# Determination of Quark-Gluon-Plasma Parameters from a Global Bayesian Analysis

## TL;DR

This paper presents a Bayesian analysis method to quantitatively extract quark-gluon plasma properties from heavy-ion collision data, improving parameter constraints and model calibration.

## Contribution

It introduces a novel Bayesian methodology for simultaneous calibration and parameter extraction of quark-gluon plasma properties from collision data.

## Key findings

- Quantitative constraints on the temperature dependence of shear viscosity.
- Successful calibration of the collision model to LHC data.
- Demonstration of a universal, extensible analysis framework.

## Abstract

The quality of data taken at RHIC and LHC as well as the success and sophistication of computational models for the description of ultra-relativistic heavy-ion collisions have advanced to a level that allows for the quantitative extraction of the transport properties of the Quark-Gluon-Plasma. However, the complexity of this task as well as the computational effort associated with it can only be overcome by developing novel methodologies: in this paper we outline such an analysis based on Bayesian Statistics and systematically compare an event-by-event heavy-ion collision model to data from the Large Hadron Collider. We simultaneously probe multiple model parameters including fundamental quark-gluon plasma properties such as the temperature-dependence of the specific shear viscosity $\eta/s$, calibrate the model to optimally reproduce experimental data, and extract quantitative constraints for all parameters simultaneously. The method is universal and easily extensible to other data and collision models.