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arXiv · 2507.11394

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

Syed Afrid Jahan·Hendrik Roch·Chun Shen
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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.

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Syed Afrid Jahan, Hendrik Roch, Chun Shen. 2025-07-15. Bayesian Model Selection and Uncertainty Propagation for Beam Energy Scan Heavy-Ion Collisions. https://doi.org/10.1103/y962-1lyg

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