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

Steffen A Bass,Jonah Bernhard,J Scott Moreland

doi:10.1016/j.nuclphysa.2017.05.052

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 η/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.

Highlights

Relativistic heavy-ion collisions produce a hot, dense phase of strongly-interacting matter commonly known as the quark-gluon plasma (QGP), which rapidly expands and freezes into hadrons [1, 2, 3, 4, 5, 6, 7]
Since the QGP is not directly observable – only final-state hadrons are detected – present research seeks to quantify the fundamental properties of the QGP, such as its transport coefficients and the nature of the initial state, through comparisons of experimental measurements to computational model calculations
Computational models must take a set of input parameters including the physical properties of interest, simulate the full time-evolution of heavy-ion collisions, and produce outputs analogous to experimental measurements

Summary

Introduction

Relativistic heavy-ion collisions produce a hot, dense phase of strongly-interacting matter commonly known as the quark-gluon plasma (QGP), which rapidly expands and freezes into hadrons [1, 2, 3, 4, 5, 6, 7]. Computational models must take a set of input parameters including the physical properties of interest, simulate the full time-evolution of heavy-ion collisions, and produce outputs analogous to experimental measurements. The true values of the physical properties are extracted by calibrating the input parameters so that the model output optimally reproduces the experimental data. This generic recipe is called “model-to-data comparison”. Challenges faced by this type of analysis are the amount of computational effort required to scan the parameter space of the model and correlations among the input parameters which may affect multiple observables, so that they cannot be constrained independently. Only a broad sketch of the analysis can be given – for a rigorous description we refer the reader to [8, 9]

Bayesian Model to Data Comparison

Other Applications and Outlook