Event reconstruction for KM3NeT/ORCA using convolutional neural networks
The authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), LabEx UnivEart...
Published in: | Journal of Instrumentation |
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Format: | Article in Journal/Newspaper |
Language: | English |
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IOP Publishing
2020
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Online Access: | http://hdl.handle.net/10481/64400 https://doi.org/10.1088/1748-0221/15/10/P10005 |
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DIGIBUG: Repositorio Institucional de la Universidad de Granada |
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ftunivgranada |
language |
English |
topic |
Cherenkov detectors Large detector systems for particle and astroparticle physics Neutrino detectors Performance of High Energy Physics Detectors |
spellingShingle |
Cherenkov detectors Large detector systems for particle and astroparticle physics Neutrino detectors Performance of High Energy Physics Detectors Aiello, S. Anguita López, Mancia Díaz García, Antonio Francisco López Coto, Daniel Navas Concha, Sergio Tenllado, Enrique Event reconstruction for KM3NeT/ORCA using convolutional neural networks |
topic_facet |
Cherenkov detectors Large detector systems for particle and astroparticle physics Neutrino detectors Performance of High Energy Physics Detectors |
description |
The authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), LabEx UnivEarthS (ANR-10-LABX-0023 and ANR-18-IDEX-0001), Paris Ile-de-France Region, France; Shota Rustaveli National Science Foundation of Georgia (SRNSFG, FR-18-1268), Georgia; Deutsche Forschungsgemeinschaft (DFG), Germany; The General Secretariat of Research and Technology (GSRT), Greece; Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Universita e della Ricerca (MUR), PRIN 2017 program (Grant NAT-NET 2017W4HA7S) Italy; Ministry of Higher Education, Scientific Research and Professional Training, Morocco; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; The National Science Centre, Poland (2015/18/E/ST2/00758); National Authority for Scientific Research (ANCS), Romania; Ministerio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento (refs. PGC2018-096663-B-C41, -A-C42, -B-C43, -B-C44) (MCIU/FEDER), Severo Ochoa Centre of Excellence and MultiDark Consolider (MCIU), Junta de Andalucia (ref. SOMM17/6104/UGR), Generalitat Valenciana: Grisolia (ref. GRISOLIA/2018/119) and GenT (ref. CIDEGENT/2018/034) programs, La Caixa Foundation (ref. LCF/BQ/IN17/11620019), EU: MSC program (ref. 713673), Spain. The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches. French National Research Agency (ANR) ANR-15-CE31-0020 Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund) European Union (EU) Institut Universitaire de France (IUF) LabEx UnivEarthS ANR-10-LABX-0023 ANR-18-IDEX-0001 Shota Rustaveli National Science Foundation of Georgia FR-18-1268 German Research Foundation (DFG) Greek Ministry of Development-GSRT Istituto Nazionale di Fisica Nucleare (INFN) Ministry of Education, Universities and Research (MIUR) Research Projects of National Relevance (PRIN) Ministry of Higher Education, Scientific Research and Professional Training, Morocco Netherlands Organization for Scientific Research (NWO) National Science Centre, Poland 2015/18/E/ST2/00758 National Authority for Scientific Research (ANCS), Romania Ministerio de Ciencia, Innovacion, Investigacion y Universidades PGC2018-096663-B-C41 A-C42 B-C43 B-C44 Severo Ochoa Centre of Excellence Junta de Andalucia SOMM17/6104/UGR Generalitat Valenciana: Grisolia GRISOLIA/2018/119 CIDEGENT/2018/034 La Caixa Foundation LCF/BQ/IN17/11620019 EU: MSC program 713673 |
format |
Article in Journal/Newspaper |
author |
Aiello, S. Anguita López, Mancia Díaz García, Antonio Francisco López Coto, Daniel Navas Concha, Sergio Tenllado, Enrique |
author_facet |
Aiello, S. Anguita López, Mancia Díaz García, Antonio Francisco López Coto, Daniel Navas Concha, Sergio Tenllado, Enrique |
author_sort |
Aiello, S. |
title |
Event reconstruction for KM3NeT/ORCA using convolutional neural networks |
title_short |
Event reconstruction for KM3NeT/ORCA using convolutional neural networks |
title_full |
Event reconstruction for KM3NeT/ORCA using convolutional neural networks |
title_fullStr |
Event reconstruction for KM3NeT/ORCA using convolutional neural networks |
title_full_unstemmed |
Event reconstruction for KM3NeT/ORCA using convolutional neural networks |
title_sort |
