000003924 001__ 3924
000003924 005__ 20231019154251.0
000003924 037__ $$aBELLE2-MTHESIS-2023-038
000003924 041__ $$aeng
000003924 100__ $$aAli Bavarchee
000003924 245__ $$aOptimization of the PID algorithms at the Belle II Experiment
000003924 260__ $$aPadova$$bUniversity of Padova$$c2023
000003924 300__ $$a67
000003924 500__ $$aPresented on 05 06 2023
000003924 502__ $$aMSc$$bPadova, University of Padova$$c2023
000003924 520__ $$aParticle identification in the Belle II experiment involves utilizing information from various sub-detectors to classify six different species of charged particles: electrons, muons, charged pions, charged kaons, protons, and deuterons. Previous studies have demonstrated that directly adding log-likelihoods from each detector for each hypothesis is not an optimal use of available information since poorly calibrated detectors can hurt overall particle identification performance. To address these issues, we study different approaches that involve assigning to the individual contributions different weights, depending on the region of the phase space under study. Machine learning tools are employed in order to optimize the weights and study the possible improvements in the performance.
000003924 700__ $$aAlessandro Gaz$$edir.
000003924 8560_ $$fali.bavarchee@gmail.com
000003924 8564_ $$uhttps://docs.belle2.org/record/3924/files/BELLE2-MTHESIS-2023-038.pdf
000003924 980__ $$aTHESIS