nOne must increase identifications of very remote galaxies, also\nin view of the new generation ground and space telescopes,\nwhich will allow their study at high resolution.\n\n\n\n\\subsection{\nExtension of identified USS sources to fainter fluxes}\n\nIt has been known since the late 70's \n(Tielens et al., \\cite{tie:mi}; Blumenthal \\& Miley \\cite{blu:mi}) \nthat radio sources with steep \nspectra are optically fainter (and hence probably more distant) \nthan sources with flatter spectra. \nLater it was established \nthat observing faint radio sources\nwith ultra-steep spectra (USS) is an efficient\nway to detect radio galaxies at high redshifts\n(see e.g. McCarthy \\cite{mcc}).\nAs the USS Fanaroff-Riley type II \n(FRII-type; Fanaroff \\& Riley \\cite{fan:ri}) \nradio galaxies are not\ngood ``standard radio candles'' and as \nthe reason for the success of\nthe spectrum criterium is not known \n(see e.g. R\\\"{o}ttgering et al. \\cite{rott:lacy}),\nit is not clear what the outcome will be when USS samples are extended to \na progressively fainter flux limit.\nFainter flux may\nimply 1) larger redshifts, 2) similar redshifts, though weaker\nradio luminosity, or 3) smaller redshifts and still weaker luminosities.\nThe first alternative is most interesting, though cases 2 and 3 \nare also important: extension of the luminosity range will help\none to uncover the influence of radio luminosity on the classical\ncosmological tests (angular size--redshift; \nNilsson et al. \\cite{nils:val}\nand Hubble diagram; Eales et al. \\cite{eal:ra}) and\nto decide whether alignment effect depends primarily\non redshift or luminosity.\n\n\nThe flux range where differential normalized source counts \nshow steepening is generally regarded as the most promising \nhunting place for high redshift objects.\nParijskij et al. (\\cite{pari:bur}) pointed out that the bulk of the\nRATAN-600 sample (see below) has fluxes in the range \nof 10-50 mJy at 3.9 GHz where the\nnormalized counts show a maximum steepening, usually interpreted as\na cosmological effect.\nA similar steepening in the counts is seen separately for steep\nspectrum sources (Fig. 6 in Kellermann \\& Wall 1987).\nIt has been suggested (e.g. R\\\"{o}ttgering et al. \\cite{rott:lacy}) \n that the most effective way to \nfind distant galaxies would be a USS sample with \n$S_{408}\\sim 0.2-1$ Jy.\nIndeed, this has proven to be so\nsince about 50\\% of the R\\\"{o}ttgering et al. (\\cite{rott:lacy})\nUSS objects have $z>2$ (van Ojik et al. \\cite{vanojik:rott}).\nThe bright end of the USS sources is well studied\n(e.g. $4C\/USS, B2\/1Jy, MRC\/1Jy$ McCarthy \\cite{mcc}\nand references therein) and\nrecently fainter flux limits have been reached \n(e.g. $B3\/VLA$ $S_{408}>0.8$ Jy Thompson et al. \\cite{thompson};\nESO\/Key-Project $S_{365}>0.3$ Jy\nR\\\"{o}ttgering at el. \\cite{rott:lacy}). \nHowever, in the R\\\"{o}ttgering et al. (\\cite{rott:lacy})\nsample 365 MHz flux density distribution peaks at about 1 Jy. \n\n\n\n\\subsection{RATAN-600 (RC) and UTRAO catalogues}\n\nThis paper is part of a programme initiated\nat the Special Astrophysical Observatory (Russia) with\nthe aim of searching distant radio galaxies and\ninvestigating the early evolutionary stages of\nthe universe (Goss et al. \\cite{goss:par}).\nWe wish to extend the steep-spectrum criteria to fainter fluxes\nthan previously.\nThis is accomplished by RC and UTRAO catalogues (see Fig. ~\\ref{fig1})\n\n\n\n\\begin{figure}\n\\begin{center}\n\\hspace*{0.5cm}\n\\epsfig{file=astrds7556f1.ps, height=5.0cm}\n\\end{center}\n\\caption[]{\nFrequency - flux limit diagram with the positions of the RC sample\nand some other major radio catalogues. \nUTRAO (-36$\\degr < \\delta <$ 72$\\degr$) is the optimum \nlow frequency catalogue presently available, \nwhich can be used for calculating\nthe spectral index for a large part of the RC sample \n($\\delta \\sim 5\\degr$).\nNote that the 6C sample has $\\delta >$ 20$\\degr$.\nThe lines correspond to a source with $\\alpha$=1.\n}\n\\label{fig1}\n\\end{figure}\n\n\n\\begin{figure}\n\\hspace*{0.5cm}\n\\epsfig{file=astrds7556f2.ps, height=5.0cm}\n\\caption[]{\nHubble diagram in R-band for various radio galaxies \nfrom the literature.\nThe triangles are from the complete Molonglo sample\n(McCarthy et al. \\cite{mcc:kap2}),\nthe open boxes are from \nAllington-Smith et al.(\\cite{all:spi}),\nMaxfield et al. (\\cite{max:tho}),\nMcCarthy et al. (\\cite{mcc:spi}),\nMcCarthy et al. (\\cite{mcc:kap1}),\nMcCarthy et al. (\\cite{mcc:van}),\nThompson et al. (\\cite{thompson}),\nWindhorst et al. (\\cite{wind}).