Transport Properties of QGP within a Bayesian Holographic QCD Model

TL;DR

This paper employs a Bayesian holographic QCD model to compute and analyze the transport coefficients of the quark-gluon plasma, providing uncertainty quantification and validation against lattice QCD and experimental data.

Contribution

It introduces a Bayesian inference framework to holographic QCD, enabling precise estimation of QGP transport properties with uncertainty quantification and comparison to experimental and lattice results.

Findings

Diffusion coefficient aligns with lattice QCD for T ~ 1.2-2 T_c

Jet quenching parameter agrees with RHIC and LHC data

Viscosity coefficients are consistent with existing literature

Abstract

Using a holographic QCD model augmented by Bayesian inference, we calculate key transport coefficients of the quark-gluon plasma (QGP)$\text{-}$including the drag force, jet quenching parameter, heavy quark diffusion coefficient, and shear and bulk viscosities$\text{-}$at finite temperature and chemical potential. Posterior parameter distributions at the 68\% and 95\% confidence levels (CL), as well as the maximum a posteriori (MAP) estimates, are employed to quantify uncertainties. Our findings indicate that the diffusion coefficient within the Bayesian credible regions aligns with lattice QCD results for $T \sim 1.2T_c$ to $2T_c$, and is consistent with ALICE experimental measurements near $T_c$. The jet quenching parameter obtained from the Bayesian analysis agrees with RHIC and LHC data, while viscosity coefficients show compatibility with existing literature. These results demonstrate the efficacy of a Bayesian holographic approach in elucidating the nonperturbative transport properties of QCD matter.