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...
Published in: | Journal of Instrumentation |
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Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Other Authors: | , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2020
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Subjects: | |
Online Access: | http://hdl.handle.net/11585/774385 https://doi.org/10.1088/1748-0221/15/10/P10005 https://iopscience.iop.org/article/10.1088/1748-0221/15/10/P10005 |
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ftunibolognairis:oai:cris.unibo.it:11585/774385 |
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record_format |
openpolar |
institution |
Open Polar |
collection |
IRIS Università degli Studi di Bologna (CRIS - Current Research Information System) |
op_collection_id |
ftunibolognairis |
language |
English |
topic |
Cherenkov detector Large detector systems for particle and astroparticle physic Neutrino detector Performance of High Energy Physics Detectors |
spellingShingle |
Cherenkov detector Large detector systems for particle and astroparticle physic Neutrino detector Performance of High Energy Physics Detectors Aiello, S. Albert, A. Garre, S. Alves Aly, Z. Ameli, F. Andre, M. Androulakis, G. Anghinolfi, M. Anguita, M. Anton, G. Ardid, M. Aublin, J. Bagatelas, C. Barbarino, G. Baret, B. Pree, S. Basegmez du Bendahman, M. Berbee, E. van den Berg, A. M. Bertin, V. Biagi, S. Biagioni, A. Bissinger, M. Boettcher, M. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Brânzaş, H. Bruijn, R. Brunner, J. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Capone, A. Carretero, V. Castaldi, P. Celli, S. Chabab, M. Chau, N. Chen, A. Cherubini, S. Chiarella, V. Chiarusi, T. Circella, M. Cocimano, R. Coelho, J. A. B. Coleiro, A. Molla, M. Colomer Coniglione, R. Coyle, P. Creusot, A. Cuttone, G. D'Onofrio, A. Dallier, R. Palma, M. De Palma, I. Di Díaz, A. F. Diego-Tortosa, D. Distefano, C. Domi, A. Donà, R. Donzaud, C. Dornic, D. Dörr, M. Drouhin, D. Eberl, T. Eddyamoui, A. Eeden, T. van Eijk, D. van Bojaddaini, I. El Elsaesser, D. Enzenhöfer, A. Roselló, V. Espinosa Fermani, P. Ferrara, G. Filipović, M. D. Filippini, F. Fusco, L. A. Gabella, O. Gal, T. Soto, A. Garcia Garufi, F. Gatelet, Y. Geißelbrecht, N. Gialanella, L. Giorgio, E. Gozzini, S. R. Gracia, R. Graf, K. Grasso, D. Grella, G. Guderian, D. Guidi, C. Hallmann, S. Hamdaoui, H. Haren, H. van Heijboer, A. Hekalo, A. Hernández-Rey, J. J. Hofestädt, J. Huang, F. Ibnsalih, W. Idrissi Illuminati, G. James, C. W. de Jong, M. de Jong, P. Jung, B. J. Kadler, M. Kalaczyński, P. Kalekin, O. Katz, U. F. Chowdhury, N. R Khan Kistauri, G. Knaap, F. van der Koffeman, E. N. Kooijman, P. Kouchner, A. Kreter, M. Kulikovskiy, V. Lahmann, R. Larosa, G. Breton, R. Le Leonardi, O. Leone, F. Leonora, E. Levi, G. Lincetto, M. Clark, M. Lindsey