\nFilled dots are from\nCarilli et al. (\\cite{car:ro}),\nChambers et al. (\\cite{cham:mi}),\nDjorgovski et al. (\\cite{djor:spin}),\nDunlop\\&Peacock (\\cite{dun}),\nEales et al. (\\cite{eal:raw}), \nHammer\\&LeFevre (\\cite{ham:le}), \nKristian et al. (\\cite{kri:san}),\nLacy et al. (\\cite{lac:mi}), \nLeFevre et al. (\\cite{lefev:ham1}),\nLeFevre\\&Hammer (\\cite{lefev:ham2}),\nLilly (\\cite{lil1}),\nLilly (\\cite{lil2}),\nOwen\\&Keel (\\cite{owen:keel}),\nMiley et al. (\\cite{mi:ch}), \nSpinrad et al. (\\cite{spin}) \nand filled stars are from the ESO\/Key-Project\n(R\\\"{o}ttgering et al. \\cite{rott:miley}\nR\\\"{o}ttgering et al. \\cite{rott:west}\nvan Ojik et al. \\cite{van:rott}).\nOpen symbols are $r$-magnitudes, which are transformed\nas $R=r-0.4$.\nThe histogram of RC\/USS sources\n$R$-magnitudes is shown above.\nThe magnitudes are from K95b.\nPresent NOT-observations are concerned with $R\\la24$. \n}\n\\label{fig2}\n\\end{figure}\n\n\nOur high frequency\ncatalogue is based on a sample of faint radio sources\noriginally discovered using the RATAN-600 radio telescope\nin the \"Kholod\" (\"Cold\") experiment in 1980-81\n(Parijskij et al. \\cite{pari:bur}; Parijskij et al. \\cite{pari:bur2}\nParijskij \\& Korolkov \\cite{par:kor}).\nIn the experiment, performed at 7.6 cm (3.9 GHz),\nthe strip around the sky at $\\delta$=5$\\degr \\pm$ 20$\\arcmin$\nwas surveyed with a limiting flux of about 4 mJy.\nThe RC catalogue resulted in containing 1145 objects.\nWithin the inner strip of $\\pm$ 5$\\arcmin$\nthe completeness of the catalogue reaches 80\\% at\nthe flux limit S$_{3.9} =7.5$ mJy and is almost\n100\\% at 15mJy (Parijskij et al. \\cite{pari:bur}).\nSuch flux limits are really quite faint and allow one\nto identify a large number of steep spectrum sources,\nif a low frequency catalogue with\nsufficiently faint flux limit is available.\nThe UTRAO (Douglas et al. \\cite{douglas})\nis such a catalogue with a\nflux limit of $\\sim$ 100mJy at 365 MHz (see Fig. ~\\ref{fig1}).\nThe RATAN-600 catalogue (RC) provided the first sample \nwhich allowed one\nto calculate the spectral index for practically all\nUTRAO sources within the region covered by the \"Kholod\"\nexperiment (Soboleva et al. \\cite{sobo:pari}).\nOf the original sample of 840 sources (Parijskij et al. \\cite{pari:bur}), \n491 sources matched those of the UTRAO catalogue. \nSoboleva et al. (\\cite{sobo:pari}) could identify optically from\nPOSS (Palomar Optical Sky Survey) 240 sources at galactic \nlatitude $>$ 20$\\degr$.\n\n\n\n\n\\begin{table*}\n\\caption[]{RC\/USS source parameters. \nThe IAU name is in the first column followed by the\nequatorial, then galactic coordinates and galactic extinction in R-band. \nThe radio spectral index is in the seventh column, followed by\n3.9 GHz flux density and the LAS of the radio source.\nThe results of optical identification are in the last column.\nThe data have been taken from \nKopylov et al. \\cite{kop:goss1}, \\cite{kop:goss2} \nand Parijskij et al. \\cite{pari:goss}. }\n\\begin{flushleft}\n\\begin{center}\n\\begin{tabular}{lllrrrlrrr}\n\\noalign{\\smallskip}\n\\hline\n\\noalign{\\smallskip}\nName& R.A. &Decl &$l$&$b$&A$_{R}$& $\\alpha^{365}_{3900}$ & S$_{3900}$ & LAS & m$_{R}$\\\\\n & B1950 &B1950& \\degr &\\degr & & &mJy & $[\\arcsec]$ & \\\\ \n\\noalign{\\smallskip}\n\\hline\n\\noalign{\\smallskip}\n\\object{J0406+0453}&04 03 48.22&4 39 49.7&186 &-33&0.41 &1.02 & 79 & 21.8 & 24.9\\\\\n\\object{J0444+0501}&04 41 38.68&4 55 55.79&192&-25&0.41 &1.09 & 69 & 10.8 &23.0 \\\\\n\\object{J0457+0452}&04 55 15.09&4 49 13.77&194&-22&0.23 &1.12 & 56 & 34 &19.4 \\\\\n\\object{J0459+0456}&04 56 25.51&4 51 30.45&194&-22&0.23 &0.95 & 76 & 63.8 &22.1 \\\\\n\\object{J0506+0508}&05 03 45.56&5 04 21.1 &195&-20&0.27 &0.88 & 70 & 0.8 &21.6 \\\\\n\\object{J0552+0451}&05 50 16.92&4 46 49.9 &201&-10&1.51 &1.18 & 65 & 1.6 &$>$25.5 \\\\\n\\object{J0743+0455}&07 40 36.54&5 03 02.88&214& 13&0.12 &1.07 & 37 & 20.5 &23.5 \\\\\n\\object{J0756+0450}&07 53 31.2 &4 47 17.1 &216& 16&0.07 &1.16 & 14 & $<$1&$>$25.0 \\\\\n\\object{J0837+0446}&08 34 51.28&4 54 51.82&221& 25&0.07 &1.0 & 54 & 3.9 &22.4 \\\\\n\\object{J0845+0444}&08 42 53.28&4 53 52.9 &222& 27&0.09 &1.14 & 135 & 4.6 &21.4 \\\\\n\\object{J0909+0445}&09 07 13.51&4 56 37.0 &225& 32&0.08 &1.0 & 64 & 1 &20.6 \\\\\n\\object{J0934+0505}&09 31 48.21&5 17 10.76&229& 38&0.06 &1.07 & 36 & 5 &24.4 \\\\\n\\object{J1031+0443}&10 28 43.01&4 58 33.53&240& 49&0.06 &1.2 & 191 & 33 &22.5 \\\\\n\\object{J1043+0443}&10 41 10.27&4 56 12.62&243& 52&0.06 &1.14 & 37 & 48 &23.0 \\\\\n\\object{J1113+0436}&11 11 24.05&4 54 20.12&252& 57&0.10 &0.98 & 52 & 29 &22.4 \\\\\n\\object{J1152+0449}&11 49 49.71&5 