000002771 001__ 2771
000002771 005__ 20220308094904.0
000002771 037__ $$aBELLE2-POSTER-CONF-2021-002
000002771 041__ $$aeng
000002771 100__ $$aAbtin Narimani Charan
000002771 245__ $$aParticle identification with the Belle II calorimeter using machine learning
000002771 260__ $$aACAT 2021$$c2021-11-24
000002771 300__ $$amult. p
000002771 520__ $$aThe Belle II experiment is located at the asymmetric SuperKEKB e+e- collider in Tsukuba, Japan. The Belle II electromagnetic calorimeter (ECL) is designed to measure the energy deposited by charged and neutral particles. It also provides important contributions to the particle identification system. Identification of low-momenta muons and pions in the ECL is crucial if they do not reach the outer muon detector. This talk presents an application of a convolutional neural network (CNN) to separate muons and pions in the ECL. Since track-seeded cluster energy images provide the best possible information, the shape of the energy depositions for muons and pions in the crystals around an extrapolated track at the entering point of the ECL is used together with crystal positions and transverse momentum of the track to train a CNN. The CNN is exploiting the difference between the dispersed energy depositions from pion hadronic interactions and the more localized muon electromagnetic interactions. The performance of the CNN is investigated with a subset of 2020 and 2021 data with almost pure muon and pion samples from different physics channels. Finally, comparisons of the CNN approach with a standard likelihood-based particle identification and a boosted decision tree using shower-shapes are presented.
000002771 8560_ $$fabtin.narimani.charan@desy.de
000002771 8564_ $$uhttps://docs.belle2.org/record/2771/files/BELLE2-POSTER-CONF-2021-002.pdf