000001866 001__ 1866
000001866 005__ 20200304034942.0
000001866 037__ $$aBELLE2-CONF-PROC-2020-005
000001866 041__ $$aeng
000001866 100__ $$aMarco Milesi
000001866 245__ $$aLepton identification in Belle II using observables from the electromagnetic calorimeter and precision trackers
000001866 260__ $$a$$c2020-02-24
000001866 300__ $$a7
000001866 520__ $$aWe present a major overhaul to lepton identification for the Belle II experiment, based on a novel multi-variate classification algorithm. Boosted decision trees are trained combining measurements from the electromagnetic calorimeter (ECL) and the tracking system. The chosen observables are sensitive to the different physics that governs interactions of hadrons, electrons and muons with the calorimeter crystals. Dedicated classifiers are used in various detector regions and lepton momentum ranges. The tree output is eventually combined with classifiers that rely upon independent measurements from other sub-detectors. Using simulation, the performance of the new algorithm is com- pared against the method used for analysis of the 2018 Belle II data, namely a likelihood discriminator based on the ratio of energy measured in the ECL over the momentum measured by the trackers. In the critical low momentum region, we largely improve the lepton-pion separation power, decreasing mis- identification probability down to a factor 10 (2) for the same electron (muon) identification efficiency.
000001866 6531_ $$aMachine learning
000001866 6531_ $$aBoosted decision trees
000001866 6531_ $$aECL
000001866 6531_ $$aParticle identification
000001866 700__ $$aJustin Tan2
000001866 700__ $$aPhillip Urquijo
000001866 8560_ $$fmarco.milesi@desy.de
000001866 8564_ $$uhttps://docs.belle2.org/record/1866/files/BELLE2-CONF-PROC-2020-005.pdf