Cosmic ray composition measurement using Graph Neural Networks for KM3NeT/ORCA ... : Messung der Zusammensetzung von kosmischer Strahlung mit Graph-basierten neuronalen Netzwerken in KM3NeT/ORCA ...
The chemical composition of cosmic rays arriving at the Earth's atmosphere at high energies in the PeV region and beyond has been the research target of various experiments, but is still subject to large uncertainties. A precise understanding of the high energy composition allows to constrain t...
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| Format: | Doctoral or Postdoctoral Thesis |
| Language: | English |
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Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
2023
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| Online Access: | https://dx.doi.org/10.25593/open-fau-135 https://open.fau.de/handle/openfau/30170 |
| Summary: | The chemical composition of cosmic rays arriving at the Earth's atmosphere at high energies in the PeV region and beyond has been the research target of various experiments, but is still subject to large uncertainties. A precise understanding of the high energy composition allows to constrain the origin of cosmic rays and the processes by which such high energies can be reached in astronomical accelerators. Highly energetic cosmic rays are measured indirectly, for which detailed simulations of air showers and the particle interactions within are required. Recent measurements at LHC have lead to a new generation of high energy interaction models, which allow to simulate the expected cosmic ray composition at higher energies than ever before. However, significant disagreements between these calculations and the measurements from several experiments which study cosmic rays via the atmospheric muons they produce in air showers have been found. This thesis examines the potential of KM3NeT/ORCA to measure the ... |
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