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I present an application of a convolutional neural network (CNN) to separatemuons and pions in the Belle II electromagnetic calorimeter (ECL). The ECL isdesigned to measure the energy deposited by charged and neutral particles. Italso provides important contributions to the particle identification (PID)system. Identification of low-momenta muons and pions in the ECL is crucial ifthey do not reach the outer muon detector. Track-seeded cluster energy imagesprovide the maximal possible information. The shape of the energy depositionsfor muons and pions in the crystals around an extrapolated track at theentering point of the ECL is used together with crystal positions in$\theta-\phi$ plane and transverse momentum of the track to train a CNN. TheCNN exploits the difference between the dispersed energy depositions from pionhadronic interactions and the more localized muon electromagnetic interactions.Using simulation, the performance of the CNN algorithm is compared with otherPID methods at Belle II which are based on track-matched clusteringinformation. The results show that the CNN PID method improves muon-pionseparation in low momentum.

Particle identification with the Belle II calorimeter using machine learning