Dielectron production in nuclear collisions

A few microseconds after the Big Bang, the universe was an incredibly dense plasma, so hot that no nuclei or compound particles could exist. The tem- perature was about 100.000 times higher than that at the center of the Sun. This particular state of matter, called quark-gluon plasma (QGP), consisted of quarks, which are the smallest building blocks of atomic nuclei, and gluons, which are the massless particles responsible for the interactions between quarks. Under normal conditions quarks and gluons are confined together and to form particles, while in the QGP phase, they are instead free to fly around. In big particle accelerators like the Large Hadron Collider (LHC) at CERN, heavy nuclei are accelerated to velocities close the speed of light and, when colliding to each other, are able to produce a tiny QGP. While the QGP cools down particles are produced like in the early universe. By studying the particles produced in the collisions we can learn about the properties of the QGP. The aim of the project is to study the production of electron-positron pairs (also called dielectrons) with the ALICE experiment at the LHC. Dielectrons are indeed connected with important properties of the QGP, first of all with its temperature which can be extracted from the measurement of the electromag- netic radiation from the QGP. However, there are several sources of dielectrons. Most of them are not produced directly in the QGP but e.g. from decays of particles containing heavy quarks (charm and beauty). So it is important to understand the origin of the observed dielectrons and measure the contribution connected to the temperature of the QGP. This measurement in PbPb collisions is very challenging, due to the high back- ground components. For this, we need to study dielectron production also in smaller systems, i.e. pp and pPb collisions, which are simpler environments where we expect the QGP not to be formed. In this way we can better un- derstand the background components and all side effects which happen in the collisions not related to the QGP formation. In this project we will extensively use Monte Carlo simulations to get the ex- pected distributions of electrons from different sources. We will use the most up-to-date techniques for data analysis. In particular we will use machine learn- ing methods. These methods are based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. With help of these techniques, we will develop and apply predictive algorithms to separate efficiently background and signal components which are contained in the observed dielectron distributions. 1