Graph Neural Networks for low-energy event classification & reconstruction in IceCube
IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a c...
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eScholarship, University of California
2022
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Online Access: | https://escholarship.org/uc/item/357268r5 |
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ftcdlib:oai:escholarship.org:ark:/13030/qt357268r5 2024-01-21T10:07:08+01:00 Graph Neural Networks for low-energy event classification & reconstruction in IceCube Abbasi, R Ackermann, M Adams, J Aggarwal, N Aguilar, JA Ahlers, M Ahrens, M Alameddine, JM Alves, AA Amin, NM Andeen, K Anderson, T Anton, G Argüelles, C Ashida, Y Athanasiadou, S Axani, S Bai, X V., A Balagopal Baricevic, M Barwick, SW Basu, V Bay, R Beatty, JJ Becker, K-H Tjus, J Becker Beise, J Bellenghi, C Benda, S BenZvi, S Berley, D Bernardini, E Besson, DZ Binder, G Bindig, D Blaufuss, E Blot, S Bontempo, F Book, JY Borowka, J Meneguolo, C Boscolo Böser, S Botner, O Böttcher, J Bourbeau, E Braun, J Brinson, B Brostean-Kaiser, J Burley, RT Busse, RS Campana, MA Carnie-Bronca, EG Chen, C Chen, Z Chirkin, D Choi, K Clark, BA Classen, L Coleman, A Collin, GH Connolly, A Conrad, JM Coppin, P Correa, P Countryman, S Cowen, DF Cross, R Dappen, C Dave, P De Clercq, C DeLaunay, JJ López, D Delgado Dembinski, H Deoskar, K Desai, A Desiati, P de Vries, KD de Wasseige, G DeYoung, T Diaz, A Díaz-Vélez, JC Dittmer, M Dujmovic, H DuVernois, MA Ehrhardt, T Eller, P Engel, R Erpenbeck, H Evans, J Evenson, PA Fan, KL Fazely, AR Fedynitch, A Feigl, N Fiedlschuster, S Fienberg, AT Finley, C Fischer, L Fox, D Franckowiak, A p11003 2022-11-01 application/pdf https://escholarship.org/uc/item/357268r5 unknown eScholarship, University of California qt357268r5 https://escholarship.org/uc/item/357268r5 CC-BY Journal of Instrumentation, vol 17, iss 11 Nuclear and Plasma Physics Particle and High Energy Physics Physical Sciences Analysis and statistical methods Data analysis Neutrino detectors Particle identification methods Engineering Nuclear & Particles Physics article 2022 ftcdlib 2023-12-25T19:05:44Z IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challenge due to the irregular detector geometry, inhomogeneous scattering and absorption of light in the ice and, below 100 GeV, the relatively low number of signal photons produced per event. To address this challenge, it is possible to represent IceCube events as point cloud graphs and use a Graph Neural Network (GNN) as the classification and reconstruction method. The GNN is capable of distinguishing neutrino events from cosmic-ray backgrounds, classifying different neutrino event types, and reconstructing the deposited energy, direction and interaction vertex. Based on simulation, we provide a comparison in the 1 GeV-100 GeV energy range to the current state-of-the-art maximum likelihood techniques used in current IceCube analyses, including the effects of known systematic uncertainties. For neutrino event classification, the GNN increases the signal efficiency by 18% at a fixed background rate, compared to current IceCube methods. Alternatively, the GNN offers a reduction of the background (i.e. false positive) rate by over a factor 8 (to below half a percent) at a fixed signal efficiency. For the reconstruction of energy, direction, and interaction vertex, the resolution improves by an average of 13%-20% compared to current maximum likelihood techniques in the energy range of 1 GeV-30 GeV. The GNN, when run on a GPU, is capable of processing IceCube events at a rate nearly double of the median IceCube trigger rate of 2.7 kHz, which opens the possibility of using low energy neutrinos in online searches for transient events. Article in Journal/Newspaper Ice Sheet South pole University of California: eScholarship South Pole |
