Enhancing Bayesian parameter estimation by adapting to multiple energy scales in RHIC and LHC heavy-ion collisions

TL;DR

This paper improves Bayesian parameter estimation in heavy-ion collision models by incorporating multiple energy scales and separate centrality calibration, leading to better data agreement and understanding of collision dynamics.

Contribution

It introduces a method that adapts Bayesian estimation to multiple energy scales and calibrates centrality separately, enhancing model accuracy for heavy-ion collisions.

Findings

Preference for smaller nucleon width and minimum volume parameters.

Improved agreement with flow coefficients and particle yields.

Enhanced understanding of collision dynamics across energies.

Abstract

Improved constraints on current model parameters in a heavy-ion collision model are established using the latest measurements from three distinct collision systems. Various observables are utilized from Au--Au collisions at $\sqrt{s_\mathrm{NN}}=200$~GeV and Pb--Pb collisions at $\sqrt{s_\mathrm{NN}}=5.02$~TeV and $\sqrt{s_\mathrm{NN}}=2.76$~TeV. Additionally, the calibration of centrality is now carried out separately for all parametrizations. The inclusion of an Au--Au collision system with an order of magnitude lower beam energy, along with separate centrality calibration, suggests a preference for smaller values of nucleon width, minimum volume per nucleon, and free-streaming time. The results with the acquired \textit{maximum a posteriori} parameters show improved agreement with the data for the second-order flow coefficient, identified particle yields, and mean transverse momenta. This work contributes to a more comprehensive understanding of heavy-ion collision dynamics and sets the stage for future improvements in theoretical modeling and experimental measurements.