Speaker
Description
Centrality determination is essential in relativistic heavy-ion collisions, as it establishes a quantitative relationship between the final-state observables and the collision impact parameter - the key variable governing the initial-state geometry and energy density. This mapping enables robust comparisons among experimental measurements, theoretical calculations, and results from different collision systems. Standard centrality estimators include charged-particle multiplicity, detector hit distributions, and forward energy deposition. The use of multiple independent estimators reduces autocorrelation biases and systematic uncertainties in the reconstructed collision geometry.
We present a novel centrality determination method based on Bayesian inference incorporating two simultaneous observables, which provides improved constraints on initial-state fluctuations. A systematic comparison is made between centrality frameworks using the Monte Carlo Glauber model and our Bayesian approach, applied to Xe+CsI collisions at 3.8A GeV recorded by the BM@N experiment. This analysis yields critical insights into model dependencies and the robustness of centrality determination at intermediate energies.