institution |
Open Polar |
collection |
University of California: eScholarship |
op_collection_id |
ftcdlib |
language |
unknown |
topic |
Nuclear and Plasma Physics Particle and High Energy Physics Physical Sciences Analysis and statistical methods Data analysis Neutrino detectors Particle identification methods Engineering Nuclear & Particles Physics |
spellingShingle |
Nuclear and Plasma Physics Particle and High Energy Physics Physical Sciences Analysis and statistical methods Data analysis Neutrino detectors Particle identification methods Engineering Nuclear & Particles Physics Abbasi, R Ackermann, M Adams, J Aggarwal, N Aguilar, JA Ahlers, M Ahrens, M Alameddine, JM Alves, AA Amin, NM Andeen, K Anderson, T Anton, G Argüelles, C Ashida, Y Athanasiadou, S Axani, S Bai, X V., A Balagopal Baricevic, M Barwick, SW Basu, V Bay, R Beatty, JJ Becker, K-H Tjus, J Becker Beise, J Bellenghi, C Benda, S BenZvi, S Berley, D Bernardini, E Besson, DZ Binder, G Bindig, D Blaufuss, E Blot, S Bontempo, F Book, JY Borowka, J Meneguolo, C Boscolo Böser, S Botner, O Böttcher, J Bourbeau, E Braun, J Brinson, B Brostean-Kaiser, J Burley, RT Busse, RS Campana, MA Carnie-Bronca, EG Chen, C Chen, Z Chirkin, D Choi, K Clark, BA Classen, L Coleman, A Collin, GH Connolly, A Conrad, JM Coppin, P Correa, P Countryman, S Cowen, DF Cross, R Dappen, C Dave, P De Clercq, C DeLaunay, JJ López, D Delgado Dembinski, H Deoskar, K Desai, A Desiati, P de Vries, KD de Wasseige, G DeYoung, T Diaz, A Díaz-Vélez, JC Dittmer, M Dujmovic, H DuVernois, MA Ehrhardt, T Eller, P Engel, R Erpenbeck, H Evans, J Evenson, PA Fan, KL Fazely, AR Fedynitch, A Feigl, N Fiedlschuster, S Fienberg, AT Finley, C Fischer, L Fox, D Franckowiak, A Graph Neural Networks for low-energy event classification & reconstruction in IceCube |
topic_facet |
Nuclear and Plasma Physics Particle and High Energy Physics Physical Sciences Analysis and statistical methods Data analysis Neutrino detectors Particle identification methods Engineering Nuclear & Particles Physics |
description |
IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challenge due to the irregular detector geometry, inhomogeneous scattering and absorption of light in the ice and, below 100 GeV, the relatively low number of signal photons produced per event. To address this challenge, it is possible to represent IceCube events as point cloud graphs and use a Graph Neural Network (GNN) as the classification and reconstruction method. The GNN is capable of distinguishing neutrino events from cosmic-ray backgrounds, classifying different neutrino event types, and reconstructing the deposited energy, direction and interaction vertex. Based on simulation, we provide a comparison in the 1 GeV-100 GeV energy range to the current state-of-the-art maximum likelihood techniques used in current IceCube analyses, including the effects of known systematic uncertainties. For neutrino event classification, the GNN increases the signal efficiency by 18% at a fixed background rate, compared to current IceCube methods. Alternatively, the GNN offers a reduction of the background (i.e. false positive) rate by over a factor 8 (to below half a percent) at a fixed signal efficiency. For the reconstruction of energy, direction, and interaction vertex, the resolution improves by an average of 13%-20% compared to current maximum likelihood techniques in the energy range of 1 GeV-30 GeV. The GNN, when run on a GPU, is capable of processing IceCube events at a rate nearly double of the median IceCube trigger rate of 2.7 kHz, which opens the possibility of using low energy neutrinos in online searches for transient events. |
