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arXiv · 1704.07800

A data-driven analysis of the heavy quark transport coefficient

Yingru Xu·Marlene Nahrgang·Jonah E. Bernhard·Shanshan Cao·Steffen A. Bass
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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 $D_s2πT$ 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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Yingru Xu, Marlene Nahrgang, Jonah E. Bernhard, Shanshan Cao, Steffen A. Bass. 2017-04-25. A data-driven analysis of the heavy quark transport coefficient. https://doi.org/10.1016/j.nuclphysa.2017.05.035

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