Bayesian Model Selection and Uncertainty Propagation for Beam Energy Scan Heavy-Ion Collisions

TL;DR

This paper employs Bayesian model selection to optimize parameters in a hybrid framework for relativistic heavy-ion collisions, assessing experimental impacts and predicting observables with quantified uncertainties.

Contribution

It introduces a Bayesian approach to optimize model parameters and quantify uncertainties in heavy-ion collision simulations within the Beam Energy Scan program.

Findings

Optimized model parameters using Bayesian model selection.

Predicted flow observables and identified particle spectra.

Quantified systematic uncertainties in model predictions.

Abstract

We apply the Bayesian model selection method (based on the Bayes factor) to optimize $\sqrt{s_\mathrm{NN}}$-dependence in the phenomenological parameters of the (3+1)-dimensional hybrid framework for describing relativistic heavy-ion collisions within the Beam Energy Scan program at the Relativistic Heavy-Ion Collider. The effects of various experimental measurements on the posterior distribution are investigated. We also make model predictions for longitudinal flow decorrelation, rapidity-dependent anisotropic flow and identified particle $v_0(p_\mathrm{T})$ in Au+Au collisions, as well as anisotropic flow coefficients in small systems. Systematic uncertainties in the model predictions are estimated using the variance of the simulation results with a few parameter sets sampled from the posterior distributions.