Particle identification with machine learning from incomplete data in the ALICE experiment

arXiv:2403.17436v1 Announce Type: cross
Abstract: The ALICE experiment at the LHC measures properties of the strongly interacting matter formed in ultrarelativistic heavy-ion collisions. Such studies require accurate particle identification (PID). ALICE provides PID information via several detectors for particles with momentum from about 100 MeV/c up to 20 GeV/c. Traditionally, particles are selected with rectangular cuts. Acmuch better performance can be achieved with machine learning (ML) methods. Our solution uses multiple neural networks (NN) serving as binary classifiers. Moreover, we …

abstract arxiv cs.lg data experiment hep-ex identification incomplete data information machine machine learning matter particle studies type via