Improved calorimetric particle identification in NA62 using machine learning techniques

2023/04/20 by NA62 Collaboration · 1 citation
#FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Instrumentation and Detectors (physics.ins-det)

paper · doi:10.48550/arxiv.2304.10580

Abstract

Measurement of the ultra-rare K⁺→π⁺νν decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of 1.2× 10^-5 for a pion identification efficiency of 75% in the momentum range of 15-40 GeV/c. In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%. Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of 10^-5.

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