000002890 001__ 2890
000002890 005__ 20220921151555.0
000002890 037__ $$aBELLE2-CONF-PROC-2022-008
000002890 041__ $$aeng
000002890 100__ $$aAbtin Narimani Charan
000002890 245__ $$aParticle identification with the Belle II calorimeter using machine learning
000002890 260__ $$a$$c2022-02-27
000002890 300__ $$a5
000002890 520__ $$aI present an application of a convolutional neural network (CNN) to separate muons and pions in the Belle II electromagnetic calorimeter (ECL). The ECL is designed to measure the energy deposited by charged and neutral particles. It also provides important contributions to the particle identification (PID) system. Identification of low-momenta muons and pions in the ECL is crucial if they do not reach the outer muon detector. Track-seeded cluster energy images provide the maximal 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 in $\theta-\phi$ plane and transverse momentum of the track to train a CNN. The CNN exploits the difference between the dispersed energy depositions from pion hadronic interactions and the more localized muon electromagnetic interactions. Using simulation, the performance of the CNN algorithm is compared with other PID methods at Belle II which are based on track-matched clustering information. The results show that the CNN PID method improves muon-pion separation in low momentum.
000002890 6531_ $$aCNN, PID, ECL, Muon, Pion
000002890 8560_ $$fabtin.narimani.charan@desy.de
000002890 8564_ $$uhttps://docs.belle2.org/record/2890/files/BELLE2-CONF-PROC-2022-008.pdf