Composition Analysis of cosmic-rays at IceCube Observatory, using Graph Neural Networks
The IceCube Neutrino Observatory, located at the South Pole, is a multi-component detector that detects high-energy particles from astrophysical sources. Cosmic Rays (CRs) are charged particles from these astrophysical accelerators. CRs and CR-induced air-showers furnish us with the possibility to d...
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ftubkarlsruhe:oai:EVASTAR-Karlsruhe.de:1000143576 2023-05-15T18:22:52+02:00 Composition Analysis of cosmic-rays at IceCube Observatory, using Graph Neural Networks Koundal, Paras IceCube Collaboration 2022-03-09 https://publikationen.bibliothek.kit.edu/1000143576 eng eng https://publikationen.bibliothek.kit.edu/1000143576 info:eu-repo/semantics/openAccess ddc:530 Physics info:eu-repo/classification/ddc/530 doc-type:conferenceObject Event info:eu-repo/semantics/article article 2022 ftubkarlsruhe 2023-01-22T23:23:39Z The IceCube Neutrino Observatory, located at the South Pole, is a multi-component detector that detects high-energy particles from astrophysical sources. Cosmic Rays (CRs) are charged particles from these astrophysical accelerators. CRs and CR-induced air-showers furnish us with the possibility to discern the fundamental properties and behavior of such sources. When coupled to the IceTop surface array, IceCube affords unique three-dimensional detection and cosmic-ray analysis in the transition region from galactic to extragalactic sources. This work tries to improve the estimation of CR primary mass on a per-event basis in the mentioned energy range. The work benefits from using the full in-ice shower footprint and additional composition-sensitive air-shower parameters, in addition to global shower-footprint parameters already used in an earlier work. A Graph Neural Network (GNN) based implementation uses the full in-ice shower footprint. Described using nodes and edges, graphs allow us to efficiently represent relational data and learn hidden representations of input data to obtain better model accuracy. Mapping in-ice IceCube detectors, DOMs(Digital Optical Module), as a graph emerges as a natural solution. Using GNNs for cosmic-ray analysis at IceCube also has the added benefit of allowing an easier re-implementation to the planned next-generation upgraded instrument, called IceCube-Gen2. Article in Journal/Newspaper South pole KITopen (Karlsruhe Institute of Technologie) South Pole |
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KITopen (Karlsruhe Institute of Technologie) |
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English |
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ddc:530 Physics info:eu-repo/classification/ddc/530 |
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ddc:530 Physics info:eu-repo/classification/ddc/530 Koundal, Paras IceCube Collaboration Composition Analysis of cosmic-rays at IceCube Observatory, using Graph Neural Networks |
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ddc:530 Physics info:eu-repo/classification/ddc/530 |
description |
The IceCube Neutrino Observatory, located at the South Pole, is a multi-component detector that detects high-energy particles from astrophysical sources. Cosmic Rays (CRs) are charged particles from these astrophysical accelerators. CRs and CR-induced air-showers furnish us with the possibility to discern the fundamental properties and behavior of such sources. When coupled to the IceTop surface array, IceCube affords unique three-dimensional detection and cosmic-ray analysis in the transition region from galactic to extragalactic sources. This work tries to improve the estimation of CR primary mass on a per-event basis in the mentioned energy range. The work benefits from using the full in-ice shower footprint and additional composition-sensitive air-shower parameters, in addition to global shower-footprint parameters already used in an earlier work. A Graph Neural Network (GNN) based implementation uses the full in-ice shower footprint. Described using nodes and edges, graphs allow us to efficiently represent relational data and learn hidden representations of input data to obtain better model accuracy. Mapping in-ice IceCube detectors, DOMs(Digital Optical Module), as a graph emerges as a natural solution. Using GNNs for cosmic-ray analysis at IceCube also has the added benefit of allowing an easier re-implementation to the planned next-generation upgraded instrument, called IceCube-Gen2. |
format |
Article in Journal/Newspaper |
author |
Koundal, Paras IceCube Collaboration |
author_facet |
Koundal, Paras IceCube Collaboration |
author_sort |
Koundal, Paras |
title |
Composition Analysis of cosmic-rays at IceCube Observatory, using Graph Neural Networks |
title_short |
Composition Analysis of cosmic-rays at IceCube Observatory, using Graph Neural Networks |
title_full |
Composition Analysis of cosmic-rays at IceCube Observatory, using Graph Neural Networks |
title_fullStr |
Composition Analysis of cosmic-rays at IceCube Observatory, using Graph Neural Networks |
title_full_unstemmed |
Composition Analysis of cosmic-rays at IceCube Observatory, using Graph Neural Networks |
title_sort |
composition analysis of cosmic-rays at icecube observatory, using graph neural networks |
publishDate |
2022 |
url |
https://publikationen.bibliothek.kit.edu/1000143576 |
geographic |
South Pole |
geographic_facet |
South Pole |
genre |
South pole |
genre_facet |
South pole |
op_relation |
https://publikationen.bibliothek.kit.edu/1000143576 |
op_rights |
info:eu-repo/semantics/openAccess |
_version_ |
1766202280830304256 |