Lipreau, T. Lonardo, A. Longhitano, F. Lopez-Coto, D. Maderer, L. Mańczak, J. Mannheim, K. Margiotta, A. Marinelli, A. Markou, C. Martin, L. Martínez-Mora, J. A. Martini, A. Marzaioli, F. Mastroianni, S. Mazzou, S. Melis, K. W. Miele, G. Migliozzi, P. Migneco, E. Mijakowski, P. Miranda, L. S. Mollo, C. M. Morganti, M. Moser, M. Moussa, A. Muller, R. Musumeci, M. Nauta, L. Navas, S. Nicolau, C. A. Ó Fearraigh, B. Organokov, M. Orlando, A. Papalashvili, G. Papaleo, R. Pastore, C. Păun, A. M. Păvălaş, G. E. Pellegrino, C. Perrin-Terrin, M. Piattelli, P. Pieterse, C. Pikounis, K. Pisanti, O. Poirè, C. Popa, V. Post, M. Pradier, T. Pühlhofer, G. Pulvirenti, S. Rabyang, O. Raffaelli, F. Randazzo, N. Rapicavoli, A. Razzaque, S. Real, D. Reck, S. Riccobene, G. Richer, M. Rivoire, S. Rovelli, A. Greus, F. Salesa Samtleben, D. F. E. Sánchez Losa, A. Sanguineti, M. Santangelo, A. Santonocito, D. Sapienza, P. Schnabel, J. Seneca, J. Sgura, I. Shanidze, R. Sharma, A. Simeone, F. Sinopoulou, A. Spisso, B. Spurio, M. Stavropoulos, D. Steijger, J. Stellacci, S. M. Taiuti, M. Tayalati, Y. Tenllado, E. Thakore, T. Tingay, S. Tzamariudaki, E. Tzanetatos, D. Elewyck, V. Van Vannoye, G. Vasileiadis, G. Versari, F. Viola, S. Vivolo, D. de Wasseige, G. Wilms, J. Wojaczyński, R. de Wolf, E. Zaborov, D. Zavatarelli, S. Zegarelli, A. Zito, D. Zornoza, J. D. Zúñiga, J. Zywucka, N. Event reconstruction for KM3NeT/ORCA using convolutional neural networks |
topic_facet |
Cherenkov detector Large detector systems for particle and astroparticle physic Neutrino detector Performance of High Energy Physics Detectors |
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. |
author2 |
Aiello, S. Albert, A. Garre, S. Alve Aly, Z. Ameli, F. Andre, M. Androulakis, G. Anghinolfi, M. Anguita, M. Anton, G. Ardid, M. Aublin, J. Bagatelas, C. Barbarino, G. Baret, B. Pree, S. Basegmez du Bendahman, M. Berbee, E. van den Berg, A.M. Bertin, V. Biagi, S. Biagioni, A. Bissinger, M. Boettcher, M. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Brânzaş, H. Bruijn, R. Brunner, J. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Capone, A. Carretero, V. Castaldi, P. Celli, S. Chabab, M. Chau, N. Chen, A. Cherubini, S. Chiarella, V. Chiarusi, T. Circella, M. Cocimano, R. Coelho, J.A.B. Coleiro, A. |
format |
Article in Journal/Newspaper |
author |
Aiello, S. Albert, A. Garre, S. Alves Aly, Z. Ameli, F. Andre, M. Androulakis, G. Anghinolfi, M. Anguita, M. Anton, G. Ardid, M. Aublin, J. Bagatelas, C. Barbarino, G. Baret, B. Pree, S. Basegmez du Bendahman, M. Berbee, E. van den Berg, A. M. Bertin, V. Biagi, S. Biagioni, A. Bissinger, M. Boettcher, M. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Brânzaş, H. Bruijn, R. Brunner, J. