$\Lambda$$_{c}$ detection using a weighted Bayesian PID approach

The aim of this thesis is to evaluate the performance of the ALICE detector for the detection of the $\Lambda$$_{c}$ baryon in PbPb collisions using a novel approach for particle identification (PID) . The main idea in the new approach is the replacement of the usual cut–based selection on the detector signals with one that uses the probabilities derived from the Bayes theorem (used as weight), namely "Weighted Bayesian". In order to provide a quantitative statement and to establish which method is the most efficient, a comparison with other standard PID approaches used in ALICE is presented. To do that a fast Monte Carlo simulation software, tuned on the particle abundances expected in the new LHC regime and on the observed detector performance was implemented. Therefore, a realistic estimate of the $\Lambda$$_{c}$ production in PbPb collisions was derived, combining the known results from previous experiments, and this was used to evaluate the significance after RUN2 and RUN3 data taking LHC periods. This dissertation is composed by three chapters: The physics studied by ALICE will be briefly described in the first chapter, touching topics like the standard model, quantum chromodynamics and the quark gluon plasma. Furthermore, a summary of the latest analysis results at RHIC and at LHC will be described. The second chapter contains a description of ALICE detector, reporting its structure and explaining how its principal components work. These are followed by a discussion on particle identification in ALICE and eventually by a description of the new bayesian approach tested in the simulations presented in this thesis. Finally, the last chapter describes how the simulation and the analysis were performed, and the results obtained with different PID approaches are presented.