Quantifying properties of hot and dense QCD matter through systematic model-to-data comparison

TL;DR

This paper introduces a Bayesian framework to systematically compare heavy-ion collision models with LHC data, enabling precise calibration of quark-gluon plasma properties like shear viscosity and providing quantitative parameter constraints.

Contribution

It presents a universal Bayesian method for model-to-data comparison in heavy-ion physics, allowing simultaneous extraction of multiple fundamental parameters.

Findings

Quantitative constraints on shear viscosity $\\eta/s$.

Successful calibration of collision models to LHC data.

Framework is adaptable to other models and datasets.

Abstract

We systematically compare an event-by-event heavy-ion collision model to data from the Large Hadron Collider. Using a general Bayesian method, we probe multiple model parameters including fundamental quark-gluon plasma properties such as 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.