Applying Bayesian parameter estimation to relativistic heavy-ion collisions: simultaneous characterization of the initial state and quark-gluon plasma medium

TL;DR

This paper uses Bayesian methods with a multi-parameter model to simultaneously estimate initial conditions and transport properties of the quark-gluon plasma in heavy-ion collisions, providing new constraints on its characteristics.

Contribution

It introduces a Bayesian calibration approach applied to a hybrid model combining initial state modeling and viscous hydrodynamics, offering novel constraints on QGP properties.

Findings

Initial entropy deposition aligns with saturation models

Extracted shear viscosity shows a temperature-dependent relation

Detected a significant nonzero bulk viscosity

Abstract

We quantitatively estimate properties of the quark-gluon plasma created in ultra-relativistic heavy-ion collisions utilizing Bayesian statistics and a multi-parameter model-to-data comparison. The study is performed using a recently developed parametric initial condition model, TRENTO, which interpolates among a general class of particle production schemes, and a modern hybrid model which couples viscous hydrodynamics to a hadronic cascade. We calibrate the model to multiplicity, transverse momentum, and flow data and report constraints on the parametrized initial conditions and the temperature-dependent transport coefficients of the quark-gluon plasma. We show that initial entropy deposition is consistent with a saturation-based picture, extract a relation between the minimum value and slope of the temperature-dependent specific shear viscosity, and find a clear signal for a nonzero bulk viscosity.