04 56.75&268& 63&0.07 &1.0 & 29 & 7 &$>$24.0 \\\\\n\\object{J1155+0444}&11 52 45.43&5 00 13.86&269& 63&0.07 &1.0 & 54 & 13 &18.6 \\\\\n\\object{J1219+0446}&12 17 06.94&5 04 02.84&282& 66&0.06 &1.23 & 23 & 118 &22.0 \\\\\n\\object{J1235+0435}&12 33 16.52&4 49 26.7 &292& 67&0.06 &0.98 & 45 & 7 &21.5 \\\\\n\\object{J1322+0449}&13 19 31.84&5 04 28.13&322& 66&0.06 &0.96 & 47 & 7 &20.4 \\\\\n\\object{J1333+0451}&13 30 32.35&5 07 08.5 &328& 65&0.05 &1.3 & 11 & 1 &23.4 \\\\\n\\object{J1333+0452}&13 30 54.66&5 07 21.17&328& 65&0.05 &1.4 & 16 & 54 &23.3 \\\\\n\\object{J1339+0445}&13 37 06.5 &5 10 15.85&332& 64&0.06 &1.07 & 41 & 34 &22.7 \\\\\n\\object{J1347+0441}&13 44 37.58&4 57 16.48&336& 63&0.06 &0.98 & 43 & 1.4 &23.5 \\\\\n\\object{J1429+0501}&14 26 45.73&5 14 43.41&353& 57&0.06 &0.92 & 82 &11.1 &$>$24.0\\\\\n\\object{J1436+0501}&14 34 04.66&5 15 10.8 &356& 56&0.06 &1.25 & 48 & 15 &22.9 \\\\\n\\object{J1439+0455}&14 37 15.64&5 08 38.68&357& 55&0.06 &1.15 & 40 & 17.9&$>$24.0 \\\\\n\\object{J1510+0438}&15 07 43.00&4 50 51.72& 4& 50&0.06 &0.9 & 67 & 3.4 &22.1 \\\\\n\\object{J1609+0456}&16 06 54.69&5 07 50.48& 16& 38&0.14 &1.15 & 30 & 6.3 &$>$24.5 \\\\\n\\object{J1626+0448}&16 24 21.72&4 55 33.4 & 19& 34&0.18 &1.26 & 39 & 2.4 &22.9 \\\\\n\\object{J1646+0501}&16 44 24.94&5 06 28.92& 22& 29&0.27 &0.92 & 54 & 15.7 &21.2 \\\\\n\\object{J1658+0454}&16 55 43.34&4 58 04.9 & 23& 27&0.27 &1.25 & 31&$<$0.3&$>$24.5\\\\\n\\object{J1703+0502}&17 01 01.3 &5 06 20.0 & 24& 26&0.27 &1.18 & 175 & 1.8 &23.6 \\\\\n\\object{J1720+0455}&17 17 36.0 &4 56 48.0 & 26& 22&0.27 &1.22 & 19 & $<$0.5&20.6 \\\\\n\\object{J1725+0457}&17 23 04.58&5 00 05.0 & 27& 21&0.27 &1.26 & 27 & 1 &$>$24.0 \\\\\n\\object{J1735+0454}&17 33 13.52&4 57 07.37& 28& 19&0.39 &1.0 & 30 & 4 &23.5 \\\\\n\\object{J1740+0502}&17 38 06.03&5 04 11.1 & 29& 18&0.41 &1.2 & 32 & 4 &22.5 \\\\\n\\object{J2013+0508}&20 10 54.69&5 01 24.78& 47&-15&0.46 &0.96 & 51 & 10 &21.1 \\\\\n\\object{J2036+0451}&20 34 27.46&4 39 22.7 & 50&-20&0.27 &1.02 & 75 & 56 &19.0 \\\\\n\\object{J2144+0513}&21 41 56.65&4 57 26.1 & 61&-34&0.18 &1.06 & 72 & $<$5.5&18.8 \\\\\n\\noalign{\\smallskip}\t\t\t\t\t\t \n\\hline\t\t\t\t\t\t\t\t \n\\end{tabular}\t\t\t\t\t\t\t \n\\end{center}\n\\end{flushleft}\n\\end{table*}\t\t\t\t\t\t\t \n\t\t\t\t\t\t\t\t \n\t\t\t\t\t\t\t\t \n\n\n\\subsection{\nConstruction and properties of the RC\/USS sample}\n\n\nThe present study is concerned with\nsources in the range 4$^{h}$ 0.9, \\, $f_{\\nu}\\propto\\nu^{-\\alpha}$),\ndouble or triple FRII sources,\nand optically fainter than the POSS limit. The radio morphology\ncomes from observations with the VLA \n(Kopylov et al. \\cite{kop:goss1}).\nThe largest angular size of the radio source (LAS) \nwas not used as a criterion, because\nonly eight sources had LAS larger than 30\".\nThe median LAS of the sample is 7$\\arcsec$. \nThe median 365 MHz flux density is 0.5 Jy (average 0.7 Jy) \nranging from 0.2 Jy to 3 Jy.\n\n\nOptical identifications were made from deep observations at\nthe 6 m telescope, down to about $m_{R}$=24. These results\nand the optical fields around the sources have been reported\nby Kopylov et al. (\\cite{kop:goss2}, here after K95b). \nTable 1 contains information on the basic RC\/USS sample:\nsource name, equatorial and galactic coordinates, spectral index,\nflux, LAS and $m_{R}$. \nFrom this list we selected\nobjects which are not unreasonably faint\n($m_{R} <$ 24 mag) for a medium sized telescope.\n\n\n\nFig.~\\ref{fig2} gives a representative\n m$_{R}$-$z$ Hubble diagram for radio galaxies\ncollected from the literature, together with the magnitude distribution\nof the RC\/USS objects. \nThe Hubble diagram allows one to estimate\na lower limit to redshift, because of the rather sharp lower envelope,\nespecially above $m_{R}$=21. Where the bulk of the RC\/USS galaxies \nare situated, redshift is expected to be $\\ga$0.7 as shown\nin Fig. 2. \nSoboleva et al. (\\cite{sobo:pari}) estimated the maximum \nphotometric redshifts for the RC\/USS objects from \nthe requirement that radio\nluminosity is not higher than optical luminosity: when radio flux\nis known, the minimum optical magnitude may be calculated,\nhence the rough maximum $z_{ph}$, which is usually large, $>$1.\n\n\nIt should be mentioned that one optically\nbright ($m_{R}$=19) object RC2036+0451 was measured at the\n6 m telescope to have $z$=2.95 (Pariskij et al. \\cite{pari:sobo}).\nThough for a quasar, this large $z$ also supports the view that\npresent selection criteria lead to high average redshift.\n\nThe aim of the NOT imaging\nwas to study the morphology\nof the RC\/USS sources with high resolution\nand confirm the optical identifications.