Improved calorimetric particle identification in NA62 using machine learning techniques

A bstract Measurement of the ultra-rare K + → π + ν ν ¯ 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 .