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arXiv:1403.7218 · Europhysics Letters (EPL)

Spectral analysis of finite-time correlation matrices near equilibrium phase transitions

Vinayak, T. Prosen, Berislav Buča, T. H. Seligman

Published Oct 1, 2014• 24 citations
arXivHTMLPDF

Abstract

We study spectral densities for systems on lattices, which, at a phase transition display, power-law spatial correlations. Constructing the spatial correlation matrix we prove that its eigenvalue density shows a power law that can be derived from the spatial correlations. In practice time series are short in the sense that they are either not stationary over long time intervals or not available over long time intervals. Also we usually do not have time series for all variables available. We shall make numerical simulations on a two-dimensional Ising model with the usual Metropolis algorithm as time evolution. Using all spins on a grid with periodic boundary conditions we find a power law, that is, for large grids, compatible with the analytic result. We still find a power law even if we choose a fairly small subset of grid points at random. The exponents of the power laws will be smaller under such circumstances. For very short time series leading to singular correlation matrices we use a recently developed technique to lift the degeneracy at zero in the spectrum and find a significant signature of critical behavior even in this case as compared to high temperature results which tend to those of random matrix models.

DOI
10.1209/0295-5075/108/20006

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We study spectral densities for systems on lattices, which, at a phase transition display, power-law spatial correlations. Constructing the spatial correlation matrix we prove that its eigenvalue density shows a power law that can be derived from the spatial correlations. In practice time series are short in the sense that they are either not stationary over long time intervals or not available over long time intervals. Also we usually do not have time series for all variables available. We shall make numerical simulations on a two-dimensional Ising model with the usual Metropolis algorithm as time evolution. Using all spins on a grid with periodic boundary conditions we find a power law, that is, for large grids, compatible with the analytic result. We still find a power law even if we choose a fairly small subset of grid points at random. The exponents of the power laws will be smaller under such circumstances. For very short time series leading to singular correlation matrices we use a recently developed technique to lift the degeneracy at zero in the spectrum and find a significant signature of critical behavior even in this case as compared to high temperature results which tend to those of random matrix models.
The earlier times of evolution of a magnetic system contain more information than we can imagine. Capturing correlation matrices G of different time evolutions of a simple testbed spin system, as the Ising model, we analyzed the density of eigenvalues of G^{T}G for different temperatures. We observe a transition of the shape of the distribution that presents a gap of eigenvalues from critical temperature with a continuous migration to the Marchenko-Pastur law for the paramagnetic phase. We consider the analysis a promising method to be applied in other spin systems to characterize phase transitions. Our approach is different from alternatives in the literature since it uses the magnetization matrix and not the spatial matrix of spins.
An introductory review to short-time critical dynamics is given. From the scaling relation valid already in the early stage of the evolution of a system at or near the critical point, one derives power law behaviour for various quantities. By a numerical simulation of the system one can measure the critical exponents and, by searching for the best power law behaviour, one can determine the critical point. Critical slowing down as well as finite size corrections are nearly absent, since the correlation length is still small for times far before equilibrium is reached. By measuring the (pseudo) critical points it is also possible to distinguish (weak) first-order from second-order phase transitions.
Many important concepts used in the study of complex systems have their origin in the lattice statistical mechanics of cooperative phase transitions. The classic order-disorder transitions of Potts/Ising models illustrate emergent phenomena such as spontaneous symmetry breaking, scale-free critical behaviour and power-law singularities. However, a class of lattice statistics models show that rather more complex behaviour can be obtained, under conditions which are suffciently general that they might also occur in less idealised models. A 5-state lattice statistics model is analysed to explore behaviour in and around a so-called phase. The massless phase, characterised by power law decay of correlations, appears as an intermediate regime between high temperature disorder and low-temperature ordering. The critical exponents (and fractal spatial distributions) occur over a finite range of temperatures. The state of the massless phase is characterised by a topological ordering with analogues of spin-waves being the dominant perturbations. The transition from the ordered to massless is analysed using new exact series expansions obtained by the finite lattice method. The results are also compared to Monte Carlo simulations. The results are compared to other studies in the small number of cases where such series expansions exist for comparable models, including the 6-state clock model. One of the properties that makes the massless phase dificult to study is the weak nature of the associated Kosterlitz-Thouless transition with exponentially-weak behaviour rather than power-law behaviour. As a further diffculty, the limiting behaviour is confined to a narrow regime, outside which one sees an apparent cross-over to power law behaviour. This suggests that behaviour analogous to the phases will be diffcult to characterise as one moves beyond idealised lattice systems.
