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.
Highlights
Particle identification (PID) plays a crucial role in LHCb [1] analyses
The LHCb PID system is composed of a tracking system, two ring-imaging Cherenkov detectors (RICH), electromagnetic (ECAL) and hadron (HCAL) calorimeters and a series of muon chambers
Global PID at LHCb identifies the charged particle type associated with a given track
Summary
Particle identification (PID) plays a crucial role in LHCb [1] analyses. The LHCb PID system is composed of a tracking system, two ring-imaging Cherenkov detectors (RICH), electromagnetic (ECAL) and hadron (HCAL) calorimeters and a series of muon chambers. Combining information from these subdetectors allows one to distinguish between various species of long-lived charged particles. Advanced machine learning techniques are employed to obtain the best PID performance and control systematic uncertainties in a data-driven way
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