Bayesian Model Selection and Uncertainty Propagation for Beam Energy Scan Heavy-Ion Collisions
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| Format: | Preprint |
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2025
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| _version_ | 1866912930539241472 |
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| author | Jahan, Syed Afrid Roch, Hendrik Shen, Chun |
| author_facet | Jahan, Syed Afrid Roch, Hendrik Shen, Chun |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_11394 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bayesian Model Selection and Uncertainty Propagation for Beam Energy Scan Heavy-Ion Collisions Jahan, Syed Afrid Roch, Hendrik Shen, Chun Nuclear Theory High Energy Physics - Phenomenology 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. |
| title | Bayesian Model Selection and Uncertainty Propagation for Beam Energy Scan Heavy-Ion Collisions |
| topic | Nuclear Theory High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2507.11394 |