Particle-identification techniques and performance at LHCb in Run 2

Abstract

One of the most challenging data analysis tasks of modern High Energy Physics experiments is the identification of particles. In this proceedings we review the new approaches used for particle identification at the LHCb experiment. Machine-Learning based techniques are used to identify the species of charged and neutral particles using several observables obtained by the LHCb sub-detectors. We show the performances of various solutions based on Neural Network and Boosted Decision Tree models. • Advanced machine learning techniques allow to increase particle identification performance both for charged and neutral particles. • Combining information from the LHCb subdetectors allows to achieve high background rejection for charged particle identification. • Using machine learning approaches to analyse the raw energy deposits in the calorimeter provides good prospects for high quality neutral particle identification.