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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| Format: | Book |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://publikationen.bibliothek.kit.edu/1000167361 https://publikationen.bibliothek.kit.edu/1000167361/152030166 https://doi.org/10.5445/IR/1000167361 |
| _version_ | 1828677134809300992 |
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| author | Koundal, Paras IceCube Collaboration |
| author_facet | Koundal, Paras IceCube Collaboration |
| author_sort | Koundal, Paras |
| collection | KITopen (Karlsruhe Institute of Technologie) |
| 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 | Book |
| genre | South pole |
| genre_facet | South pole |
| geographic | South Pole |
| geographic_facet | South Pole |
| id | ftubkarlsruhe:oai:EVASTAR-Karlsruhe.de:1000167361 |
| institution | Open Polar |
| language | English |
| op_collection_id | ftubkarlsruhe |
| op_doi | https://doi.org/10.5445/IR/100016736110.48550/arXiv.2211.17198 |
| op_relation | info:eu-repo/semantics/altIdentifier/doi/10.48550/arXiv.2211.17198 https://publikationen.bibliothek.kit.edu/1000167361 https://publikationen.bibliothek.kit.edu/1000167361/152030166 https://doi.org/10.5445/IR/1000167361 |
| op_rights | https://creativecommons.org/licenses/by/4.0/deed.de info:eu-repo/semantics/openAccess |
| publishDate | 2024 |
| record_format | openpolar |
| spelling | ftubkarlsruhe:oai:EVASTAR-Karlsruhe.de:1000167361 2025-04-06T15:06:38+00:00 Cosmic-Ray Composition analysis at IceCube using Graph Neural Networks Koundal, Paras IceCube Collaboration 2024-01-16 application/pdf https://publikationen.bibliothek.kit.edu/1000167361 https://publikationen.bibliothek.kit.edu/1000167361/152030166 https://doi.org/10.5445/IR/1000167361 eng eng info:eu-repo/semantics/altIdentifier/doi/10.48550/arXiv.2211.17198 https://publikationen.bibliothek.kit.edu/1000167361 https://publikationen.bibliothek.kit.edu/1000167361/152030166 https://doi.org/10.5445/IR/1000167361 https://creativecommons.org/licenses/by/4.0/deed.de info:eu-repo/semantics/openAccess ddc:530 Physics info:eu-repo/classification/ddc/530 doc-type:report Text info:eu-repo/semantics/book monograph info:eu-repo/semantics/publishedVersion 2024 ftubkarlsruhe https://doi.org/10.5445/IR/100016736110.48550/arXiv.2211.17198 2025-03-11T04:07:45Z 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. Book South pole KITopen (Karlsruhe Institute of Technologie) South Pole |
| spellingShingle | ddc:530 Physics info:eu-repo/classification/ddc/530 Koundal, Paras IceCube Collaboration Cosmic-Ray Composition analysis at IceCube using Graph Neural Networks |
| title | 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_short | Cosmic-Ray Composition analysis at IceCube using Graph Neural Networks |
| title_sort | cosmic-ray composition analysis at icecube using graph neural networks |
| topic | ddc:530 Physics info:eu-repo/classification/ddc/530 |
| topic_facet | ddc:530 Physics info:eu-repo/classification/ddc/530 |
| url | https://publikationen.bibliothek.kit.edu/1000167361 https://publikationen.bibliothek.kit.edu/1000167361/152030166 https://doi.org/10.5445/IR/1000167361 |