AI 中文总结
本研究提出IPD贝叶斯框架，以MC Glauber模型联合分布为先验、负二项分布为似然，从带电粒子多重数推断碰撞几何参数后验，替代传统硬截断中心度分类，经测试校准良好且提升了净重子累积量重建精度。
AI 中文摘要
我们提出了推断驱动参与者确定（IPD）方法，这是一个贝叶斯框架，用于从相对论重离子碰撞的末态带电粒子多重数中，逐事件推断参与者数（$N_{\text{part}}$）和二元碰撞数（$N_{\text{coll}}$）的后验分布。该方法将Monte-Carlo Glauber模型得到的$(N_{\text{part}}, N_{\text{coll}})$联合分布作为先验，以经带电粒子多重数涨落校准的负二项分布定义似然。该方法用基于$N_{\text{part}}$的概率指派替代了传统的硬截断中心度分类，明确了多重数-几何的弥散效应，降低了体积涨落对下游可观测量的影响。在$\sqrt{s_{NN}} = 19.6$ GeV下采用UrQMD-MCG混合模型的闭合测试表明，该方法得到的后验分布校准良好，偏差可忽略，且相较于传统的基于多重数的中心度选择方法，提升了净重子累积量的重建效果。
英文摘要
We propose the Inference-driven Participant Determination (IPD) method, a Bayesian framework for inferring event-by-event posterior distributions of the number of participants ($N_{\text{part}}$) and binary collisions ($N_{\text{coll}}$) from final-state charged-particle multiplicities in relativistic heavy-ion collisions. The joint distribution of $(N_{\text{part}}, N_{\text{coll}})$ obtained from the Monte-Carlo Glauber model is used as the prior, while negative binomial distributions calibrated to charged-particle multiplicity fluctuations define the likelihood. This approach replaces conventional hard-cut centrality classification with a probabilistic assignment based on $N_{\text{part}}$, making the multiplicity--geometry smearing explicit and reducing the impact of volume fluctuations on downstream observables. A closure test using an UrQMD-MCG hybrid model at $\sqrt{s_{NN}} = 19.6$~GeV shows that the method yields well-calibrated posterior distributions with negligible bias and improves the reconstruction of net-proton cumulants relative to conventional multiplicity-based centrality selection.
