标题： 贝叶斯模型选择与束能扫描重离子碰撞中的不确定性传播 显示英文标题

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

摘要： 我们应用贝叶斯模型选择方法（基于贝叶斯因子）来优化(3+1)维混合框架中现象学参数的$\sqrt{s_\mathrm{NN}}$依赖性，以描述相对论重离子碰撞，在相对论重离子对撞机的束能扫描计划中。 研究了各种实验测量对后验分布的影响。 我们还对金金碰撞中的纵向流去相关性、快速度依赖的各向异性流和识别粒子$v_0(p_\mathrm{T})$以及小系统中的各向异性流系数进行了模型预测。 通过使用从后验分布中采样的少量参数集的模拟结果方差来估计模型预测的系统不确定性。 显示英文摘要

摘要： 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.