Improved calorimetric particle identification in NA62 using machine learning techniques
(2023) Journal of High Energy Physics — Vol. 11 (2023)
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- et. al.
- Abstract
- Measurement of the ultra-rare $ {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 × 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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Citations
Cortina Gil, E., Kleimenova, A., Minucci, E., Padolski, S., Rumenov Petrov, P., Shaikhiev, A., Volpe, R., Jerhot, J., Lurkin, N., NA62, & et al. (2023). Improved calorimetric particle identification in NA62 using machine learning techniques. Journal of High Energy Physics, 11. https://doi.org/10.1007/JHEP11(2023)138 (Original work published 2023)