Abtin Narimani Charan

24 November 2021
ACAT 2021

Abstract: The 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.