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Capone, A. Carretero, V. Castaldi, P. Celli, S. Chabab, M. Chau, N. Chen, A. Cherubini, S. Chiarella, V. Chiarusi, T. Circella, M. Cocimano, R. Coelho, J. A. B. Coleiro, A. Molla, M. Colomer Coniglione, R. Coyle, P. Creusot, A. Cuttone, G. D'Onofrio, A. Dallier, R. Palma, M. De Palma, I. Di Díaz, A. F. Diego-Tortosa, D. Distefano, C. Domi, A. Donà, R. Donzaud, C. Dornic, D. Dörr, M. Drouhin, D. Eberl, T. Eddyamoui, A. Eeden, T. van Eijk, D. van Bojaddaini, I. El Elsaesser, D. Enzenhöfer, A. Roselló, V. Espinosa Fermani, P. Ferrara, G. Filipović, M. D. Filippini, F. Fusco, L. A. Gabella, O. Gal, T. Soto, A. Garcia Garufi, F. Gatelet, Y. Geißelbrecht, N. Gialanella, L. Giorgio, E. Gozzini, S. R. Gracia, R. Graf, K. Grasso, D. Grella, G. Guderian, D. Guidi, C. Hallmann, S. Hamdaoui, H. Haren, H. van Heijboer, A. Hekalo, A. Hernández-Rey, J. J. Hofestädt, J. Huang, F. Ibnsalih, W. Idrissi Illuminati, G. James, C. W. de Jong, M. de Jong, P. Jung, B. J. Kadler, M. Kalaczyński, P. Kalekin, O. Katz, U. F. Chowdhury, N. R Khan Kistauri, G. Knaap, F. van der Koffeman, E. N. Kooijman, P. Kouchner, A. Kreter, M. Kulikovskiy, V. Lahmann, R. Larosa, G. Breton, R. Le Leonardi, O. Leone, F. Leonora, E. Levi, G. Lincetto, M. Clark, M. Lindsey Lipreau, T. Lonardo, A. Longhitano, F. Lopez-Coto, D. Maderer, L. Mańczak, J. Mannheim, K. Margiotta, A. Marinelli, A. Markou, C. Martin, L. Martínez-Mora, J. A. Martini, A. Marzaioli, F. Mastroianni, S. Mazzou, S. Melis, K. W. Miele, G. Migliozzi, P. Migneco, E. Mijakowski, P. Miranda, L. S. Mollo, C. M. Morganti, M. Moser, M. Moussa, A. Muller, R. Musumeci, M. Nauta, L. Navas, S. Nicolau, C. A. Ó Fearraigh, B. Organokov, M. Orlando, A. Papalashvili, G. Papaleo, R. Pastore, C. Păun, A. M. Păvălaş, G. E. Pellegrino, C. Perrin-Terrin, M. Piattelli, P. Pieterse, C. Pikounis, K. Pisanti, O. Poirè, C. Popa, V. Post, M. Pradier, T. Pühlhofer, G. Pulvirenti, S. Rabyang, O. Raffaelli, F. Randazzo, N. Rapicavoli, A. Razzaque, S. Real, D. Reck, S. Riccobene, G. Richer, M. Rivoire, S. Rovelli, A. Greus, F. Salesa Samtleben, D. F. E. Sánchez Losa, A. Sanguineti, M. Santangelo, A. Santonocito, D. Sapienza, P. Schnabel, J. Seneca, J. Sgura, I. Shanidze, R. Sharma, A. Simeone, F. Sinopoulou, A. Spisso, B. Spurio, M. Stavropoulos, D. Steijger, J. Stellacci, S. M. Taiuti, M. Tayalati, Y. Tenllado, E. Thakore, T. Tingay, S. Tzamariudaki, E. Tzanetatos, D. Elewyck, V. Van Vannoye, G. Vasileiadis, G. Versari, F. Viola, S. Vivolo, D. de Wasseige, G. Wilms, J. Wojaczyński, R. de Wolf, E. Zaborov, D. Zavatarelli, S. Zegarelli, A. Zito, D. Zornoza, J. D. Zúñiga, J. Zywucka, N. |
author_facet |
