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: | , |
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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 |
| Summary: | 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. |
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