Bayesian Inference of Heavy-Quark Dissipation and Jet Transport Parameters from D-Meson observables in heavy-ion collisions at the LHC energies

Abstract: We perform the first simultaneous Bayesian inference of the temperature-dependent heavy-quark spatial diffusion coefficient $2πT\mathcal{D}<em>s$ and the scaled jet transport coefficient $\hat{q}/T<sup>3$ in the quark-gluon plasma, utilizing $D$-meson nuclear modification factor $R</em>\text{AA}$ and elliptic flow $v_2$ data from Pb-Pb collisions at $\sqrt{s_\text{NN}} = 5.02\ \text{TeV}$. 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 $\hat{q}/T<sup>3$ and those of $2πT\mathcal{D}_s$ are well constrained, and compared with the results of theoretical models or other experimental data extraction, respectively. The $30-50\%$ centrality data provide significantly stronger constraints than the $0-10\%$ data. The extracted ratio $\hat{q}/κ$ between the quark jet transport and heavy-quark diffusion coefficients exhibits a non-monotonic temperature dependence, deviating from the value $2$ estimated from the definition, with a value interval spanning 0.25--0.8 corresponding to the mean values of the inferred parameters. This work establishes a data-driven quantitative relationship between these two fundamental transport properties in the same observables, offering crucial insight into their interplay in the strongly coupled medium.