Aiello, S. Albert, A. Garre, S. Alves Aly, Z. Ameli, F. Andre, M. Androulakis, G. Anghinolfi, M. Anguita, M. Anton, G. Ardid, M. Aublin, J. Bagatelas, C. Barbarino, G. Baret, B. Pree, S. Basegmez du Bendahman, M. Berbee, E. van den Berg, A. M. Bertin, V. Biagi, S. Biagioni, A. Bissinger, M. Boettcher, M. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Brânzaş, H. Bruijn, R. Brunner, J. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Capone, A. Carretero, V. Castaldi, P. Celli, S. Chabab, M. Chau, N. Chen, A. Cherubini, S. Chiarella, V. Chiarusi, T. Circella, M. Cocimano, R. Coelho, J. A. B. Coleiro, A. Molla, M. Colomer Coniglione, R. Coyle, P. Creusot, A. Cuttone, G. D'Onofrio, A. Dallier, R. Palma, M. De Palma, I. Di Díaz, A. F. Diego-Tortosa, D. Distefano, C. Domi, A. Donà, R. Donzaud, C. Dornic, D. Dörr, M. Drouhin, D. Eberl, T. Eddyamoui, A. Eeden, T. van Eijk, D. van Bojaddaini, I. El Elsaesser, D. Enzenhöfer, A. Roselló, V. Espinosa Fermani, P. Ferrara, G. Filipović, M. D. Filippini, F. Fusco, L. A. Gabella, O. Gal, T. Soto, A. Garcia Garufi, F. Gatelet, Y. Geißelbrecht, N. Gialanella, L. Giorgio, E. Gozzini, S. R. Gracia, R. Graf, K. Grasso, D. Grella, G. Guderian, D. Guidi, C. Hallmann, S. Hamdaoui, H. Haren, H. van Heijboer, A. Hekalo, A. Hernández-Rey, J. J. Hofestädt, J. Huang, F. Ibnsalih, W. Idrissi Illuminati, G. James, C. W. de Jong, M. de Jong, P. Jung, B. J. Kadler, M. Kalaczyński, P. Kalekin, O. Katz, U. F. Chowdhury, N. R Khan Kistauri, G. Knaap, F. van der Koffeman, E. N. Kooijman, P. Kouchner, A. Kreter, M. Kulikovskiy, V. Lahmann, R. Larosa, G. Breton, R. Le Leonardi, O. Leone, F. Leonora, E. Levi, G. Lincetto, M. Clark, M. Lindsey Lipreau, T. Lonardo, A. Longhitano, F. Lopez-Coto, D. Maderer, L. Mańczak, J. Mannheim, K. Margiotta, A. Marinelli, A. Markou, C. Martin, L. Martínez-Mora, J. A. Martini, A. Marzaioli, F. Mastroianni, S. Mazzou, S. Melis, K. W. Miele, G. Migliozzi, P. Migneco, E. Mijakowski, P. Miranda, L. S. Mollo, C. M. Morganti, M. Moser, M. Moussa, A. Muller, R. Musumeci, M. Nauta, L. Navas, S. Nicolau, C. A. Ó Fearraigh, B. Organokov, M. Orlando, A. Papalashvili, G. Papaleo, R. Pastore, C. Păun, A. M. Păvălaş, G. E. Pellegrino, C. Perrin-Terrin, M. Piattelli, P. Pieterse, C. Pikounis, K. Pisanti, O. Poirè, C. Popa, V. Post, M. Pradier, T. Pühlhofer, G. Pulvirenti, S. Rabyang, O. Raffaelli, F. Randazzo, N. Rapicavoli, A. Razzaque, S. Real, D. Reck, S. Riccobene, G. Richer, M. Rivoire, S. Rovelli, A. Greus, F. Salesa Samtleben, D. F. E. Sánchez Losa, A. Sanguineti, M. Santangelo, A. Santonocito, D. Sapienza, P. Schnabel, J. Seneca, J. Sgura, I. Shanidze, R. Sharma, A. Simeone, F. Sinopoulou, A. Spisso, B. Spurio, M. Stavropoulos, D. Steijger, J. Stellacci, S. M. Taiuti, M. Tayalati, Y. Tenllado, E. Thakore, T. Tingay, S. Tzamariudaki, E. Tzanetatos, D. Elewyck, V. Van Vannoye, G. Vasileiadis, G. Versari, F. Viola, S. Vivolo, D. de Wasseige, G. Wilms, J. Wojaczyński, R. de Wolf, E. Zaborov, D. Zavatarelli, S. Zegarelli, A. Zito, D. Zornoza, J. D. Zúñiga, J. Zywucka, N. |
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 |
