Bayesian analysis of (3+1)D relativistic nuclear dynamics with the RHIC beam energy scan data

TL;DR

This paper employs Bayesian inference to constrain the properties of quark-gluon plasma and nuclear dynamics in relativistic heavy-ion collisions, using detailed (3+1)D simulations and experimental data from RHIC's Beam Energy Scan.

Contribution

It introduces a comprehensive Bayesian framework with multiple emulators to analyze high-dimensional model parameters and provides robust constraints on nuclear matter properties.

Findings

Robust constraints on QGP transport coefficients.

Predictions for differential observables with quantified uncertainties.

Insights into parameter sensitivities of experimental observables.

Abstract

This work presents a Bayesian inference study for relativistic heavy-ion collisions in the Beam Energy Scan program at the Relativistic Heavy-Ion Collider. The theoretical model simulates event-by-event (3+1)D collision dynamics using hydrodynamics and hadronic transport theory. We analyze the model's 20-dimensional posterior distributions obtained using three model emulators with different accuracy and demonstrate the essential role of training an accurate model emulator in the Bayesian analysis. Our analysis provides robust constraints on the Quark-Gluon Plasma's transport properties and various aspects of (3+1)D relativistic nuclear dynamics. By running full model simulations with 100 parameter sets sampled from the posterior distribution, we make predictions for $p_{\rm T}$-differential observables and estimate their systematic theory uncertainty. A sensitivity analysis is performed to elucidate how individual experimental observables respond to different model parameters, providing useful physics insights into the phenomenological model for heavy-ion collisions.