Ensemble-free configurational temperature for spin systems

TL;DR

This paper introduces a Bayesian-based estimator for the dynamical temperature in spin systems, validated through simulations of the 2D XY-model, providing a robust tool for assessing thermalization and ergodicity.

Contribution

The paper derives a new ensemble-free microscopic temperature estimator using Bayesian principles, applicable to arbitrary spin system ensembles.

Findings

Estimator accurately measures temperature in the 2D XY-model

Provides direct assessment of thermalization process

Enables rigorous testing of ergodicity in simulations

Abstract

An estimator for the dynamical temperature in an arbitrary ensemble is derived in the framework of Bayesian statistical mechanics and the maximum entropy principle. We test this estimator numerically by a simulation of the two-dimensional XY-model in the canonical ensemble. As this model is critical in the whole region of temperatures below the Berezinski-Kosterlitz-Thouless critical temperature $T_{BKT}$, we use a generalization of Wolff's uni-cluster algorithm. The numerical results allow us to confirm the robustness of the analytical expression for the microscopic estimator of the temperature. This microscopic estimator has also the advantage that it gives a direct measure of the thermalization process and can be used to compute absolute errors associated to statistical fluctuations. In consequence, this estimator allows for a direct, absolute and astringent test of the ergodicity of the underlying Markov process, which encodes the algorithm used in a numerical simulation.