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

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.

27 pages, 22 figures, published version

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