Event reconstruction for KM3NeT/ORCA using convolutional neural networks
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...
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Online Access: | https://doi.org/10.1088/1748-0221/15/10/P10005 https://hdl.handle.net/1959.7/uws:58172 |
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ftunivwestsyd:oai:researchdirect.westernsydney.edu.au:uws_58172 2023-05-15T17:53:11+02:00 Event reconstruction for KM3NeT/ORCA using convolutional neural networks Aiello, Sebastiano Albert, Andrea M. Garre, S. Alves Aly, Z. Ameli, Fabrizio F. Andre, Michel Androulakis, Giorgos C. Anghinolfi, Marco Anguita, Mancia Anton, Gisela K. Ardid, Miguel Aublin, Julien Bagatelas, Christos Barbarino, G. C. Baret, Bruny Du Pree, Suzan B. Bendahman, Meriem Berbee, Edward M. van den Berg, Ad M. Bertin, Vincent Biagi, Simone Biagioni, Andrea Bissinger, Matthias Boettcher, M. Boumaaza, Jihad Bouta, Mohammed Bouwhuis, Mieke C. Bozza, Cristiano Branzas, Horea Bruijn, Ronald Brunner, Juergen Buis, Ernst J. Buompane, Raffaele Busto, Jose Caiffi, B. Calvo, David Capone, Antonio Carretero, V. Castaldi, P. Celli, Silvia Chabab, Mohamed Chau, N. Chen, A. Cherubine, S. Chiarella, V. Chiarusi, Tommaso Circella, Marco Cocimano, Rosanna Coelho, Joao A. Coleiro, Alexis Molla, M. Colomer Coniglione, Rosa Coyle, Paschal A. Creusot, Alexandre Cuttone, Giacomo D'Onofrio, Antonio Dallier, Richard De Palma, Mauro Di Palma, Irene Diaz, Antonio F. Diego-Tortosa, D. Distefano, Carla Domi, A. Dona, Roberto Donzaud, Corinne Dornic, Damien Dorr, M. Drouhin, D. Eberl, Thomas Eddyamoui, A. van Eeden, Thijs van Eijk, Daan El Bojaddaini, Imad Elsaesser, Dominik Enzenhofer, Alexander Rosello, V. Espinosa Fermani, Paolo Ferrara, Giovanna A. Filipovic, Miroslav (R7673) 2020 print 39 https://doi.org/10.1088/1748-0221/15/10/P10005 https://hdl.handle.net/1959.7/uws:58172 eng eng U.K., Institute of Physics Publishing Journal of Instrumentation--1748-0221-- Vol. 15 Issue. 10 No. P10005 pp: - © 2020 The Author(s). Published by IOP Publishing Ltd on behalf of Sissa Medialab. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (https://creativecommons.org/licenses/by/4.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. CC-BY XXXXXX - Unknown Cherenkov counters detectors performance convolutions (mathematics) neural networks (computer science) machine learning journal article 2020 ftunivwestsyd https://doi.org/10.1088/1748-0221/15/10/P10005 2020-12-14T23:24:27Z 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. Article in Journal/Newspaper Orca University of Western Sydney (UWS): Research Direct Journal of Instrumentation 15 10 P10005 P10005 |
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XXXXXX - Unknown Cherenkov counters detectors performance convolutions (mathematics) neural networks (computer science) machine learning |
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XXXXXX - Unknown Cherenkov counters detectors performance convolutions (mathematics) neural networks (computer science) machine learning Aiello, Sebastiano Albert, Andrea M. Garre, S. Alves Aly, Z. Ameli, Fabrizio F. Andre, Michel Androulakis, Giorgos C. Anghinolfi, Marco Anguita, Mancia Anton, Gisela K. Ardid, Miguel Aublin, Julien Bagatelas, Christos Barbarino, G. C. Baret, Bruny Du Pree, Suzan B. Bendahman, Meriem Berbee, Edward M. van den Berg, Ad M. Bertin, Vincent Biagi, Simone Biagioni, Andrea Bissinger, Matthias Boettcher, M. Boumaaza, Jihad Bouta, Mohammed Bouwhuis, Mieke C. Bozza, Cristiano Branzas, Horea Bruijn, Ronald Brunner, Juergen Buis, Ernst J. Buompane, Raffaele Busto, Jose Caiffi, B. Calvo, David Capone, Antonio Carretero, V. Castaldi, P. Celli, Silvia Chabab, Mohamed Chau, N. Chen, A. Cherubine, S. Chiarella, V. Chiarusi, Tommaso Circella, Marco Cocimano, Rosanna Coelho, Joao A. Coleiro, Alexis Molla, M. Colomer Coniglione, Rosa Coyle, Paschal A. Creusot, Alexandre Cuttone, Giacomo D'Onofrio, Antonio Dallier, Richard De Palma, Mauro Di Palma, Irene Diaz, Antonio F. Diego-Tortosa, D. Distefano, Carla Domi, A. Dona, Roberto Donzaud, Corinne Dornic, Damien Dorr, M. Drouhin, D. Eberl, Thomas Eddyamoui, A. van Eeden, Thijs van Eijk, Daan El Bojaddaini, Imad Elsaesser, Dominik Enzenhofer, Alexander Rosello, V. Espinosa Fermani, Paolo Ferrara, Giovanna A. Filipovic, Miroslav (R7673) Event reconstruction for KM3NeT/ORCA using convolutional neural networks |
