Improved calorimetric particle identification in NA62 using machine learning techniques
Apr 20, 202313 pages
Published in:
- JHEP 11 (2023) 138
- Published: Nov 21, 2023
e-Print:
- 2304.10580 [hep-ex]
Report number:
- CERN-EP-2023-066
Experiments:
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Abstract: (Springer)
Measurement of the ultra-rare 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 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.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
References(19)
Figures(6)
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