# A data-driven analysis of the heavy quark transport coefficient

## TL;DR

This paper employs Bayesian analysis to estimate the temperature-dependent heavy quark diffusion coefficients by calibrating a Langevin model to experimental data from RHIC and LHC, showing consistency with lattice QCD.

## Contribution

It introduces a Bayesian calibration of a Langevin model for heavy quark transport, providing quantitative constraints on diffusion coefficients across different collision energies.

## Key findings

- Diffusion coefficient constrained around (1.3-1.5) T_c
- Model describes R_AA and v_2 at RHIC and LHC
- Results compatible with lattice QCD

## Abstract

Using a Bayesian model-to-data analysis, we estimate the temperature dependence of the heavy quark diffusion coefficients by calibrating to the experimental data of $D$-meson $R_{\mathrm{AA}}$ and $v_2$ in AuAu collisions ($\sqrt{s_{NN}}=200$ GeV) and PbPb collisions ($\sqrt{s_{NN}}=2.76$ TeV)~\cite{Xie:2016iwq}. The spatial diffusion coefficient $D_s2\pi T$ is found to be mostly constraint around $(1.3-1.5) T_c$ and is compatible with lattice QCD calculations. 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 feasibility to apply a Bayesian analysis to quantitatively study the heavy flavor transport in heavy-ion collisions.