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: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Format: | Article in Journal/Newspaper |
Language: | English |
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2020
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Subjects: | |
Online Access: | http://hdl.handle.net/11386/4751310 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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ftunisalernoiris:oai:www.iris.unisa.it:11386/4751310 |
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record_format |
openpolar |
institution |
Open Polar |
collection |
EleA@Unisa (Università degli Studi di Salerno) |
op_collection_id |
ftunisalernoiris |
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 appli- cability 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 classifica- tion and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simu- lated 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/11386/4751310 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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ENVELOPE(23.767,23.767,67.383,67.383) |
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Simu |
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Orca |
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Orca |
op_relation |
info:eu-repo/semantics/altIdentifier/wos/WOS:000577278000005 volume:15 firstpage:P10005 lastpage:P10005 numberofpages:39 journal:JOURNAL OF INSTRUMENTATION http://hdl.handle.net/11386/4751310 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_doi |
https://doi.org/10.1088/1748-0221/15/10/P10005 |
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ftunisalernoiris:oai:www.iris.unisa.it:11386/4751310 2024-02-11T10:07:40+01:00 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 http://hdl.handle.net/11386/4751310 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 firstpage:P10005 lastpage:P10005 numberofpages:39 journal:JOURNAL OF INSTRUMENTATION http://hdl.handle.net/11386/4751310 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 Cherenkov detector Large detector systems for particle and astroparticle physic Neutrino detector Performance of High Energy Physics Detectors info:eu-repo/semantics/article 2020 ftunisalernoiris https://doi.org/10.1088/1748-0221/15/10/P10005 2024-01-17T17:44:11Z 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 appli- cability 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 classifica- tion and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simu- lated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches. Article in Journal/Newspaper Orca EleA@Unisa (Università degli Studi di Salerno) Simu ENVELOPE(23.767,23.767,67.383,67.383) Journal of Instrumentation 15 10 P10005 P10005 |