Improved calorimetric particle identification in NA62 using machine learning techniques

Collaboration
Apr 20, 2023
13 pages
Published in:
  • JHEP 11 (2023) 138
  • Published: Nov 21, 2023
e-Print:
Report number:
  • CERN-EP-2023-066
Experiments:

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Abstract: (Springer)
Measurement of the ultra-rare K+π+νν {K}^{+}\to {\pi}^{+}\nu \overline{\nu} 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 × 105^{−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 105^{−5}.
Note:
  • Updated author list and Ref. 4
  • Fixed Target Experiments
  • Branching fraction
  • Rare Decay
  • Flavour Physics
  • K+: secondary beam
  • K+: rare decay
  • K+: semileptonic decay
  • neutrino: pair production
  • muon: particle identification
  • particle identification: efficiency