\nThis paper is organised as follows. \nIn Sect. 2 we describe our observations\nand data reduction. Morphology of individual galaxies \nis discussed\nin Sect. 3. The results are summarised in Sect. 4.\n\n\n\n\n\\section{Observations and reductions}\n\n\\subsection{Observations}\n\n\nOptical images were obtained with the 2.56 m\nNordic Optical Telescope (NOT)\nat La Palma during three observing runs in March,\nMay and December 1994.\nTable 2 summarises the instrumentation used. \nIn addition, we have\nsome supplementary observations from other observing runs.\nI-band observations of RC1510+0438 were made with \n``Stockholm'' CCD in July 1994 and\nRC2013+0508 was observed with Brocam1 in September 1994.\nThe complete log of observations is given in Table 3.\nFor each observed object it contains the filter used, \nnumber of separate images,\ntotal integration time, seeing, and date. \nCalibration stars from Landolt (\\cite{landolt}) were\nobserved several times each night at a range of air masses.\n\n\n\n\\begin{table*}\n\\caption[]{Instruments}\n\\begin{flushleft}\n\\begin{tabular}{lllll}\n\\noalign{\\smallskip}\n\\hline\n\\noalign{\\smallskip}\nCCD & Date & Field size (pixels) & Field size ($\\arcmin$)&\nPixel size ($\\arcsec$) \\\\\n\\noalign{\\smallskip}\n\\hline\n\\noalign{\\smallskip}\nAstromed &&&&\\\\\nEEV P88200 & March 1994 & 1152x770 & 3.1x2.1& 0.163\\\\\nIAC CCD &&&&\\\\\nTHX31156 & May 1994 & 1024x1024 & 2.4x2.4 &\n0.14\\\\\nBrocam 1 &&&&\\\\\nTK1024A& December 1994 & 1024x1024& 3x3 & 0.176\\\\\n\\noalign{\\smallskip}\n\\hline\n\\end{tabular}\n\\end{flushleft}\n\\end{table*}\n\n\n\n\\begin{table*}\n\\caption[]{Journal of observations. }\n\\begin{flushleft}\n\\begin{tabular}{cccrll|cccrll}\n\\noalign{\\smallskip}\n\\hline\n\\noalign{\\smallskip}\nObject & Band & No. of & T$_{int}$ & Seeing & Date & \nObject & Band & No. of & T$_{int}$ & Seeing & Date \\\\\n & & images & $[sec]$ & $[\\arcsec]$ & 1994 & \n& & images & $[sec]$ & $[\\arcsec]$&1994 \\\\\n\\noalign{\\smallskip}\n\\hline\n\\noalign{\\smallskip}\nRC0444+0501 & R& 5 & 3300 & 2.0 &3.12. &RC1219+0446 & R &5 & 3000 & 0.5&18.5 \\\\\n & I &3 & 2700 & 2.0 & 4.12.&RC1235+0435 & V &5 & 3000 & 3 & 14.3 \\\\\nRC0457+0452 & B &3 & 3200 & 3.2-3.9& 3.\\&5.12.& & V &3 & 2100 & 0.6 &17.5 \\\\\n & V &2 & 1800 & 0.7 & 2.12.& \t & R &3 & 1800 & 3 & 14.3\\\\ \n & R &4 & 3600 & 0.8 & 2.12.&\t& R &3 & 1800 & 0.6&17.5\\\\ \n & I &4 & 2100 & 2.0-2.9 & 4.\\& 5.12. & RC1322+0449 &V&6 &3600 & 1.8 & 14.3\\\\\nRC0459+0456 & V &2 & 1800 & 0.7& 2.12.&\t & R &3 & 1800 & 1.8 & 14.3\\\\\n & R &2 & 1800 & 0.6 & 2.12.& RC1333+0451 & V &1 & 600 &0.6 & 18.5\\\\\n & I &3 & 2700 & 2.0 & 3.12.& \t & R &3 & 2100 & 0.5&18.5 \\\\\nRC0506+0558 & V &2 & 1200 & 0.8& 2.12.&\tRC1339+0445 &V &5 & 3000 & 1.4 & 15.3\\\\\n & R &6 & 1800 & 0.8 & 2.12.&\t & R &3 & 1800 & 1.4 & 15.3\\\\ \n & I &2 & 1200 & 1.5 & 4.12.& RC1347+0441 & V &5 &3300 & 1.1 &16.\\\\\nRC0743+0455 & V &3 & 1800 & 1.3 & 15.3&\t & R &3 & 2400 & 0.7 &16.5 \\\\\n & R &3 & 1800 & 1.1 & 15.3&\t RC1510+0438& V &3 & 2100 & 0.6 &17.5\\\\\n & R &1 & 600 & 0.8 & 2.12.&\t& R &7 & 4100 & 0.5&17\\&18.5 \\\\\nRC0837+0446 & V &3 & 1800 & 0.9 & 14.3&\tRC1609+0456 & V &1& 600 & 0.6 &18.5 \\\\\n & R &4 & 2400 & 1.0 & 14.3&\t & R &2 & 600 & 0.5&18.5 \\\\\n & I &3 & 2400 & 1.6 & 4.12.&RC1626+0448& V &2 & 1200 & 1.7 & 15.3\\\\\nRC0845+0444 & V &1 & 600 & 1.2 & 15.3&\t & R &2 & 1200 & 1.7 & 15.3\\\\\n & R &1 & 600 & 1.1 & 15.3&\tRC1646+0501&V&3&1800 & 1.5-2.0 & 13.3\\\\\n & I &2 & 2800 & 1.2 & 4.12.& & R &3 & 1800 & 1.5-2.0 & 13.3\\\\ \nRC0909+0445 & B &3 & 2700 & 1.8& 3.12.&\tRC1703+0502 &R &4 & 2400 & 0.6 & 17.5\\\\\n & B &4 & 4500 & 3.0 & 5.12.&RC1720+0455& V &2 & 1200 & 1.5-2.0 & 13.3\\\\ \n & V &2 & 1200 & 1.7 & 14.3 & & V &1 & 600 &0.7 &16.5 \\\\ \n & V &2 & 1800 & 1.6 & 3.12.& & R &1 & 600 & 1.5-2.0 & 13.3\\\\ \n & R &2 & 1200 & 1.7 & 14.3&\t & R &2 & 1200 & 0.7 &16.5 \\\\ \n & R &4 & 3600 & 2.0 & 3.12.& RC1735+0454&R&12& 3080& 0.6&17\\&18.5\\\\\n & I &1 & 900 & 1.5 & 4.12. & RC1740+0502&V &3 & 1800 & 0.6 &16.5\\\\\n & I &3 & 2700 & 3.0 & 5.12.& & R &2& 1200 & 0.6 &16.5 \\\\\nRC1031+0443 & V &6 & 3600 &1.5-2.0&13.3&RC2013+0508 & V &2&1800&1.5&3.\\&4.12.\\\\\n & V &3 & 1800 & 1.5 & 15.3 & & R &1 & 900 & 1.2 & 4.12.\\\\\n & R &3 & 1800 & 1.6 & 13.3 & & R &1 & 600 & 0.6 & 4.9. \\\\\n & R &3 & 1800 & 0.9 & 15.3 &RC2036+0451&B&2 &1800 & 1.4 & 2.12.\\\\\n & I &2 & 1800 & 1.4 & 4.12.& & V &2 & 1800 & 1.5 & 3.\\& 4.12.\\\\ \nRC1043+0443 & V &5 & 3000 & 1.7 & 14.3 & & R &1 & 900 & 1.1 & 4.12.\\\\ \n & R &4 & 2400 & 1.7 & 14.3 & & I &1 & 900 & 3.0 & 5.12.