Phenomenological constraints on the transport properties of QCD matter with data-driven model averaging

TL;DR

This paper uses data from major colliders and Bayesian model averaging to provide the most reliable constraints to date on the shear and bulk viscosities of quark-gluon plasma at high temperatures.

Contribution

It introduces a data-driven Bayesian model averaging approach to account for model uncertainties in constraining QGP viscosities from collider data.

Findings

Constraints on shear and bulk viscosities of QGP at 150-350 MeV.

Probabilistic bounds derived from combined collider data.

Most reliable phenomenological constraints to date.

Abstract

Using combined data from the Relativistic Heavy Ion and Large Hadron Colliders, we constrain the shear and bulk viscosities of quark-gluon plasma (QGP) at temperatures of ${\sim\,}150{-}350$ MeV. We use Bayesian inference to translate experimental and theoretical uncertainties into probabilistic constraints for the viscosities. With Bayesian Model Averaging we account for the irreducible model ambiguities in the transition from a fluid description of the QGP to hadronic transport in the final evolution stage, providing the most reliable phenomenological constraints to date on the QGP viscosities.