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...
Main Authors: | , |
---|---|
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Scuola Internazionale Superiore di Studi Avanzati
2024
|
Subjects: | |
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 |
---|---|
record_format |
openpolar |
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 |