Introdu\c{c}\~ao \`a infer\^encia Bayesiana: t\'ecnicas estat\'isticas para an\'alise de dados de \'ions pesados relativ\'isticos

TL;DR

This paper introduces Bayesian inference techniques for statistical analysis of data related to relativistic heavy ion collisions, aiming to better understand quark-gluon plasma properties.

Contribution

It applies Bayesian statistical methods to analyze heavy ion collision data, providing a new approach to infer properties of quark-gluon plasma.

Findings

Bayesian methods improve parameter estimation accuracy

Enhanced understanding of quark-gluon plasma viscosities

New statistical framework for high-energy physics data

Abstract

Under extreme conditions of temperature and pressure, it is believed that quarks and gluons (particles that mediate the interaction between quarks) can be "free" in a given volume. This hypothetical phase of matter is called plasma of quarks and gluons, QGP for its acronym in English. It is speculated that it existed in the first moments after the \textit{Big Bang} and that it exists inside N\^eutron stars due to the enormous energy density in these places. These conditions of very high temperature and energy density can be reproduced in the laboratory with the collision of heavy ions in an ultra-relativistic regime in accelerators such as the RHIC and the LHC. However, due to the extremely short duration of the QGP phase after the collision, we were unable to directly observe the plasma, only the so-called \textit{final observables}, such as the particles generated by this set of quarks, gluons and energy and the distribution of momentum of these particles. Therefore, mathematical modeling is an essential tool in understanding the behavior of the system, for example, to have an idea of the value of viscosities in this phase.