Bayesian inference of event-by-event collision geometry from charged-particle multiplicity in heavy-ion collisions

Abstract

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.