A data-driven analysis for the temperature and momentum dependence of the heavy quark diffusion coefficient in relativistic heavy-ion collisions

TL;DR

This paper uses Bayesian methods to estimate the temperature and momentum dependence of the heavy quark diffusion coefficient in relativistic heavy-ion collisions, aligning with lattice QCD and describing experimental data across multiple collision systems.

Contribution

It introduces a Bayesian model-to-data analysis within an improved Langevin framework to extract the heavy quark diffusion coefficient's dependence on temperature and momentum.

Findings

Diffusion coefficient consistent with lattice QCD

Model describes R_AA and v_2 at RHIC and LHC

Capable of describing higher order flow coefficients like v_3

Abstract

By applying a Bayesian model-to-data analysis, we estimate the temperature and momentum dependence of the heavy quark diffusion coefficient in an improved Langevin framework. The posterior range of the diffusion coefficient is obtained by performing a Markov chain Monte Carlo random walk and calibrating on the experimental data of $D$-meson $R_{\mathrm{AA}}$ and $v_2$ in three different collision systems at RHIC and the LHC: AuAu collisions at 200 GeV, PbPb collisions at 2.76 and 5.02 TeV. The spatial diffusion coefficient is found to be consistent with lattice QCD calculations and comparable with other models' estimation. We demonstrate the capability of our improved Langevin model to simultaneously describe the $R_{\mathrm{AA}}$ and $v_2$ at both RHIC and the LHC energies, as well as the higher order flow coefficient such as $D$-meson $v_3$. We show that by applying a Bayesian analysis, we are able to quantitatively and systematically study the heavy flavor dynamics in heavy-ion collisions.