Bibliographic Details
| Title: |
Improved calorimetric particle identification in NA62 using machine learning techniques |
| Authors: |
The NA62 collaboration, E. Cortina Gil, A. Kleimenova, E. Minucci, S. Padolski, P. Petrov, A. Shaikhiev, R. Volpe, W. Fedorko, T. Numao, Y. Petrov, B. Velghe, V. W. S. Wong, M. Yu, D. Bryman, J. Fu, Z. Hives, T. Husek, J. Jerhot, K. Kampf, M. Zamkovsky, B. De Martino, M. Perrin-Terrin, A. T. Akmete, R. Aliberti, G. Khoriauli, J. Kunze, D. Lomidze, L. Peruzzo, M. Vormstein, R. Wanke, P. Dalpiaz, M. Fiorini, A. Mazzolari, I. Neri, A. Norton, F. Petrucci, M. Soldani, H. Wahl, L. Bandiera, A. Cotta Ramusino, A. Gianoli, M. Romagnoni, A. Sytov, E. Iacopini, G. Latino, M. Lenti, P. Lo Chiatto, I. Panichi, A. Parenti, A. Bizzeti, F. Bucci, A. Antonelli, G. Georgiev, V. Kozhuharov, G. Lanfranchi, S. Martellotti, M. Moulson, T. Spadaro, G. Tinti, F. Ambrosino, T. Capussela, M. Corvino, M. D’Errico, D. Di Filippo, R. Fiorenza, R. Giordano, P. Massarotti, M. Mirra, M. Napolitano, I. Rosa, G. Saracino, G. Anzivino, F. Brizioli, E. Imbergamo, R. Lollini, R. Piandani, C. Santoni, M. Barbanera, P. Cenci, B. Checcucci, P. Lubrano, M. Lupi, M. Pepe, M. Piccini, F. Costantini, L. Di Lella, N. Doble, M. Giorgi, S. Giudici, G. Lamanna, E. Lari, E. Pedreschi, M. Sozzi, C. Cerri, R. Fantechi, L. Pontisso, F. Spinella, I. Mannelli, G. D’Agostini, M. Raggi, A. Biagioni, P. Cretaro, O. Frezza, E. Leonardi, A. Lonardo, M. Turisini, P. Valente, P. Vicini, R. Ammendola, V. Bonaiuto, A. Fucci, A. Salamon, F. Sargeni, R. Arcidiacono, B. Bloch-Devaux, M. Boretto, E. Menichetti, E. Migliore, D. Soldi, C. Biino, A. Filippi, F. Marchetto, A. Briano Olvera, J. Engelfried, N. Estrada-Tristan, M. A. Reyes Santos, P. Boboc, A. M. Bragadireanu, S. A. Ghinescu, O. E. Hutanu, L. Bician, T. Blazek, V. Cerny, Z. Kucerova, J. Bernhard, A. Ceccucci, M. Ceoletta, H. Danielsson, N. De Simone, F. Duval, B. Döbrich, L. Federici, E. Gamberini, L. Gatignon, R. Guida, F. Hahn, E. B. Holzer, B. Jenninger, M. Koval, P. Laycock, G. Lehmann Miotto, P. Lichard, A. Mapelli, R. Marchevski, K. Massri, M. Noy, V. Palladino, J. Pinzino, V. Ryjov, S. Schuchmann, S. Venditti, T. Bache, M. B. Brunetti, V. Duk, V. Fascianelli, J. R. Fry, F. Gonnella, E. Goudzovski, J. Henshaw, L. Iacobuzio, C. Kenworthy, C. Lazzeroni, N. Lurkin, F. Newson, C. Parkinson, A. Romano, J. Sanders, A. Sergi, A. Sturgess, J. Swallow, A. Tomczak, H. Heath, R. Page, S. Trilov, B. Angelucci, D. Britton, C. Graham, D. Protopopescu, J. Carmignani, J. B. Dainton, R. W. L. Jones, G. Ruggiero, L. Fulton, D. Hutchcroft, E. Maurice, B. Wrona, A. Conovaloff, P. Cooper, D. Coward, P. Rubin, A. Baeva, D. Baigarashev, D. Emelyanov, T. Enik, V. Falaleev, S. Fedotov, K. Gorshanov, E. Gushchin, V. Kekelidze, D. Kereibay, S. Kholodenko, A. Khotyantsev, A. Korotkova, Y. Kudenko, V. Kurochka, V. Kurshetsov, L. Litov, D. Madigozhin, M. Medvedeva, A. Mefodev, M. Misheva, N. Molokanova, S. Movchan, V. Obraztsov, A. Okhotnikov, A. Ostankov, I. Polenkevich, Yu. Potrebenikov, A. Sadovskiy, V. Semenov, S. Shkarovskiy, V. Sugonyaev, O. Yushchenko, A. Zinchenko |
| Source: |
Journal of High Energy Physics, Vol 2023, Iss 11, Pp 1-15 (2023) |
| Publisher Information: |
SpringerOpen, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Nuclear and particle physics. Atomic energy. Radioactivity |
| Subject Terms: |
Fixed Target Experiments, Branching fraction, Rare Decay, Flavour Physics, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798 |
| More Details: |
Abstract Measurement of the ultra-rare K + → π + ν ν ¯ $$ {K}^{+}\to {\pi}^{+}\nu \overline{\nu} $$ 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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1029-8479 |
| Relation: |
https://doaj.org/toc/1029-8479 |
| DOI: |
10.1007/JHEP11(2023)138 |
| Access URL: |
https://doaj.org/article/b0925bcdc8a24492bdae2ca893573300 |
| Accession Number: |
edsdoj.b0925bcdc8a24492bdae2ca893573300 |
| Database: |
Directory of Open Access Journals |
