Particle identification in KM3NeT/ORCA
International audience One of the main goals of KM3NeT/ORCA is to measure atmospheric neutrino oscillation parameters with competitive precision. To achieve this goal, good discrimination between track-like and shower-like events is necessary, with particular focus on the measurement of the tau neut...
Published in: | Proceedings of 38th International Cosmic Ray Conference — PoS(ICRC2023) |
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ftccsdartic:oai:HAL:hal-04186231v1 2023-10-09T21:55:01+02:00 Particle identification in KM3NeT/ORCA Cerisy, L Pedrajas, A.L. Lastoria, C Perrin-Terrin, M Brunner, J Dabhi, V Centre de Physique des Particules de Marseille (CPPM) Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS) Universitat de València (UV) KM3NeT Nagoya, Japan 2023-07-26 https://hal.science/hal-04186231 https://hal.science/hal-04186231/document https://hal.science/hal-04186231/file/Particle%20identification%20in%20KM3NeT%20ORCA.pdf https://doi.org/10.22323/1.444.1191 en eng HAL CCSD info:eu-repo/semantics/altIdentifier/doi/10.22323/1.444.1191 hal-04186231 https://hal.science/hal-04186231 https://hal.science/hal-04186231/document https://hal.science/hal-04186231/file/Particle%20identification%20in%20KM3NeT%20ORCA.pdf doi:10.22323/1.444.1191 INSPIRE: 2683883 http://creativecommons.org/licenses/by-nc-nd/ info:eu-repo/semantics/OpenAccess PoS 38th International Cosmic Ray Conference https://hal.science/hal-04186231 38th International Cosmic Ray Conference, Jul 2023, Nagoya, Japan. pp.1191, ⟨10.22323/1.444.1191⟩ machine learning neutral current flavor Grid computing particle identification electron: interaction atmosphere neutrino: oscillation charged current KM3NeT muon [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph] info:eu-repo/semantics/conferenceObject Conference papers 2023 ftccsdartic https://doi.org/10.22323/1.444.1191 2023-09-23T22:43:59Z International audience One of the main goals of KM3NeT/ORCA is to measure atmospheric neutrino oscillation parameters with competitive precision. To achieve this goal, good discrimination between track-like and shower-like events is necessary, with particular focus on the measurement of the tau neutrino normalisation. The track-like signal is mainly carried by muon neutrinos from charged current interactions, while the shower-like signal comes from charged current interactions of electron and tau neutrinos, and neutral current interactions of all flavours. A Random Grid Search algorithm is optimised to separate these channels and its performance is compared with machine learning methods using boosted decision trees. This contribution will report on the technical aspects of the algorithm and the performance of the particle identification with data recorded in 2020 and 2021 using an early six-lines configuration of the ORCA detector (ORCA6). Conference Object Orca Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Proceedings of 38th International Cosmic Ray Conference — PoS(ICRC2023) 1191 |
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Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
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ftccsdartic |
language |
English |
topic |
machine learning neutral current flavor Grid computing particle identification electron: interaction atmosphere neutrino: oscillation charged current KM3NeT muon [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph] |
spellingShingle |
machine learning neutral current flavor Grid computing particle identification electron: interaction atmosphere neutrino: oscillation charged current KM3NeT muon [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph] Cerisy, L Pedrajas, A.L. Lastoria, C Perrin-Terrin, M Brunner, J Dabhi, V Particle identification in KM3NeT/ORCA |
topic_facet |
machine learning neutral current flavor Grid computing particle identification electron: interaction atmosphere neutrino: oscillation charged current KM3NeT muon [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph] |
description |
International audience One of the main goals of KM3NeT/ORCA is to measure atmospheric neutrino oscillation parameters with competitive precision. To achieve this goal, good discrimination between track-like and shower-like events is necessary, with particular focus on the measurement of the tau neutrino normalisation. The track-like signal is mainly carried by muon neutrinos from charged current interactions, while the shower-like signal comes from charged current interactions of electron and tau neutrinos, and neutral current interactions of all flavours. A Random Grid Search algorithm is optimised to separate these channels and its performance is compared with machine learning methods using boosted decision trees. This contribution will report on the technical aspects of the algorithm and the performance of the particle identification with data recorded in 2020 and 2021 using an early six-lines configuration of the ORCA detector (ORCA6). |
author2 |
Centre de Physique des Particules de Marseille (CPPM) Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS) Universitat de València (UV) KM3NeT |
format |
Conference Object |
author |
Cerisy, L Pedrajas, A.L. Lastoria, C Perrin-Terrin, M Brunner, J Dabhi, V |
author_facet |
Cerisy, L Pedrajas, A.L. Lastoria, C Perrin-Terrin, M Brunner, J Dabhi, V |
author_sort |
Cerisy, L |
title |
Particle identification in KM3NeT/ORCA |
title_short |
Particle identification in KM3NeT/ORCA |
title_full |
Particle identification in KM3NeT/ORCA |
title_fullStr |
Particle identification in KM3NeT/ORCA |
title_full_unstemmed |
Particle identification in KM3NeT/ORCA |
title_sort |
particle identification in km3net/orca |
publisher |
HAL CCSD |
publishDate |
2023 |
url |
https://hal.science/hal-04186231 https://hal.science/hal-04186231/document https://hal.science/hal-04186231/file/Particle%20identification%20in%20KM3NeT%20ORCA.pdf https://doi.org/10.22323/1.444.1191 |
op_coverage |
Nagoya, Japan |
genre |
Orca |
genre_facet |
Orca |
op_source |
PoS 38th International Cosmic Ray Conference https://hal.science/hal-04186231 38th International Cosmic Ray Conference, Jul 2023, Nagoya, Japan. pp.1191, ⟨10.22323/1.444.1191⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.22323/1.444.1191 hal-04186231 https://hal.science/hal-04186231 https://hal.science/hal-04186231/document https://hal.science/hal-04186231/file/Particle%20identification%20in%20KM3NeT%20ORCA.pdf doi:10.22323/1.444.1191 INSPIRE: 2683883 |
op_rights |
http://creativecommons.org/licenses/by-nc-nd/ info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.22323/1.444.1191 |
container_title |
Proceedings of 38th International Cosmic Ray Conference — PoS(ICRC2023) |
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1191 |
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1779318799812526080 |