publishDate |
2020 |
url |
http://hdl.handle.net/11585/774385 https://doi.org/10.1088/1748-0221/15/10/P10005 https://iopscience.iop.org/article/10.1088/1748-0221/15/10/P10005 |
genre |
Orca |
genre_facet |
Orca |
op_relation |
info:eu-repo/semantics/altIdentifier/wos/WOS:000577278000005 volume:15 issue:10 firstpage:1 lastpage:34 numberofpages:34 journal:JOURNAL OF INSTRUMENTATION http://hdl.handle.net/11585/774385 doi:10.1088/1748-0221/15/10/P10005 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85096774849 https://iopscience.iop.org/article/10.1088/1748-0221/15/10/P10005 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1088/1748-0221/15/10/P10005 |
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Journal of Instrumentation |
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P10005 |
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ftunibolognairis:oai:cris.unibo.it:11585/774385 2024-04-14T08:17:55+00:00 Event reconstruction for KM3NeT/ORCA using convolutional neural networks Aiello, S. Albert, A. Garre, S. Alves Aly, Z. Ameli, F. Andre, M. Androulakis, G. Anghinolfi, M. Anguita, M. Anton, G. Ardid, M. Aublin, J. Bagatelas, C. Barbarino, G. Baret, B. Pree, S. Basegmez du Bendahman, M. Berbee, E. van den Berg, A. M. Bertin, V. Biagi, S. Biagioni, A. Bissinger, M. Boettcher, M. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Brânzaş, H. Bruijn, R. Brunner, J. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Capone, A. Carretero, V. Castaldi, P. Celli, S. Chabab, M. Chau, N. Chen, A. Cherubini, S. Chiarella, V. Chiarusi, T. Circella, M. Cocimano, R. Coelho, J. A. B. Coleiro, A. Molla, M. Colomer Coniglione, R. Coyle, P. Creusot, A. Cuttone, G. D'Onofrio, A. Dallier, R. Palma, M. De Palma, I. Di Díaz, A. F. Diego-Tortosa, D. Distefano, C. Domi, A. Donà, R. Donzaud, C. Dornic, D. Dörr, M. Drouhin, D. Eberl, T. Eddyamoui, A. Eeden, T. van Eijk, D. van Bojaddaini, I. El Elsaesser, D. Enzenhöfer, A. Roselló, V. Espinosa Fermani, P. Ferrara, G. Filipović, M. D. Filippini, F. Fusco, L. A. Gabella, O. Gal, T. Soto, A. Garcia Garufi, F. Gatelet, Y. Geißelbrecht, N. Gialanella, L. Giorgio, E. Gozzini, S. R. Gracia, R. Graf, K. Grasso, D. Grella, G. Guderian, D. Guidi, C. Hallmann, S. Hamdaoui, H. Haren, H. van Heijboer, A. Hekalo, A. Hernández-Rey, J. J. Hofestädt, J. Huang, F. Ibnsalih, W. Idrissi Illuminati, G. James, C. W. de Jong, M. de Jong, P. Jung, B. J. Kadler, M. Kalaczyński, P. Kalekin, O. Katz, U. F. Chowdhury, N. R Khan Kistauri, G. Knaap, F. van der Koffeman, E. N. Kooijman, P. Kouchner, A. Kreter, M. Kulikovskiy, V. Lahmann, R. Larosa, G. Breton, R. Le Leonardi, O. Leone, F. Leonora, E. Levi, G. Lincetto, M. Clark, M. Lindsey