\\\\ \nRC1113+0436 & V &5 & 3000 & 1.6 & 15.3 &RC2144+0513 & B &2 & 1800&1.8 & 3.12.\\\\\n & R &3 & 1800 & 1.6 & 15.3 & & V &2 & 1800 & 1.0 & 2.12.\\\\ \nRC1152+0449 & V &5 & 3000 & 1.0 &16.5& & R &3 & 1920 & 1.0 & 2.12.\\\\ \n & R &3 & 1800 & 0.8 &16.5& & I &2 & 1800 & 1.2 & 4.12.\\\\ \nRC1155+0444 & V &1 & 600 & 0.6 &17.5 \\\\\n & R &1 & 600 & 0.6 &17.5 \\\\ \n\\noalign{\\smallskip}\n\\hline\n\\end{tabular}\n\\end{flushleft}\n\\end{table*}\n\n\n\nIn this paper,\nwe shall restrict the discussion to observations\nmade under excellent or good seeing (FWHM $\\la 1\\farcs$1) \nconditions, totaling 22 objects.\nWe present only R-band images except for a few cases where\nthe morphology has a strong wavelength dependence and the\nS\/N-ratio in other passbands is high enough.\nAll observations presented were made under\nphotometric conditions. \n\n``Blooming'' of the CCD was a serious problem with the IAC CCD.\nSome objects were close to bright stars which limited\nthe longest possible exposure time, or a bright star had to be\nmoved outside the CCD.\nRC1735+0454 lies close to the galactic plane, hence\nthe field is crowded with bright stars. \nWe could not obtain long exposures of this faint object \nand had to move it close to the edge of the CCD.\nNote that the exposure time of the greyscale image is \n900 seconds and for the contour image 3080 seconds.\nThe bright star northeast from the centre of gravity of RC1219+0446\nhampered the observations and the northern part of the radio\nsource was not observed.\n\n\\subsection{Reductions}\n\nThe reductions were carried out using standard IRAF routines\n(bias subtraction, trimming, flat fielding).\nThe average bias frame was constructed for each night.\nThe flatfielding was made by twilight flats\nobtained each evening and morning.\nAll the scientific frames were flattened at better than a 1\\%-level.\nThe exposures of each object were registered in position\nusing several stars in the field and then averaged. The number of\nreference stars varied from three up to a dozen. \n\n\nThe astrometric calibration was carried out using the\nAPM Catalogue (Irwin et al. \\cite{irwin}) whenever possible.\nFor the objects near the galactic plane\nthe Guide Star Catalog (GSC) (Lasker et al. \\cite{lasker})\nwas used. \nDue to the small field of view of the CCDs there\nwere typically only a few reference stars in the frame.\nThe number of stars and hence the accuracy\nof the astrometry strongly depends on\ngalactic latitude. We estimate the accuracy\nof the astrometric calibration to be typically better than\n1 second of arc. This is enough for the current study,\nbecause the typical resolution of the radio map is\nabout 1$\\farcs$5\nand most of the radio sources are so compact that the optical\nidentification is straightforward. \n\n\nAs a check of our photometry in the March and May 1994 run\nwe measured comparison stars\nof OJ287 (Fiorucci \\& Tosti \\cite{fiorucci}). \nThe derived brightnesses\nwere consistent with each other within 0.1 magnitudes.\n\n\n\n\n\n\\begin{table*}\n\\caption[]{NOT imaging data. The diameter of the aperture is \nindicated in arcseconds. The magnitudes are without \ncorrection of galactic extinction. Ellipticity\nand position angle of resolved sources is measured\nwith the same aperture as the magnitudes.\nThe radio position angles are measured from Kopylov et al.\n(\\cite{kop:goss1}).\n}\n\\begin{flushleft}\n\\begin{center}\n\\begin{tabular}{llllllcrr}\n\\noalign{\\smallskip}\n\\hline\n\\noalign{\\smallskip}\nName & Aperture & $m_{R}$ & merr&$m_{V-R}$&merr&$e$&PA$_{opt}$ & PA$_{radio}$\\\\\n\\noalign{\\smallskip}\n\\hline\n\\noalign{\\smallskip }\nRC0457+0452 &7 &19.72 &0.01 &0.93 &0.05 &0.13 &13 & 58 \\\\\nRC0506+0558 &3.5 &21.54 &0.03 &1.29 &0.13 &unresolved & .. & -75 \\\\\nRC0837+0446 &6.5 &22.19 &0.08 &0.74 &0.13 &0.23 & 76 & -67 \\\\\nRC0845+0444 &8.2 &20.75 &0.08 &1.15 &0.17 &0.25 & -78 &9 \\\\\nRC1031+0443 &6.5 &22.32 &0.12 &1.12 &0.23 &0.57 & 88 &-36 \\\\\nRC1152+0449 &3 &22.23 &0.07 & 0.84 &0.15 &0.11 & -33 &-15 \\\\\nRC1155+0444 &8.4 &18.81 &0.02 & 1.09 &0.05 &0.28 & -54 &-76 \\\\\nRC1235+0453 &4.2 &21.70 &0.06 & 0.96 &0.17 &0.26 & 43 &-50 \\\\\nRC1347+0441 &2 &23.99 &0.19 & 0.75 &0.43 &0.32 & -29 &-49 \\\\\nRC1510+0438 &3 &22.20 &0.04 & 1.57 &0.25 &0.08 & -79 &62 \\\\\nRC1703+0502 &3 &23.69 &0.18 & .. & .. &0.49 & -86 &-82 \\\\\nRC1720+0455 &4.2 &20.34 &0.02 & 1.15 &0.07 &unresolved & ..