Speaker: Łukasz Graczykowski   (Politechnika Warszawska)

Title: “ALICE in particle wonderland - understanding the strong interaction with hadron correlations”

Abstract:
Strong interaction is one of the major topics of fundamental physics research. Relativistic heavy-ion collisions at the LHC create a Quark-Gluon Plasma (QGP): the hottest and densest fluid ever studied in the laboratory. It is speculated that the early Universe existed in such a state around ∼10^-6 seconds after the Big Bang. This is a state of matter where two of the basic features of low-temperature QCD, confinement and chiral symmetry breaking, are no longer present. As the heavy ions collide, an extremely dense region of partons is excited and deposits energy in the overlap region of the collision. The LHC also delivers proton-proton (pp) collisions, where initial state nuclear effects are not relevant, and pPb collisions, which have a further complementary role, where cold nuclear matter effects are present. Those collision systems provide a crucial reference to heavy-ion collisions.

My research touches upon several aspects of the strong interaction via studies of heavy-ion and proton-proton collisions using the ALICE detector, which I will discuss during the seminar (namely the system size and lifetime of the QGP phase, the hadronization process as well as the interactions between final-state hadrons before they reach the detector). I will introduce the basic concepts and methods that are used in my research, that is two-particle correlations, both in the momentum space (referred to as femtoscopy) and in the angular space. Finally, I will also show the connection of this research to areas of science such as hadron physics, astrophysics, and neutron stars.

Moreover, I will show my contribution to the technical aspects of the experiment, which is done in collaboration with WUT computer scientists. Namely, one such project is related to improving the particle identification (PID) capabilities of ALICE by providing Machine Learning-based solutions for general use by the Collaboration. I will briefly discuss the current status of the ML PID as well as the challenges we have encountered so far.