Muon bundle reconstruction with KM3NeT/ORCA using graph convolutional networks
KM3NeT/ORCA is a water-Cherenkov neutrino detector, currently under construction in the Mediterranean Sea at a depth of 2450 meters. The project's main goal is the determination of the neutrino mass hierarchy by measuring the energy- and zenith-angle-resolved oscillation probabilities of atmosp...
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ftunisalernoiris:oai:www.iris.unisa.it:11386/4877718 2024-09-30T14:40:57+00:00 Muon bundle reconstruction with KM3NeT/ORCA using graph convolutional networks Reck, Stefan Eberl, Thomas Katz, Ulì Bozza, Cristiano Keilhauer, Bianca Reck, Stefan Eberl, Thoma Katz, Ulì Bozza, Cristiano 2022 ELETTRONICO https://hdl.handle.net/11386/4877718 https://doi.org/10.22323/1.395.1048 https://pos.sissa.it/395/1048/ eng eng SISSA ispartofbook:37th International Cosmic Ray Conference 37th International Cosmic Ray Conference volume:395 firstpage:1 lastpage:10 numberofpages:10 journal:POS PROCEEDINGS OF SCIENCE https://hdl.handle.net/11386/4877718 doi:10.22323/1.395.1048 https://pos.sissa.it/395/1048/ neural network muon bundle KM3NeT graph network convolutional network info:eu-repo/semantics/conferenceObject 2022 ftunisalernoiris https://doi.org/10.22323/1.395.1048 2024-09-10T23:35:06Z KM3NeT/ORCA is a water-Cherenkov neutrino detector, currently under construction in the Mediterranean Sea at a depth of 2450 meters. The project's main goal is the determination of the neutrino mass hierarchy by measuring the energy- and zenith-angle-resolved oscillation probabilities of atmospheric neutrinos traversing the Earth. Additionally, the detector observes a large amount of atmospheric muons, which can be used to study extensive air showers generated by cosmic ray particles. This work describes a deep-learning based reconstruction of atmospheric muons using graph convolutional networks. They are used to reconstruct the zenith angle, the muon multiplicity and the diameter of atmospheric muon bundles. Simulations and measured data from an early four line stage of the detector are used to evaluate the performance. Furthermore, the reconstructions are compared to the ones of classical approaches, and use cases for the indirect study of cosmic ray particles are shown. Conference Object Orca EleA@Unisa (Università degli Studi di Salerno) Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021) 1048 |
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collection |
EleA@Unisa (Università degli Studi di Salerno) |
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ftunisalernoiris |
language |
English |
topic |
neural network muon bundle KM3NeT graph network convolutional network |
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neural network muon bundle KM3NeT graph network convolutional network Reck, Stefan Eberl, Thomas Katz, Ulì Bozza, Cristiano Muon bundle reconstruction with KM3NeT/ORCA using graph convolutional networks |
topic_facet |
neural network muon bundle KM3NeT graph network convolutional network |
description |
KM3NeT/ORCA is a water-Cherenkov neutrino detector, currently under construction in the Mediterranean Sea at a depth of 2450 meters. The project's main goal is the determination of the neutrino mass hierarchy by measuring the energy- and zenith-angle-resolved oscillation probabilities of atmospheric neutrinos traversing the Earth. Additionally, the detector observes a large amount of atmospheric muons, which can be used to study extensive air showers generated by cosmic ray particles. This work describes a deep-learning based reconstruction of atmospheric muons using graph convolutional networks. They are used to reconstruct the zenith angle, the muon multiplicity and the diameter of atmospheric muon bundles. Simulations and measured data from an early four line stage of the detector are used to evaluate the performance. Furthermore, the reconstructions are compared to the ones of classical approaches, and use cases for the indirect study of cosmic ray particles are shown. |
author2 |
Keilhauer, Bianca Reck, Stefan Eberl, Thoma Katz, Ulì Bozza, Cristiano |
format |
Conference Object |
author |
Reck, Stefan Eberl, Thomas Katz, Ulì Bozza, Cristiano |
author_facet |
Reck, Stefan Eberl, Thomas Katz, Ulì Bozza, Cristiano |
author_sort |
Reck, Stefan |
title |
Muon bundle reconstruction with KM3NeT/ORCA using graph convolutional networks |
title_short |
Muon bundle reconstruction with KM3NeT/ORCA using graph convolutional networks |
title_full |
Muon bundle reconstruction with KM3NeT/ORCA using graph convolutional networks |
title_fullStr |
Muon bundle reconstruction with KM3NeT/ORCA using graph convolutional networks |
title_full_unstemmed |
Muon bundle reconstruction with KM3NeT/ORCA using graph convolutional networks |
title_sort |
muon bundle reconstruction with km3net/orca using graph convolutional networks |
publisher |
SISSA |
publishDate |
2022 |
url |
https://hdl.handle.net/11386/4877718 https://doi.org/10.22323/1.395.1048 https://pos.sissa.it/395/1048/ |
genre |
Orca |
genre_facet |
Orca |
op_relation |
ispartofbook:37th International Cosmic Ray Conference 37th International Cosmic Ray Conference volume:395 firstpage:1 lastpage:10 numberofpages:10 journal:POS PROCEEDINGS OF SCIENCE https://hdl.handle.net/11386/4877718 doi:10.22323/1.395.1048 https://pos.sissa.it/395/1048/ |
op_doi |
https://doi.org/10.22323/1.395.1048 |
container_title |
Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021) |
container_start_page |
1048 |
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1811643399232552960 |