Nara Moraes

Abstract

In nuclear theory, the characterization of transport parameters and phase transition properties of a new state of matter, the quark-gluon plasma (QGP), believed to have existed only in the earliest moments after the Big Bang, has been a central objective of scientists since the 1980s, when the development of quantum chromodynamics (QCD) predicted its existence. Since then, scientists have sought to reproduce the conditions necessary for its formation in large heavy-ion collision facilities, such as the LHC and RHIC. According to QCD, a smooth transition is expected to occur at energy densities of 1 GeV/fm³. More recent experiments reach center-of-mass energies of up to 5.2 TeV, sufficient to produce the conditions required for QGP formation. This state is estimated to last on the order of 10⁻²³ s, and therefore analyses of particle collision detector data can only probe properties of the matter after hadronization. The extraction of the physical parameters of the QGP is performed through the model-to-data comparison methodology, in which simulations of heavy-ion collisions are confronted with measured observables. Recent studies have achieved precise estimates of parameters such as the shear viscosity η/s using Bayesian inference, combined with Gaussian process emulators to efficiently explore the model parameter space. This approach accounts for multiple sources of uncertainty, including statistical and systematic uncertainties in the experimental data, uncertainties associated with the physical model and the emulation process, as well as correlations between different experimental observables. However, the correlation structure of these uncertainties has often been treated in a simplified manner, typically assuming independence between bins or employing limited approximations. In this work, we propose a systematic analysis of the impact of correlations in experimental uncertainties on the Bayesian inference of QGP parameters. In particular, we investigate how different structures of the covariance matrix of observables, including correlations between adjacent bins, global systematic effects, and varying correlation lengths, affect the shape of the posterior distribution, the width of marginal parameter distributions, and the correlation structure among them, with the aim of achieving a more robust and accurate characterization of the properties of the quark-gluon plasma.

Keywords: Quark-Gluon Plasma, Heavy-Ion Collisions, Bayesian Inference, Gaussian Processes, Correlated Experimental Uncertainties.