A data-driven analysis of the heavy quark transport coefficient

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 $D_{s}2\pi T$ 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.

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• Cracking the difference of estimating heavy quark transport coefficients in a Quark-Gluon Plasma
• Approach to equilibrium of quarkonium in quark-gluon plasma
• DAB-MOD sensitivity study of heavy flavor $R_{AA}$ and azimuthal anisotropies based on beam energy, initial conditions, hadronization, and suppression mechanisms
• Development of heavy-flavour flow-harmonics in high-energy nuclear collisions
• Event-shape engineering and heavy-flavour observables in relativistic heavy-ion collisions
• Understanding the QCD medium by the diffusion of charm quarks using a Color String Percolation Model
• Investigating Heavy-flavor vs Light-flavor Puzzle with Event Topology and Multiplicity in Proton+Proton Collisions at $\sqrt{s}$ = 13 TeV using PYTHIA8
• Quantifying the Quark Gluon Plasma