Efficient Computational method using random matrices describing critical thermodynamics
Roberto da Silva, Eliseu Venites, Sandra D. Prado, J. R. Drugowich de, Felicio

TL;DR
This paper introduces a novel matrix-based spectral method using Wishart-like matrices derived from magnetization data to identify and analyze phase transitions and critical phenomena in the Q-state Potts model, revealing universal eigenvalue behaviors.
Contribution
It presents a new computational approach linking spectral properties of correlation matrices to thermodynamic phase transitions in spin systems, including a didactic toy model for universal behavior.
Findings
Successfully distinguishes second-order and weaker first-order transitions.
Establishes a strong correlation between spectral eigenvalues and thermodynamic properties.
Reveals universal eigenvalue distribution patterns influenced by temperature and correlations.
Abstract
Our research highlights the effectiveness of utilizing matrices akin to Wishart matrices, derived from magnetization time series data under specific dynamics, to elucidate phase transitions and critical phenomena in the Q-state Potts model. By employing appropriate statistical methods, we not only discern second-order transitions but also differentiate weaker first-order transitions through careful analysis of the density of eigenvalues and their fluctuations. Furthermore, we investigate the method's sensitivity to stronger first-order transition points. Importantly, we establish a robust correlation between the system's actual thermodynamics and the spectral thermodynamics encapsulated within the eigenvalues. Our findings are further substantiated by correlation histograms of the time series data, revealing insightful patterns. Expanding upon our core findings, we present a didactic…
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Taxonomy
TopicsTheoretical and Computational Physics · Complex Systems and Time Series Analysis · Spectroscopy and Quantum Chemical Studies