event reconstruction for km3net/orca using convolutional neural networks |
publisher |
IOP Publishing |
publishDate |
2020 |
url |
http://hdl.handle.net/10481/64400 https://doi.org/10.1088/1748-0221/15/10/P10005 |
genre |
Orca |
genre_facet |
Orca |
op_relation |
Aiello, S. et. al. Event reconstruction for KM3NeT/ORCA using convolutional neural networks. 2020 JINST 15 P10005 [https://doi.org/10.1088/1748-0221/15/10/P10005] http://hdl.handle.net/10481/64400 doi:10.1088/1748-0221/15/10/P10005 |
op_rights |
Atribución 3.0 España http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1088/1748-0221/15/10/P10005 |
container_title |
Journal of Instrumentation |
container_volume |
15 |
container_issue |
10 |
container_start_page |
P10005 |
op_container_end_page |
P10005 |
_version_ |
1766161245510041600 |
spelling |
ftunivgranada:oai:digibug.ugr.es:10481/64400 2023-05-15T17:53:32+02:00 Event reconstruction for KM3NeT/ORCA using convolutional neural networks Aiello, S. Anguita López, Mancia Díaz García, Antonio Francisco López Coto, Daniel Navas Concha, Sergio Tenllado, Enrique 2020-10-08 http://hdl.handle.net/10481/64400 https://doi.org/10.1088/1748-0221/15/10/P10005 eng eng IOP Publishing Aiello, S. et. al. Event reconstruction for KM3NeT/ORCA using convolutional neural networks. 2020 JINST 15 P10005 [https://doi.org/10.1088/1748-0221/15/10/P10005] http://hdl.handle.net/10481/64400 doi:10.1088/1748-0221/15/10/P10005 Atribución 3.0 España http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess CC-BY Cherenkov detectors Large detector systems for particle and astroparticle physics Neutrino detectors Performance of High Energy Physics Detectors info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2020 ftunivgranada https://doi.org/10.1088/1748-0221/15/10/P10005 2021-03-17T00:20:20Z The authors acknowledge the financial support of the funding agencies: Agence Nationale de la Recherche (contract ANR-15-CE31-0020), Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund and Marie Curie Program), Institut Universitaire de France (IUF), LabEx UnivEarthS (ANR-10-LABX-0023 and ANR-18-IDEX-0001), Paris Ile-de-France Region, France; Shota Rustaveli National Science Foundation of Georgia (SRNSFG, FR-18-1268), Georgia; Deutsche Forschungsgemeinschaft (DFG), Germany; The General Secretariat of Research and Technology (GSRT), Greece; Istituto Nazionale di Fisica Nucleare (INFN), Ministero dell'Universita e della Ricerca (MUR), PRIN 2017 program (Grant NAT-NET 2017W4HA7S) Italy; Ministry of Higher Education, Scientific Research and Professional Training, Morocco; Nederlandse organisatie voor Wetenschappelijk Onderzoek (NWO), the Netherlands; The National Science Centre, Poland (2015/18/E/ST2/00758); National Authority for Scientific Research (ANCS), Romania; Ministerio de Ciencia, Innovacion, Investigacion y Universidades (MCIU): Programa Estatal de Generacion de Conocimiento (refs. PGC2018-096663-B-C41, -A-C42, -B-C43, -B-C44) (MCIU/FEDER), Severo Ochoa Centre of Excellence and MultiDark Consolider (MCIU), Junta de Andalucia (ref. SOMM17/6104/UGR), Generalitat Valenciana: Grisolia (ref. GRISOLIA/2018/119) and GenT (ref. CIDEGENT/2018/034) programs, La Caixa Foundation (ref. LCF/BQ/IN17/11620019), EU: MSC program (ref. 713673), Spain. The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches. French National Research Agency (ANR) ANR-15-CE31-0020 Centre National de la Recherche Scientifique (CNRS), Commission Europeenne (FEDER fund) European Union (EU) Institut Universitaire de France (IUF) LabEx UnivEarthS ANR-10-LABX-0023 ANR-18-IDEX-0001 Shota Rustaveli National Science Foundation of Georgia FR-18-1268 German Research Foundation (DFG) Greek Ministry of Development-GSRT Istituto Nazionale di Fisica Nucleare (INFN) Ministry of Education, Universities and Research (MIUR) Research Projects of National Relevance (PRIN) Ministry of Higher Education, Scientific Research and Professional Training, Morocco Netherlands Organization for Scientific Research (NWO) National Science Centre, Poland 2015/18/E/ST2/00758 National Authority for Scientific Research (ANCS), Romania Ministerio de Ciencia, Innovacion, Investigacion y Universidades PGC2018-096663-B-C41 A-C42 B-C43 B-C44 Severo Ochoa Centre of Excellence Junta de Andalucia SOMM17/6104/UGR Generalitat Valenciana: Grisolia GRISOLIA/2018/119 CIDEGENT/2018/034 La Caixa Foundation LCF/BQ/IN17/11620019 EU: MSC program 713673 Article in Journal/Newspaper Orca DIGIBUG: Repositorio Institucional de la Universidad de Granada Journal of Instrumentation 15 10 P10005 P10005 |