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...
Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Format: | Text |
Language: | English |
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Humboldt-Universität zu Berlin
2022
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Subjects: | |
Online Access: | https://dx.doi.org/10.18452/27684 https://edoc.hu-berlin.de/handle/18452/28339 |
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author | Abbasi, R. Ackermann, M. Adams, J. Aggarwal, N. Aguilar, J.A. Ahlers, M. Ahrens, M. Alameddine, J.M. Alves, A.A. Amin, N.M. Andeen, K. Anderson, T. Anton, G. Argüelles, C. Ashida, Y. Athanasiadou, S. Axani, S. Bai, X. Balagopal V., A. Baricevic, M. Barwick, S.W. Basu, V. Bay, R. Beatty, J.J. Becker, K.-H. Becker Tjus, J. Beise, J. Bellenghi, C. Benda, S. BenZvi, S. Berley, D. Bernardini, E. Besson, D.Z. Binder, G. Bindig, D. Blaufuss, E. Blot, S. Bontempo, F. Book, J.Y. Borowka, J. Boscolo Meneguolo, C. Böser, S. Botner, O. Böttcher, J. Bourbeau, E. Braun, J. Brinson, B. Brostean-Kaiser, J. Burley, R.T. Busse, R.S. |
author_facet | Abbasi, R. Ackermann, M. Adams, J. Aggarwal, N. Aguilar, J.A. Ahlers, M. Ahrens, M. Alameddine, J.M. Alves, A.A. Amin, N.M. Andeen, K. Anderson, T. Anton, G. Argüelles, C. Ashida, Y. Athanasiadou, S. Axani, S. Bai, X. Balagopal V., A. Baricevic, M. Barwick, S.W. Basu, V. Bay, R. Beatty, J.J. Becker, K.-H. Becker Tjus, J. Beise, J. Bellenghi, C. Benda, S. BenZvi, S. Berley, D. Bernardini, E. Besson, D.Z. Binder, G. Bindig, D. Blaufuss, E. Blot, S. Bontempo, F. Book, J.Y. Borowka, J. Boscolo Meneguolo, C. Böser, S. Botner, O. Böttcher, J. Bourbeau, E. Braun, J. Brinson, B. Brostean-Kaiser, J. Burley, R.T. Busse, R.S. |
author_sort | Abbasi, R. |
collection | DataCite |
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 ... |
format | Text |
genre | Ice Sheet South pole |
genre_facet | Ice Sheet South pole |
geographic | South Pole |
geographic_facet | South Pole |
id | ftdatacite:10.18452/27684 |
institution | Open Polar |
language | English |
op_collection_id | ftdatacite |
op_doi | https://doi.org/10.18452/27684 |
op_rights | Creative Commons Attribution 4.0 International (CC BY 4.0) Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
publishDate | 2022 |
publisher | Humboldt-Universität zu Berlin |
record_format | openpolar |
spelling | ftdatacite:10.18452/27684 2025-01-16T22:26:36+00:00 Graph Neural Networks for low-energy event classification & reconstruction in IceCube ... Abbasi, R. Ackermann, M. Adams, J. Aggarwal, N. Aguilar, J.A. Ahlers, M. Ahrens, M. Alameddine, J.M. Alves, A.A. Amin, N.M. Andeen, K. Anderson, T. Anton, G. Argüelles, C. Ashida, Y. Athanasiadou, S. Axani, S. Bai, X. Balagopal V., A. Baricevic, M. Barwick, S.W. Basu, V. Bay, R. Beatty, J.J. Becker, K.-H. Becker Tjus, J. Beise, J. Bellenghi, C. Benda, S. BenZvi, S. Berley, D. Bernardini, E. Besson, D.Z. Binder, G. Bindig, D. Blaufuss, E. Blot, S. Bontempo, F. Book, J.Y. Borowka, J. Boscolo Meneguolo, C. Böser, S. Botner, O. Böttcher, J. Bourbeau, E. Braun, J. Brinson, B. Brostean-Kaiser, J. Burley, R.T. Busse, R.S. 2022 https://dx.doi.org/10.18452/27684 https://edoc.hu-berlin.de/handle/18452/28339 en eng Humboldt-Universität zu Berlin Creative Commons Attribution 4.0 International (CC BY 4.0) Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Analysis and statistical methods Data analysis Neutrino detectors Particle identification methods 520 Astronomie und zugeordnete Wissenschaften 530 Physik article-journal ScholarlyArticle article Text 2022 ftdatacite https://doi.org/10.18452/27684 2023-12-01T10:51:50Z 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 ... Text Ice Sheet South pole DataCite South Pole |
spellingShingle | Analysis and statistical methods Data analysis Neutrino detectors Particle identification methods 520 Astronomie und zugeordnete Wissenschaften 530 Physik Abbasi, R. Ackermann, M. Adams, J. Aggarwal, N. Aguilar, J.A. Ahlers, M. Ahrens, M. Alameddine, J.M. Alves, A.A. Amin, N.M. Andeen, K. Anderson, T. Anton, G. Argüelles, C. Ashida, Y. Athanasiadou, S. Axani, S. Bai, X. Balagopal V., A. Baricevic, M. Barwick, S.W. Basu, V. Bay, R. Beatty, J.J. Becker, K.-H. Becker Tjus, J. Beise, J. Bellenghi, C. Benda, S. BenZvi, S. Berley, D. Bernardini, E. Besson, D.Z. Binder, G. Bindig, D. Blaufuss, E. Blot, S. Bontempo, F. Book, J.Y. Borowka, J. Boscolo Meneguolo, C. Böser, S. Botner, O. Böttcher, J. Bourbeau, E. Braun, J. Brinson, B. Brostean-Kaiser, J. Burley, R.T. Busse, R.S. Graph Neural Networks for low-energy event classification & reconstruction in IceCube ... |
title | 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_short | Graph Neural Networks for low-energy event classification & reconstruction in IceCube ... |
title_sort | graph neural networks for low-energy event classification & reconstruction in icecube ... |
topic | Analysis and statistical methods Data analysis Neutrino detectors Particle identification methods 520 Astronomie und zugeordnete Wissenschaften 530 Physik |
topic_facet | Analysis and statistical methods Data analysis Neutrino detectors Particle identification methods 520 Astronomie und zugeordnete Wissenschaften 530 Physik |
url | https://dx.doi.org/10.18452/27684 https://edoc.hu-berlin.de/handle/18452/28339 |