Data-driven analysis for the temperature and momentum dependence of the heavy-quark diffusion coefficient in relativistic heavy-ion collisions
Résumé
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.
Mots clés
heavy quark
diffusion coefficient
Bayesian analysis
heavy quark: diffusion
heavy ion: scattering
Monte Carlo: Markov chain
Brookhaven RHIC Coll
momentum dependence
CERN LHC Coll
temperature dependence
Bayesian
lattice field theory
random walk
elliptic flow
heavy quark: suppression
model: hydrodynamics
Langevin equation
flow: anisotropy
quark gluon: plasma
jet: quenching
numerical calculations: interpretation of experiments