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A data-driven analysis for the temperature and momentum dependence of the heavy quark diffusion coefficient in relativistic heavy-ion collisions
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abstract
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 $R_{\mathrm{AA}}$ and $v_2$ in three different 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 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 $R_{\mathrm{AA}}$ and $v_2$ at both RHIC and 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 to quantitatively and systematically study the heavy flavor dynamics in heavy-ion collisions.
Forward citations
Cited by 7 Pith papers
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Momentum Dependence of Heavy Quark Diffusion in a Thermal Gluonic Plasma on the Lattice
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Bayesian Inference of Heavy-Quark Dissipation and Jet Transport Parameters from D-Meson observables in heavy-ion collisions at the LHC energies
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Stochastic Dynamics of Heavy Quarks in Strongly Coupled Plasma
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Melting of $c \bar c$ and $b \bar b$ pairs in the pre-equilibrium stage of proton-nucleus collisions at the Large Hadron Collider
Color decorrelation in the evolving glasma melts roughly half of initially singlet charm and bottom quark pairs within about 0.4 to 0.5 fm/c after their formation in proton-nucleus collisions at the LHC.
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Constraining Jet Quenching in Heavy-Ion Collisions with Bayesian Inference
A Bayesian fit to LHC jet data claims a universal jet energy-loss distribution and super-Casimir color dependence, but the color result depends on a theory-informed prior.
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