&point \\\\\nRC1740+0502 &3 &22.19 &0.08 & 0.74 &0.12 &0.05 & 66 &64 \\\\\nRC2013+0508 &5.3 &20.70 &0.04 & 0.22 &0.06 &unresolved& .. &-55 \\\\\nRC2036+0451 &4.2 &19.06 &0.02 & 0.35 &0.04 &unresolved & .. &-2 \\\\\nRC2144+0513 &3.5 &18.89 &0.02 &0.24 &0.03 &unresolved& .. &point \\\\\n\\noalign{\\smallskip}\n\\hline\n\\end{tabular}\n\\end{center}\n\\end{flushleft}\n\\end{table*}\n\n\\section{Reduced images: Overview of the morphology}\n\n\n\nIn this section \nwe give a greyscale image and a contour map\nfor each identified object (Fig.~\\ref{fig4}) \nThe close environment and faint features can be \nstudied from the greyscale image and\nthe confidence level of the features and light distribution\nfrom the contour map. \nThe field of view of the greyscale image is indicated in the\nupper left hand corner. \nThe images are slightly smoothed with a Gaussian function\n($FWHM=\\frac{1}{2}seeing$) in order to enhance the low \nsurface brightness features and maintain the resolution\nof the original images.\nThe centre of gravity of the radio source and\nthe positions of the radio lobes are indicated with a cross\nand circles, respectively. The images are presented in\nlinear scale from 0$\\sigma$ to 10$\\sigma$ above\nthe background of the image.\nIn contour maps the object is in the origin and \nthe numbers on both the vertical and horizontal\naxes refer to distance in arcsecond.\nThe contour interval is 0.5 mag arcsec$^{-2}$ and\nthe surface brightness of the lowest\ncontour is indicated in the upper right hand corner. \nThe limiting surface brightness at which objects can be detected\nis typically between 25 and 26 mag arcsec$^{-2}$.\nFor RC1510+0438 we also give V and I band images.\nUncertain identifications are presented in Fig. \\ref{fig5}\nand ``faint objects'' in Fig. ~\\ref{fig6}.\n\n\nThe magnitudes are based on aperture photometry using\nDAOPHOT. The size of the aperture was selected\nin such a way that 1) as much light as possible was included\nin the aperture while keeping the errors reasonable but \n2) the companions were excluded.\nThe image shapes are determined by the moments of the \nbrightness distribution\nusing IMEXAMINE (eq. 4 in Valdes et al. \\cite{valdes})\nwith the size of the aperture the same as in photometry. \nThe estimation is vague for small \nellipticity (e.g. RC1152+0449) or when the inner regions \nhave a different position angle than the outer regions\n(e.g. RC1031+0443). The results of photometry and\n image shape analysis are given in Table 4.\nThe magnitudes between this work and K95b\nare generally in agreement.\nThe differences are primarily caused by\nthe better seeing conditions at NOT, as compared with the 6 m-telescope\nand the different size of the aperture.\n\n\n\\subsection{Identified objects}\n\nIn this section we give notes on individual objects (Fig. ~\\ref{fig4}).\nFor the strongest sources alternative names are given in brackets.\n\n\\noindent\n{\\bf \\object{RC0457+0452}}\\\\\nThis is one of the brightest objects in this sample.\nThe new VLA map shows that the radio source has FRI \nstructure (an unpublished radio map).\nThis agrees with a high optical to\nradio luminosity ratio (Parijskij et al. \\cite{pari:goss}).\nThere is a near companion $\\sim$2.5$\\arcsec$ northwest from the nucleus.\nIn addition there are four faint companions in the southeast (5$\\arcsec$).\nThe position angle of the galaxy is not uniform,\nwhich is possibly due the close companions.\nThe inner isophotes are roughly perpendicular to the radio axis\nand the outermost fuzz is roughly aligned with the radio axis. \nThe same trend can be seen in both R and V-band images.\nThis object is possibly situated in a cluster of galaxies and\nthere are a few companions with similar brightness. The \ncompanion 20$\\arcsec$ to the east has a double nucleus\n($m_{R}=19.26, m_{V-R}=0.69$), hence it is \napparently a merger, and the companion 22$\\arcsec$ to the \nsouth has distorted morphology ($m_{R}=19.94, m_{V-R}=0.93$) .\n\n\n\\noindent\n{\\bf \\object{RC0506+0508}}\\\\\nThis is a faint point-like source, even under excellent seeing\nconditions. \nIt might be a high redshift ($z>1, M_{R}< -23$) quasar\nbecause\nin the quasar catalogue by Veron-Cetty \\& Veron (\\cite{ver:ver})\nthere are only a few quasars fainter than \n21 magnitudes with $z<1$.\nThe companions 14$\\arcsec$ to the northwest have a multicomponent\nstructure with an extended diffuse emission. \n\n\\noindent\n{\\bf \\object{RC0837+0446}}\\\\\nThe galaxy is marginally resolved and lies\nin or behind a galaxy cluster.\nIn the lower left hand corner of the grey scale image is a\ntrail of a solar system object.\n\n\\noindent\n{\\bf \\object{RC0845+0444}}\\\\\nThe optical counterpart of this radio source\ncoincides with the western radio component.