format |
Article in Journal/Newspaper |
author |
Abbasi, R Ackermann, M Adams, J Aggarwal, N Aguilar, JA Ahlers, M Ahrens, M Alameddine, JM Alves, AA Amin, NM Andeen, K Anderson, T Anton, G Argüelles, C Ashida, Y Athanasiadou, S Axani, S Bai, X V., A Balagopal Baricevic, M Barwick, SW Basu, V Bay, R Beatty, JJ Becker, K-H Tjus, J Becker Beise, J Bellenghi, C Benda, S BenZvi, S Berley, D Bernardini, E Besson, DZ Binder, G Bindig, D Blaufuss, E Blot, S Bontempo, F Book, JY Borowka, J Meneguolo, C Boscolo Böser, S Botner, O Böttcher, J Bourbeau, E Braun, J Brinson, B Brostean-Kaiser, J Burley, RT Busse, RS Campana, MA Carnie-Bronca, EG Chen, C Chen, Z Chirkin, D Choi, K Clark, BA Classen, L Coleman, A Collin, GH Connolly, A Conrad, JM Coppin, P Correa, P Countryman, S Cowen, DF Cross, R Dappen, C Dave, P De Clercq, C DeLaunay, JJ López, D Delgado Dembinski, H Deoskar, K Desai, A Desiati, P de Vries, KD de Wasseige, G DeYoung, T Diaz, A Díaz-Vélez, JC Dittmer, M Dujmovic, H DuVernois, MA Ehrhardt, T Eller, P Engel, R Erpenbeck, H Evans, J Evenson, PA Fan, KL Fazely, AR Fedynitch, A Feigl, N Fiedlschuster, S Fienberg, AT Finley, C Fischer, L Fox, D Franckowiak, A |
author_facet |
Abbasi, R Ackermann, M Adams, J Aggarwal, N Aguilar, JA Ahlers, M Ahrens, M Alameddine, JM Alves, AA Amin, NM Andeen, K Anderson, T Anton, G Argüelles, C Ashida, Y Athanasiadou, S Axani, S Bai, X V., A Balagopal Baricevic, M Barwick, SW Basu, V Bay, R Beatty, JJ Becker, K-H Tjus, J Becker Beise, J Bellenghi, C Benda, S BenZvi, S Berley, D Bernardini, E Besson, DZ Binder, G Bindig, D Blaufuss, E Blot, S Bontempo, F Book, JY Borowka, J Meneguolo, C Boscolo Böser, S Botner, O Böttcher, J Bourbeau, E Braun, J Brinson, B Brostean-Kaiser, J Burley, RT Busse, RS Campana, MA Carnie-Bronca, EG Chen, C Chen, Z Chirkin, D Choi, K Clark, BA Classen, L Coleman, A Collin, GH Connolly, A Conrad, JM Coppin, P Correa, P Countryman, S Cowen, DF Cross, R Dappen, C Dave, P De Clercq, C DeLaunay, JJ López, D Delgado Dembinski, H Deoskar, K Desai, A Desiati, P de Vries, KD de Wasseige, G DeYoung, T Diaz, A Díaz-Vélez, JC Dittmer, M Dujmovic, H DuVernois, MA Ehrhardt, T Eller, P Engel, R Erpenbeck, H Evans, J Evenson, PA Fan, KL Fazely, AR Fedynitch, A Feigl, N Fiedlschuster, S Fienberg, AT Finley, C Fischer, L Fox, D Franckowiak, A |
author_sort |
Abbasi, R |
title |
Graph Neural Networks for low-energy event classification & reconstruction in IceCube |
title_short |
Graph Neural Networks for low-energy event classification & reconstruction in IceCube |
title_full |
Graph Neural Networks for low-energy event classification & reconstruction in IceCube |
title_fullStr |
Graph Neural Networks for low-energy event classification & reconstruction in IceCube |
title_full_unstemmed |
Graph Neural Networks for low-energy event classification & reconstruction in IceCube |
title_sort |
graph neural networks for low-energy event classification & reconstruction in icecube |
publisher |
eScholarship, University of California |
publishDate |
2022 |
url |
https://escholarship.org/uc/item/357268r5 |
op_coverage |
p11003 |
geographic |
South Pole |
geographic_facet |
South Pole |
genre |
Ice Sheet South pole |
genre_facet |
Ice Sheet South pole |
op_source |
Journal of Instrumentation, vol 17, iss 11 |
op_relation |
qt357268r5 https://escholarship.org/uc/item/357268r5 |
op_rights |
CC-BY |
_version_ |
1788697662602608640 |