Data-driven extraction of heavy quark diffusion in quark-gluon plasma

TL;DR

This paper uses a data-driven approach within a new transport model to extract the temperature-dependent heavy quark diffusion coefficient in quark-gluon plasma, constrained by experimental charm meson data.

Contribution

It introduces a novel data-driven extraction method for the heavy quark diffusion coefficient using the LGR transport framework, focusing on its temperature dependence.

Findings

A strong increase of the diffusion coefficient from $T_c$ to high temperature is favored by data.

The model successfully describes charm meson observables in heavy ion collisions.

Predictions for Bottom meson observables are provided for future experimental validation.

Abstract

Heavy quark production provides a unique probe of the quark-gluon plasma transport properties in heavy ion collisions. Experimental observables like the nuclear modification factor $R_{\rm AA}$ and elliptic anisotropy $v_{2}$ of heavy flavor mesons are sensitive to the heavy quark diffusion coefficient. There now exist an extensive set of such measurements, which allow a data-driven extraction of this coefficient. In this work, we make such an attempt within our recently developed heavy quark transport modeling framework (Langevin-transport with Gluon Radiation, LGR). A question of particular interest is the temperature dependence of the diffusion coefficient, for which we test a wide range of possibility and draw constraints by comparing relevant charm meson data with model results. We find that a relatively strong increase of diffusion coefficient from crossover temperature $T_c$ toward high temperature is preferred by data. We also make predictions for Bottom meson observables for further experimental tests.