Many important concepts used in the study of complex systems have their origin in the lattice statistical mechanics of cooperative phase transitions. The classic order-disorder transitions of Potts/Ising models illustrate emergent phenomena such as spontaneous symmetry breaking, scale-free critical behaviour and power-law singularities. However, a class of lattice statistics models show that rather more complex behaviour can be obtained, under conditions which are suffciently general that they might also occur in less idealised models. A 5-state lattice statistics model is analysed to explore behaviour in and around a so-called "massless phase". The massless phase, characterised by power law decay of correlations, appears as an intermediate regime between high temperature disorder and low-temperature ordering. The critical exponents (and fractal spatial distributions) occur over a finite range of temperatures. The state of the massless phase is characterised by a topological ordering with analogues of spin-waves being the dominant perturbations. The transition from the ordered to massless phases is analysed using new exact series expansions obtained by the finite lattice method. The results are also compared to Monte Carlo simulations. The results are compared to other studies in the small number of cases where such series expansions exist for comparable models, including the 6-state "clock model". One of the properties that makes the massless phase dificult to study is the weak nature of the associated Kosterlitz-Thouless transition with exponentially-weak behaviour rather than power-law behaviour. As a further diffculty, the limiting behaviour is confined to a narrow regime, outside which one sees an apparent "cross-over" to power law behaviour. This suggests that behaviour analogous to the "massless phases" will be diffcult to characterise as one moves beyond idealised lattice systems.
The earlier times of the evolution of a magnetic system contain more information than we can imagine. Capturing correlation matrices built from different time evolutions of a simple testbed spin system, as the spin-1/2 and spin-1 Ising models, we analyzed the density of eigenvalues for different temperatures of the so called Wishart matrices. We observe a transition in the shape of the distribution that presents a gap of eigenvalues for temperatures lower than the critical temperature, or in its roundness, with a continuous migration to the Marchenko–Pastur law in the paramagnetic phase. We consider the analysis a promising method to be applied in other spin systems, with or without defined Hamiltonian, to characterize phase transitions. Our approach differs from the alternatives in literature since it uses the concept of magnetization matrix, not the spatial matrix of single spins.
We investigate a one-dimensional tight-binding model in which onsite potentials [Formula: see text] exhibit power-law spatial correlations (with exponent α) and the hopping amplitudes decay as [Formula: see text]. This two-parameter family interpolates continuously between short-range Anderson-like disorder, correlated disorder with conventional hopping, and long-range hopping models with nontrivial delocalization tendencies. Using large-scale exact diagonalization, we construct a comprehensive phase map in the [Formula: see text] plane by combining spectral statistics, density-of-states analysis, and energy-resolved localization indicators such as the participation ratio, single-particle entanglement entropy, level-spacing ratio r, and the ratio of the geometric to arithmetic density of states. From these observables we define phase-indicator functions that compactly quantify localization behavior across the spectrum. Our analysis reveals robust mobility edges and multiple regimes of spectral coexistence between localized, extended, resonant, and critical states. Finite-size scaling, implemented via an explicit smoothness-based cost function, enables extraction of critical exponents and delineation of transition lines across the [Formula: see text] parameter space. To validate and complement these physics-based diagnostics, we employ a supervised autoencoder that learns high-level representations of eigenstate structure directly from raw features and reliably reproduces the phase classification defined by the indicator functions. Together, these approaches provide a coherent and internally consistent picture of the spectral transitions driven by correlated disorder and long-range hopping, establishing a unified framework for characterizing mobility edges in long-range one-dimensional systems.