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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 ...
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genre Ice Sheet
South pole
genre_facet Ice Sheet
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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