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
Creators: name: Reck, Stefan
Publisher: name: Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
Year: 2023
Subjects: KM3NeT
ORCA
Cosmic Rays
Neutrinotelescope
Deep Learning

description: 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 cosmic ray composition, which will allow it to contribute to the aforementioned topics. KM3NeT/ORCA is a water-Cherenkov neutrino detector, currently under construction in the Mediterranean Sea at a depth of 2450 meters. The project's main goal is the determination of the neutrino mass ordering by measuring the energy- and zenith-angle-resolved oscillation probabilities of atmospheric neutrinos traversing the Earth. However, the majority of the particles observed by the detector are atmospheric muons. While they are a source of background for neutrino measurements, they can also be used to indirectly study the properties of extensive air showers and cosmic ray particles. Due to the high rate at which KM3NeT/ORCA detects atmospheric muons and the high density of its instrumentation, it is especially well suited to study the properties of muon bundle events, in which multiple muons traverse the detector simultaneously. These events are essential for investigating the properties of primary particles. Among the observables of muon bundles that allow conclusions to be drawn about the primary composition are the number of muons in the event, their lateral spread, as well as their incident direction. However, no dedicated methods for reconstructing these properties exist in KM3NeT so far. In this work, a set of novel reconstructions is developed, which makes use of deep artificial neural networks to analyze low-level experimental data and directly reconstruct the desired properties, independent of existing approaches. In the course of this, the new archetype of graph neural networks is introduced to KM3NeT. By representing the data of the detector as the mathematical structure of graphs, the measured information can be treated more efficiently and with a higher precision than with previous deep learning architectures used in KM3NeT. Detailed comparisons between the performance on simulations and on measured data for a small version of the detector show decent agreement. In the case of the directional reconstruction, the result from deep learning is compared to the established approach for single muon events. It is found that both approaches produce virtually the same result on measured data. This is an important step towards building trust in the method of deep learning, as these studies are the first time that deep neural networks are applied to measured data in KM3NeT. Using these new reconstructions, for the first time in KM3NeT an indirect measurement of the mass composition of cosmic rays is successfully performed on six months of measured data. With a dedicated set of Corsika simulations, the fraction of proton, helium and iron of the overall, energy-averaged flux is measured. A detailed discussion of systematic uncertainties stemming from different energy flux models and the seasonal variation of the muon flux is included.