topic_facet |
XXXXXX - Unknown Cherenkov counters detectors performance convolutions (mathematics) neural networks (computer science) machine learning |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Aiello, Sebastiano Albert, Andrea M. Garre, S. Alves Aly, Z. Ameli, Fabrizio F. Andre, Michel Androulakis, Giorgos C. Anghinolfi, Marco Anguita, Mancia Anton, Gisela K. Ardid, Miguel Aublin, Julien Bagatelas, Christos Barbarino, G. C. Baret, Bruny Du Pree, Suzan B. Bendahman, Meriem Berbee, Edward M. van den Berg, Ad M. Bertin, Vincent Biagi, Simone Biagioni, Andrea Bissinger, Matthias Boettcher, M. Boumaaza, Jihad Bouta, Mohammed Bouwhuis, Mieke C. Bozza, Cristiano Branzas, Horea Bruijn, Ronald Brunner, Juergen Buis, Ernst J. Buompane, Raffaele Busto, Jose Caiffi, B. Calvo, David Capone, Antonio Carretero, V. Castaldi, P. Celli, Silvia Chabab, Mohamed Chau, N. Chen, A. Cherubine, S. Chiarella, V. Chiarusi, Tommaso Circella, Marco Cocimano, Rosanna Coelho, Joao A. Coleiro, Alexis Molla, M. Colomer Coniglione, Rosa Coyle, Paschal A. Creusot, Alexandre Cuttone, Giacomo D'Onofrio, Antonio Dallier, Richard De Palma, Mauro Di Palma, Irene Diaz, Antonio F. Diego-Tortosa, D. Distefano, Carla Domi, A. Dona, Roberto Donzaud, Corinne Dornic, Damien Dorr, M. Drouhin, D. Eberl, Thomas Eddyamoui, A. van Eeden, Thijs van Eijk, Daan El Bojaddaini, Imad Elsaesser, Dominik Enzenhofer, Alexander Rosello, V. Espinosa Fermani, Paolo Ferrara, Giovanna A. Filipovic, Miroslav (R7673) |
author_facet |
Aiello, Sebastiano Albert, Andrea M. Garre, S. Alves Aly, Z. Ameli, Fabrizio F. Andre, Michel Androulakis, Giorgos C. Anghinolfi, Marco Anguita, Mancia Anton, Gisela K. Ardid, Miguel Aublin, Julien Bagatelas, Christos Barbarino, G. C. Baret, Bruny Du Pree, Suzan B. Bendahman, Meriem Berbee, Edward M. van den Berg, Ad M. Bertin, Vincent Biagi, Simone Biagioni, Andrea Bissinger, Matthias Boettcher, M. Boumaaza, Jihad Bouta, Mohammed Bouwhuis, Mieke C. Bozza, Cristiano Branzas, Horea Bruijn, Ronald Brunner, Juergen Buis, Ernst J. Buompane, Raffaele Busto, Jose Caiffi, B. Calvo, David Capone, Antonio Carretero, V. Castaldi, P. Celli, Silvia Chabab, Mohamed Chau, N. Chen, A. Cherubine, S. Chiarella, V. Chiarusi, Tommaso Circella, Marco Cocimano, Rosanna Coelho, Joao A. Coleiro, Alexis Molla, M. Colomer Coniglione, Rosa Coyle, Paschal A. Creusot, Alexandre Cuttone, Giacomo D'Onofrio, Antonio Dallier, Richard De Palma, Mauro Di Palma, Irene Diaz, Antonio F. Diego-Tortosa, D. Distefano, Carla Domi, A. Dona, Roberto Donzaud, Corinne Dornic, Damien Dorr, M. Drouhin, D. Eberl, Thomas Eddyamoui, A. van Eeden, Thijs van Eijk, Daan El Bojaddaini, Imad Elsaesser, Dominik Enzenhofer, Alexander Rosello, V. Espinosa Fermani, Paolo Ferrara, Giovanna A. Filipovic, Miroslav (R7673) |
author_sort |
Aiello, Sebastiano |
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 |
U.K., Institute of Physics Publishing |
publishDate |
2020 |
url |
https://doi.org/10.1088/1748-0221/15/10/P10005 https://hdl.handle.net/1959.7/uws:58172 |
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Orca |
genre_facet |
Orca |
op_relation |
Journal of Instrumentation--1748-0221-- Vol. 15 Issue. 10 No. P10005 pp: - |
op_rights |
© 2020 The Author(s). Published by IOP Publishing Ltd on behalf of Sissa Medialab. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (https://creativecommons.org/licenses/by/4.0/). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1088/1748-0221/15/10/P10005 |
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Journal of Instrumentation |
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15 |
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10 |
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P10005 |
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P10005 |
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1766160900708892672 |