\nThis object is optically extended \nand there is a \nfaint extension towards the southwest. \n\n\\noindent\n{\\bf \\object{RC1031+0443}}\\\\\n(\\object{4C +05.43} \\& \\object{PKSB1028+049})\nThe galaxy is near the centre of gravity of the radio source. \nThe object is extended and has a multicomponent structure. \nThe strongest optical emission is aligned with the radio source,\nbut the position angle of the outermost isophotes is\nalmost perpendicular to the radio axis.\n\n\\noindent\n{\\bf \\object{RC1152+0449}}\\\\\nThis galaxy \nhas two or possibly three components.\nThe outer isophotes of the galaxy are box-like,\nbut the separate components are roughly aligned with the\nradio axis. \nThe faint companion 4$\\arcsec$ to the west is near to the \nwestern radio lobe. This blue companion may be related \nto the radio source ($m_{R}=23.0, m_{V-R}\\sim$0.3).\n\nThis is the only source where the astrometry of the present work\nis not consistent with the result of K95b, but coincides with \ncurrent identification in Parijskij et al. (\\cite{pari:goss}).\n\n\n\\noindent\n{\\bf \\object{RC1155+0444}}\\\\\nThis is the brightest galaxy in our sample.\nThe galaxy is elliptical and it is clearly aligned \nwith the radio source.\nThe two neighbouring galaxies are possibly interacting\nforeground galaxies.\n\n\n\\noindent\n{\\bf \\object{RC1235+0453}}\\\\\nThis faint galaxy is spatially extended in our R-band images. \nThe fuzz $\\sim 1 \\arcsec$ northwest from the nucleus \nhas a clumpy structure.\nOur deep images show faint low surface brightness \ncompanions near the object $\\sim 10 \\arcsec$ to the east, north and west.\nIn the V-band images only the \ncore of the galaxy is detected.\n\n \n\\noindent\n{\\bf \\object{RC1347+0441}}\\\\\nThis galaxy is the faintest of our sample. \nThe object is elongated and \nthe size of the optical object is roughly the same as the\nradio source. \n\n\\noindent\n{\\bf \\object{RC1510+0438}}\\\\\nThis is the most spectacular object in our sample,\nlying in a group of galaxies and having apparently\nwavelength dependent properties.\nIn the R- and I-band image the object is almost round\n(Table 4) but in the V-band the object is weakly elongated with\nthe same position angle as the radio source.\nThe redshift estimation from BVRI colours suggest\n$z\\sim0.6$ (Pariskij et al. \\cite{pari:sobo}).\nIf this is the case, the strong emission lines $[$\\ion{O}{ii}$]$\n3727 and $[$\\ion{O}{iii}$]$ 5007 would be shifted into R and I band,\nrespectively, possibly causing the wavelngth dependence of morphology.\nHowever, new colours from the 6 m-telescope\ndo not agree with strong line contribution. \nAnother consequence of such redshift would be that\nthis would be one of the faintest\n(intrinsically) radio galaxies in the Hubble diagram (Fig. ~\\ref{fig2}).\nThere are three relatively bright galaxies and one faint companion galaxy\nwithin 5$\\arcsec$ of the object. \nAll the companions are bluer than the object ($m_{R-I}=1.52$).\nThe objects towards the east, \nC1 ($m_{R}=22.54, m_{V-R}=0.51, m_{R-I}=0.27$), \nnorth, C2 ($m_{R}=22.34, m_{V-R}=0.81, m_{R-I}=0.87$) \nand west,C3 ($m_{R}=23.24, m_{R-I}=0.83$) could be foreground galaxies. \nIn addition there is a faint companion 1$\\farcs$5 north from the \nobject.\n\n\\noindent\n{\\bf \\object{RC1703+0502}}\\\\\n(\\object{PKS B1701+051})\nThis is one of the strongest and most compact radio sources\nof the present sample.\nThe optical and radio axes are clearly aligned and the sizes are\nalmost the same.\nThere are a few faint galaxies in the field, but no\nclose companions. \nThis object is possibly located behind \na foreground galaxy cluster although some of the\nfield objects might be faint galactic stars.\n\n\n\n\n\\noindent\n{\\bf \\object{RC1720+0455}}\\\\\nThis object is compact in radio and optical.\nThis suggests that it is a QSO and\nthe same conclusion may be drawn from radio-optical luminosity\nconsideration (Parijskij et al. 1996a).\nThe extension towards the southeast, seen in K95b, was an artifact. \nThere are a few companions close to the object.\nThe southern companion either has a double nucleus or a dust lane. \nThe wide field image shows several faint companions,\nhence this galaxy is either in a cluster of galaxies or behind one.\n\n\n\\noindent\n{\\bf \\object{RC1740+0502}}\\\\\nThis source was identified by K95b \nand it is marginally resolved in the R-band image. \nIn the V-band image the object has an extension to the\nsouth west in contrast to almost round morphology in R-band\n(see Table 4).\n\n\n\\noindent\n{\\bf \\object{RC2013+0508}}\\\\\nThis unresolved object could possibly be a quasar.\nBecause of its low galactic latitude ($b\\sim$-15),\nthe field is crowded with stars. \n\n\n\\noindent\n{\\bf \\object{RC2036+0451}}\\\\\n(\\object{MRC 2034+046}).