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

Published

Journal Article

© 2018 American Physical Society. By applying a Bayesian model-to-data analysis, we estimate the temperature and momentum dependence of the heavy quark diffusion coefficient in an improved Langevin framework. The posterior range of the diffusion coefficient is obtained by performing a Markov chain Monte Carlo random walk and calibrating on the experimental data of D-meson RAA and v2 in three different collision systems at the Relativistic Heavy-Ion Collidaer (RHIC) and the Large Hadron Collider (LHC): Au-Au collisions at 200 GeV and Pb-Pb collisions at 2.76 and 5.02 TeV. The spatial diffusion coefficient is found to be consistent with lattice QCD calculations and comparable with other models' estimation. We demonstrate the capability of our improved Langevin model to simultaneously describe the RAA and v2 at both RHIC and the LHC energies, as well as the higher order flow coefficient such as D meson v3. We show that by applying a Bayesian analysis, we are able to quantitatively and systematically study the heavy flavor dynamics in heavy-ion collisions.

Full Text

Duke Authors

Cited Authors

  • Xu, Y; Bernhard, JE; Bass, SA; Nahrgang, M; Cao, S

Published Date

  • January 24, 2018

Published In

Volume / Issue

  • 97 / 1

Electronic International Standard Serial Number (EISSN)

  • 2469-9993

International Standard Serial Number (ISSN)

  • 2469-9985

Digital Object Identifier (DOI)

  • 10.1103/PhysRevC.97.014907

Citation Source

  • Scopus