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By applying a Bayesian model-to-data analysis, we estimate the temperatureand momentum dependence of the heavy quark diffusion coefficient in an improvedLangevin framework. The posterior range of the diffusion coefficient isobtained by performing a Markov chain Monte Carlo random walk and calibratingon the experimental data of $D$-meson $R_{\mathrm{AA}}$ and $v_2$ in threedifferent 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 isfound to be consistent with lattice QCD calculations and comparable with othermodels' estimation. We demonstrate the capability of our improved Langevinmodel to simultaneously describe the $R_{\mathrm{AA}}$ and $v_2$ at both RHICand 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 toquantitatively and systematically study the heavy flavor dynamics in heavy-ioncollisions.

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