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.

Cortina Gil, E., Kleimenova, A., Minucci, E., Padolski, S., Petrov, P., Shaikhiev, A., et al. (2023). Improved calorimetric particle identification in NA62 using machine learning techniques. JOURNAL OF HIGH ENERGY PHYSICS, 2023(11) [10.1007/JHEP11(2023)138].

Improved calorimetric particle identification in NA62 using machine learning techniques

Bonaiuto V.;Sargeni F.;
2023-01-01

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.
2023
Pubblicato
Rilevanza internazionale
Articolo
Esperti anonimi
Settore FIS/04
English
Branching fraction
Fixed Target Experiments
Flavour Physics
Rare Decay
Cortina Gil, E., Kleimenova, A., Minucci, E., Padolski, S., Petrov, P., Shaikhiev, A., et al. (2023). Improved calorimetric particle identification in NA62 using machine learning techniques. JOURNAL OF HIGH ENERGY PHYSICS, 2023(11) [10.1007/JHEP11(2023)138].
Cortina Gil, E; Kleimenova, A; Minucci, E; Padolski, S; Petrov, P; Shaikhiev, A; Volpe, R; Fedorko, W; Numao, T; Petrov, Y; Velghe, B; Wong, Vws; Yu, ...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/348010
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