Bayesian Inference of Heavy-Quark Dissipation and Jet Transport Parameters from D-Meson observables in heavy-ion collisions at the LHC energies
Xu-Fei Xue, Zi-Xuan Xu, Wei Dai, Jiaxing Zhao, and Ben-Wei Zhang

TL;DR
This study uses Bayesian inference with a unified transport model to simultaneously constrain heavy-quark diffusion and jet transport coefficients in quark-gluon plasma from LHC heavy-ion collision data.
Contribution
It introduces the first combined Bayesian analysis of heavy-quark diffusion and jet transport parameters using D-meson observables in heavy-ion collisions.
Findings
Posterior distributions of transport coefficients are well constrained.
Stronger constraints are obtained from 30-50% centrality data.
The ratio of jet transport to diffusion coefficients shows a non-monotonic temperature dependence.
Abstract
We perform the first simultaneous Bayesian inference of the temperature-dependent heavy-quark spatial diffusion coefficient and the scaled jet transport coefficient in the quark-gluon plasma, utilizing -meson nuclear modification factor and elliptic flow data from Pb-Pb collisions at . The analysis employs a unified improved Langevin transport model that incorporates both collisional and radiative energy loss, followed by coalescence plus fragmentation hadronization. The posterior distributions of the parameters of and those of are well constrained, and compared with the results of theoretical models or other experimental data extraction, respectively. The centrality data provide significantly stronger constraints than the data. The…
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