Deep Neural Networks for combined neutrino energy estimate with KM3NeT/ORCA6
International audience KM3NeT/ORCA is a large-volume water-Cherenkov neutrino detector, currently under construction at the bottom of the Mediterranean Sea at a depth of 2450 meters. The main research goal ofORCA is the measurement of the neutrino mass ordering and the atmospheric neutrino oscillati...
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ftobservparis:oai:HAL:hal-04185320v1 2024-05-19T07:46:47+00:00 Deep Neural Networks for combined neutrino energy estimate with KM3NeT/ORCA6 Peña Martínez, Santiago AstroParticule et Cosmologie (APC (UMR_7164)) Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Observatoire de Paris Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité) KM3NeT Nagoya, Japan 2023-07-26 https://hal.science/hal-04185320 https://doi.org/10.22323/1.444.1035 en eng HAL CCSD info:eu-repo/semantics/altIdentifier/doi/10.22323/1.444.1035 hal-04185320 https://hal.science/hal-04185320 doi:10.22323/1.444.1035 INSPIRE: 2683391 PoS 38th International Cosmic Ray Conference https://hal.science/hal-04185320 38th International Cosmic Ray Conference, Jul 2023, Nagoya, Japan. pp.1035, ⟨10.22323/1.444.1035⟩ atmosphere KM3NeT oscillation sensitivity neural network neutrino: energy [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph] [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex] info:eu-repo/semantics/conferenceObject Conference papers 2023 ftobservparis https://doi.org/10.22323/1.444.1035 2024-04-25T01:02:55Z International audience KM3NeT/ORCA is a large-volume water-Cherenkov neutrino detector, currently under construction at the bottom of the Mediterranean Sea at a depth of 2450 meters. The main research goal ofORCA is the measurement of the neutrino mass ordering and the atmospheric neutrino oscillationparameters. Additionally, the detector is also sensitive to a wide variety of phenomena includingnon-standard neutrino interactions, sterile neutrinos, and neutrino decay.This contribution describes the use of a machine learning framework for building Deep NeuralNetworks (DNN) which combine multiple energy estimates to generate a more precise reconstructed neutrino energy. The model is optimized to improve the oscillation analysis based ona data sample of 433 kton-years of KM3NeT/ORCA with 6 detection units. The performanceof the model is evaluated by determining the sensitivity to oscillation parameters in comparisonwith the standard energy reconstruction method of maximizing a likelihood function. The resultsshow that the DNN is able to provide a better energy estimate with lower bias in the context ofoscillation analyses. Conference Object Orca Archive de l'Observatoire de Paris (HAL) Proceedings of 38th International Cosmic Ray Conference — PoS(ICRC2023) 1035 |
institution |
Open Polar |
collection |
Archive de l'Observatoire de Paris (HAL) |
op_collection_id |
ftobservparis |
language |
English |
topic |
atmosphere KM3NeT oscillation sensitivity neural network neutrino: energy [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph] [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex] |
spellingShingle |
atmosphere KM3NeT oscillation sensitivity neural network neutrino: energy [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph] [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex] Peña Martínez, Santiago Deep Neural Networks for combined neutrino energy estimate with KM3NeT/ORCA6 |
topic_facet |
atmosphere KM3NeT oscillation sensitivity neural network neutrino: energy [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph] [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex] |
description |
International audience KM3NeT/ORCA is a large-volume water-Cherenkov neutrino detector, currently under construction at the bottom of the Mediterranean Sea at a depth of 2450 meters. The main research goal ofORCA is the measurement of the neutrino mass ordering and the atmospheric neutrino oscillationparameters. Additionally, the detector is also sensitive to a wide variety of phenomena includingnon-standard neutrino interactions, sterile neutrinos, and neutrino decay.This contribution describes the use of a machine learning framework for building Deep NeuralNetworks (DNN) which combine multiple energy estimates to generate a more precise reconstructed neutrino energy. The model is optimized to improve the oscillation analysis based ona data sample of 433 kton-years of KM3NeT/ORCA with 6 detection units. The performanceof the model is evaluated by determining the sensitivity to oscillation parameters in comparisonwith the standard energy reconstruction method of maximizing a likelihood function. The resultsshow that the DNN is able to provide a better energy estimate with lower bias in the context ofoscillation analyses. |
author2 |
AstroParticule et Cosmologie (APC (UMR_7164)) Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Observatoire de Paris Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité) KM3NeT |
format |
Conference Object |
author |
Peña Martínez, Santiago |
author_facet |
Peña Martínez, Santiago |
author_sort |
Peña Martínez, Santiago |
title |
Deep Neural Networks for combined neutrino energy estimate with KM3NeT/ORCA6 |
title_short |
Deep Neural Networks for combined neutrino energy estimate with KM3NeT/ORCA6 |
title_full |
Deep Neural Networks for combined neutrino energy estimate with KM3NeT/ORCA6 |
title_fullStr |
Deep Neural Networks for combined neutrino energy estimate with KM3NeT/ORCA6 |
title_full_unstemmed |
Deep Neural Networks for combined neutrino energy estimate with KM3NeT/ORCA6 |
title_sort |
deep neural networks for combined neutrino energy estimate with km3net/orca6 |
publisher |
HAL CCSD |
publishDate |
2023 |
url |
https://hal.science/hal-04185320 https://doi.org/10.22323/1.444.1035 |
op_coverage |
Nagoya, Japan |
genre |
Orca |
genre_facet |
Orca |
op_source |
PoS 38th International Cosmic Ray Conference https://hal.science/hal-04185320 38th International Cosmic Ray Conference, Jul 2023, Nagoya, Japan. pp.1035, ⟨10.22323/1.444.1035⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.22323/1.444.1035 hal-04185320 https://hal.science/hal-04185320 doi:10.22323/1.444.1035 INSPIRE: 2683391 |
op_doi |
https://doi.org/10.22323/1.444.1035 |
container_title |
Proceedings of 38th International Cosmic Ray Conference — PoS(ICRC2023) |
container_start_page |
1035 |
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1799487026449350656 |