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

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.