\nThis is the second of the two triple radio sources in this sample. \nA point source coincides with the central component fairly well. \nThis is the only object with known redshift ($z$=2.95\nPariskij et al. \\cite{pari:goss}).\nThis indicates that the\nabsolute magnitude of this quasar is M$_{R}\\approx$-29.\n\n\n\\noindent\n{\\bf \\object{RC2144+0513}}\\\\\nThis object is unresolved. \nThe companion 3$\\arcsec$ to the southwest is most likely a\ngalactic star ($m_{R}=20.90, m_{V-R}=1.1$). \nThe profile of this object matches \nperfectly with the average stellar profile from the same field.\n\n\n\n\\subsection{Uncertain identifications}\n\n\\noindent\n{\\bf \\object{RC0459+0456}}\\\\\n(\\object{MRC 0456+048})\nThis source has two candidates for optical identification in\nK95b. Id1 ($m_{R}=22.08$) is an elongated galaxy with roughly the same \nposition angle as the radio source. This object has a \ncompanion to the west (Id2). This is a marginally resolved \npoint-like source, hence it might be\na quasar with a host galaxy ($m_{R}=21.12$).\nFWHM of the id2 0$\\farcs$66 compares with FWHM of a field star\n0$\\farcs$62.\n\n\n\\noindent\n{\\bf \\object{RC1219+0446}}\\\\\nThis is the largest radio source in our sample\n(118$\\arcsec$). The nature of the source remains unclear\nand it is possible that there are indeed two independent radio sources.\nIf this is one source, then a possible identification would be a\nfaint, rather round galaxy (Id1) 5$\\arcsec$ from the centre of \nradio source ($m_{R}=21.9$).\nOn the other hand if the southern radio lobe is an independent\nobject, the identification could be an unresolved \nobject (Id2) 2$\\arcsec$ southeast from the radio source ($m_{R}=17.88$). \n\n\n\\noindent\n{\\bf \\object{RC1735+0454}}\\\\\nThe possible optical counterpart is $\\sim 3 \\arcsec$ to the east of \nthe radio source. The galaxy has several components and it is elongated\nin a north south direction. \nThe identification should be confirmed by future observations.\n\n\n\\subsection{Faint objects}\n\n\n\\noindent\n{\\bf \\object{RC0743+0455}}\\\\\nThis object is very faint and hardly visible in the 30 min. exposure. \n\n\\noindent\n{\\bf \\object{RC1333+0451}}\\\\\nThe radio source is compact. There is a faint \nextended emission exactly at the position of the radio source. \n\n\n\\noindent\n{\\bf \\object{RC1609+0456}}\\\\\nNew 6-m telescope measurements find an object with $m_{R}\\sim25.5$ \nexactly at the position of the radio source.\nOur 600 second exposures are not deep enough to detect \nthis object.\nThe bright nearby object is unresolved and BVRI photometry by K95b\nsuggests it to be a star.\n\n\n\\section{Concluding remarks}\n\nExcepting the quasar RC2036+0451,\nthe present galaxies do not have measured redshifts as yet. Hence,\nit is interesting to ask whether optical\nmorphology\nprovides information on the redshift\ndistribution of the sample.\nOther properties, for example\nthe Hubble diagram of the RC\/USS sample, suggest (Sec. 1.3) \nthat it contains galaxies with $z\\ga$0.7.\n\n\nOf the 22 observed objects, three are extremely faint and \nthree others are either faint or have several possible \nidentifications. Of the remaining 16 objects, 5 are unresolved.\nThis is roughly the same fraction of point sources \nwhich R\\\"{o}ttgering et al. (\\cite{rott:miley})\nfound, but slightly more than \nLu et al. (\\cite{lu:hof}) found from their \n``distant'' sample ($S_{1.4GHz}>35$mJy), which does not have \nradio spectral index selection criteria.\nTypically RC\/USS objects have a multicomponent\nstructure with extended emission.\nThis compares with\n$HST$ images which have shown that about 30\\% of intermediate\nredshift 3CR galaxies have distorted morphology\n(De Koff et al. \\cite{dekoff}).\nMore distant ($z\\sim1$) 3CR galaxies have typically \nmulticomponent structure with diffuse extended emission \n(Best et al. \\cite{best:long}).\nThe ellipticity ($e$) of the current sample (11 objects) ranges from 0.05\nto 0.57, with a mean of 0.25.\nTaking into account the measurement errors\nthese values agree with studies\nby Rigler et al. (\\cite{rigler}) for 3C galaxies (e=0.19)\n and by R\\\"{o}ttgering et al. (\\cite{rott:miley}) USS sample (e=0.33).\n\n\n\nVisual inspection of the images in Fig. ~\\ref{fig4} suggests \nthat about half of the objects have a companion \nwith comparable brightness within 10$\\arcsec$.\nWe examined the excess of companion galaxies \nalong the radio axis suggested by R\\\"{o}ttgering et al.\n(\\cite{rott:west}). Our sample has 7 resolved objects with \n3$\\arcsec