Machine Learning based Global Particle Identification Algorithms at the LHCb Experiment

Abstract

One of the most important aspects of data processing at flavor physics experiments is the particle identification (PID) algorithm. In LHCb, several different sub-detector systems provide PID information: the Ring Imaging Cherenkov detectors, the hadronic and electromagnetic calorimeters, and the muon chambers. The charged PID based on the sub-detectors response is considered as a machine learning problem solved in different modes: one-vs-rest, one-vs-one and multi-classification, which affect the models training and prediction. To improve charged particle identification for pions, kaons, protons, muons and electrons, neural network and gradient boosting models have been tested. This paper presents these models and their performance evaluated on Run 2 data and simulation samples. A discussion of the performances is also presented.