Lipreau, T. Lonardo, A. Longhitano, F. Lopez-Coto, D. Maderer, L. Mańczak, J. Mannheim, K. Margiotta, A. Marinelli, A. Markou, C. Martin, L. Martínez-Mora, J. A. Martini, A. Marzaioli, F. Mastroianni, S. Mazzou, S. Melis, K. W. Miele, G. Migliozzi, P. Migneco, E. Mijakowski, P. Miranda, L. S. Mollo, C. M. Morganti, M. Moser, M. Moussa, A. Muller, R. Musumeci, M. Nauta, L. Navas, S. Nicolau, C. A. Ó Fearraigh, B. Organokov, M. Orlando, A. Papalashvili, G. Papaleo, R. Pastore, C. Păun, A. M. Păvălaş, G. E. Pellegrino, C. Perrin-Terrin, M. Piattelli, P. Pieterse, C. Pikounis, K. Pisanti, O. Poirè, C. Popa, V. Post, M. Pradier, T. Pühlhofer, G. Pulvirenti, S. Rabyang, O. Raffaelli, F. Randazzo, N. Rapicavoli, A. Razzaque, S. Real, D. Reck, S. Riccobene, G. Richer, M. Rivoire, S. Rovelli, A. Greus, F. Salesa Samtleben, D. F. E. Sánchez Losa, A. Sanguineti, M. Santangelo, A. Santonocito, D. Sapienza, P. Schnabel, J. Seneca, J. Sgura, I. Shanidze, R. Sharma, A. Simeone, F. Sinopoulou, A. Spisso, B. Spurio, M. Stavropoulos, D. Steijger, J. Stellacci, S. M. Taiuti, M. Tayalati, Y. Tenllado, E. Thakore, T. Tingay, S. Tzamariudaki, E. Tzanetatos, D. Elewyck, V. Van Vannoye, G. Vasileiadis, G. Versari, F. Viola, S. Vivolo, D. de Wasseige, G. Wilms, J. Wojaczyński, R. de Wolf, E. Zaborov, D. Zavatarelli, S. Zegarelli, A. Zito, D. Zornoza, J. D. Zúñiga, J. Zywucka, N. Aiello, S. Albert, A. Garre, S. Alve Aly, Z. Ameli, F. Andre, M. Androulakis, G. Anghinolfi, M. Anguita, M. Anton, G. Ardid, M. Aublin, J. Bagatelas, C. Barbarino, G. Baret, B. Pree, S. Basegmez du Bendahman, M. Berbee, E. van den Berg, A.M. Bertin, V. Biagi, S. Biagioni, A. Bissinger, M. Boettcher, M. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Brânzaş, H. Bruijn, R. Brunner, J. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Capone, A. Carretero, V. Castaldi, P. Celli, S. Chabab, M. Chau, N. Chen, A. Cherubini, S. Chiarella, V. Chiarusi, T. Circella, M. Cocimano, R. Coelho, J.A.B. Coleiro, A. 2020 STAMPA http://hdl.handle.net/11585/774385 https://doi.org/10.1088/1748-0221/15/10/P10005 https://iopscience.iop.org/article/10.1088/1748-0221/15/10/P10005 eng eng info:eu-repo/semantics/altIdentifier/wos/WOS:000577278000005 volume:15 issue:10 firstpage:1 lastpage:34 numberofpages:34 journal:JOURNAL OF INSTRUMENTATION http://hdl.handle.net/11585/774385 doi:10.1088/1748-0221/15/10/P10005 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85096774849 https://iopscience.iop.org/article/10.1088/1748-0221/15/10/P10005 info:eu-repo/semantics/openAccess Cherenkov detector Large detector systems for particle and astroparticle physic Neutrino detector Performance of High Energy Physics Detectors info:eu-repo/semantics/article 2020 ftunibolognairis https://doi.org/10.1088/1748-0221/15/10/P10005 2024-03-21T18:28:27Z 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 IRIS Università degli Studi di Bologna (CRIS - Current Research Information System) Journal of Instrumentation 15 10 P10005 P10005 |