Ali Bavarchee ; Alessandro Gaz

2023
University of Padova Padova

Abstract: Particle identification in the Belle II experiment involves utilizing information from various sub-detectors to classify six different species of charged particles: electrons, muons, charged pions, charged kaons, protons, and deuterons. Previous studies have demonstrated that directly adding log-likelihoods from each detector for each hypothesis is not an optimal use of available information since poorly calibrated detectors can hurt overall particle identification performance. To address these issues, we study different approaches that involve assigning to the individual contributions different weights, depending on the region of the phase space under study. Machine learning tools are employed in order to optimize the weights and study the possible improvements in the performance.

Note: Presented on 05 06 2023
Note: MSc