Cosmic-Ray Composition analysis at IceCube using Graph Neural Networks

The IceCube Neutrino Observatory is a multi-component detector embedded deep within the South-Pole Ice. This proceeding will discuss an analysis from an integrated operation of IceCube and its surface array, IceTop, to estimate cosmic-ray composition. The work will describe a novel graph neural netw...

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Bibliographic Details
Main Authors: Koundal, Paras, IceCube Collaboration
Format: Article in Journal/Newspaper
Language:English
Published: Scuola Internazionale Superiore di Studi Avanzati 2024
Subjects:
ddc:530
Physics
info:eu-repo/classification/ddc/530
South Pole
South pole
Online Access:https://publikationen.bibliothek.kit.edu/1000167205
https://publikationen.bibliothek.kit.edu/1000167205/152030137
https://doi.org/10.5445/IR/1000167205
id ftubkarlsruhe:oai:EVASTAR-Karlsruhe.de:1000167205
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spelling ftubkarlsruhe:oai:EVASTAR-Karlsruhe.de:1000167205 2024-02-11T10:08:40+01:00 Cosmic-Ray Composition analysis at IceCube using Graph Neural Networks Koundal, Paras IceCube Collaboration 2024-01-11 application/pdf https://publikationen.bibliothek.kit.edu/1000167205 https://publikationen.bibliothek.kit.edu/1000167205/152030137 https://doi.org/10.5445/IR/1000167205 eng eng Scuola Internazionale Superiore di Studi Avanzati Pos proceedings of science info:eu-repo/semantics/altIdentifier/doi/10.22323/1.423.0085 info:eu-repo/semantics/altIdentifier/issn/1824-8039 https://publikationen.bibliothek.kit.edu/1000167205 https://publikationen.bibliothek.kit.edu/1000167205/152030137 https://doi.org/10.5445/IR/1000167205 https://creativecommons.org/licenses/by-nc-nd/4.0/deed.de info:eu-repo/semantics/openAccess ISSN: 1824-8039 ddc:530 Physics info:eu-repo/classification/ddc/530 doc-type:conferenceObject Text info:eu-repo/semantics/article article info:eu-repo/semantics/publishedVersion 2024 ftubkarlsruhe https://doi.org/10.5445/IR/100016720510.22323/1.423.0085 2024-01-21T23:13:56Z The IceCube Neutrino Observatory is a multi-component detector embedded deep within the South-Pole Ice. This proceeding will discuss an analysis from an integrated operation of IceCube and its surface array, IceTop, to estimate cosmic-ray composition. The work will describe a novel graph neural network based approach for estimating the mass of primary cosmic rays, that takes advantage of signal-footprint information and reconstructed cosmic-ray air shower parameters. In addition, the work will also introduce new composition-sensitive parameters for improving the estimation of cosmic-ray composition, with the potential of improving our understanding of the high-energy muon content in cosmic-ray air showers. Article in Journal/Newspaper South pole KITopen (Karlsruhe Institute of Technologie) South Pole
institution Open Polar
collection KITopen (Karlsruhe Institute of Technologie)
op_collection_id ftubkarlsruhe
language English
topic ddc:530
Physics
info:eu-repo/classification/ddc/530
spellingShingle ddc:530
Physics
info:eu-repo/classification/ddc/530
Koundal, Paras
IceCube Collaboration
Cosmic-Ray Composition analysis at IceCube using Graph Neural Networks
topic_facet ddc:530
Physics
info:eu-repo/classification/ddc/530
description The IceCube Neutrino Observatory is a multi-component detector embedded deep within the South-Pole Ice. This proceeding will discuss an analysis from an integrated operation of IceCube and its surface array, IceTop, to estimate cosmic-ray composition. The work will describe a novel graph neural network based approach for estimating the mass of primary cosmic rays, that takes advantage of signal-footprint information and reconstructed cosmic-ray air shower parameters. In addition, the work will also introduce new composition-sensitive parameters for improving the estimation of cosmic-ray composition, with the potential of improving our understanding of the high-energy muon content in cosmic-ray air showers.
format Article in Journal/Newspaper
author Koundal, Paras
IceCube Collaboration
author_facet Koundal, Paras
IceCube Collaboration
author_sort Koundal, Paras
title Cosmic-Ray Composition analysis at IceCube using Graph Neural Networks
title_short Cosmic-Ray Composition analysis at IceCube using Graph Neural Networks
title_full Cosmic-Ray Composition analysis at IceCube using Graph Neural Networks
title_fullStr Cosmic-Ray Composition analysis at IceCube using Graph Neural Networks
title_full_unstemmed Cosmic-Ray Composition analysis at IceCube using Graph Neural Networks
title_sort cosmic-ray composition analysis at icecube using graph neural networks
publisher Scuola Internazionale Superiore di Studi Avanzati
publishDate 2024
url https://publikationen.bibliothek.kit.edu/1000167205
https://publikationen.bibliothek.kit.edu/1000167205/152030137
https://doi.org/10.5445/IR/1000167205
geographic South Pole
geographic_facet South Pole
genre South pole
genre_facet South pole
op_source ISSN: 1824-8039
op_relation Pos proceedings of science
info:eu-repo/semantics/altIdentifier/doi/10.22323/1.423.0085
info:eu-repo/semantics/altIdentifier/issn/1824-8039
https://publikationen.bibliothek.kit.edu/1000167205
https://publikationen.bibliothek.kit.edu/1000167205/152030137
https://doi.org/10.5445/IR/1000167205
op_rights https://creativecommons.org/licenses/by-nc-nd/4.0/deed.de
info:eu-repo/semantics/openAccess
op_doi https://doi.org/10.5445/IR/100016720510.22323/1.423.0085
_version_ 1790608187728068608

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