Bayesian Analysis of Non-extensive Parameters in Au-Au Collisions

TL;DR

This paper applies Bayesian statistical methods with MCMC sampling to analyze particle yield ratios in Au-Au collisions, estimating key parameters within Non-Extensive Statistics and assessing model robustness.

Contribution

It introduces a Bayesian framework for analyzing heavy-ion collision data using NES, providing systematic parameter estimation and model comparison.

Findings

Bayesian methods effectively describe heavy-ion collision data.

Estimated parameters include the non-extensive factor q, temperature T, and chemical potential μ.

Bayes factor analysis shows no strong preference for EV-HRG over NES.

Abstract

In this work, a Bayesian statistical framework is employed to analyze particle yield ratios in Au-Au collisions, utilizing Non-Extensive Statistics (NES). Through Markov Chain Monte Carlo (MCMC) sampling, we systematically estimate key parameters, including the non-extensive factor $q$, temperature $T$, and chemical potential $\mu$. Our analysis confirms previous findings highlighting the suitability and robustness of Bayesian methods in describing heavy-ion collision data. A subsequent Bayes factor analysis does not provide definitive evidence favoring an Excluded Volume Hadron Resonance Gas (EV-HRG) model over the simpler NES approach. Overall, these results suggest that combining NES with Bayesian inference can effectively model particle distributions and improve parameter estimation accuracy, demonstrating the potential of this approach for future studies on relativistic nuclear interactions.