Point-cloud ML models classify charm- and bottom-origin electrons at ~80% purity for 40% efficiency, outperforming a BDT baseline, with performance limited by intrinsic decay similarity.
Acharya, et al., Measurement of electrons from semileptonic heavy-flavour hadron decays at midrapid- ity in pp and Pb-Pb collisions at √sNN =5.02 TeV, Phys
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Heavy-Flavor Electron Classification Using Hadronic Environment as Point Cloud
Point-cloud ML models classify charm- and bottom-origin electrons at ~80% purity for 40% efficiency, outperforming a BDT baseline, with performance limited by intrinsic decay similarity.