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1.
Investigations of CORSIKA thinning levels suitable for studies of photon-hadron discrimination at ultra-high energies
Fiona Ellwanger, Andrej Filipčič, Jon Paul Lundquist, Shima Ujjani Shivashankara, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, 2025, published scientific conference contribution

Abstract: Cosmic ray detectors like the 3000 sq. km surface array of the Pierre Auger Observatory are capable of observing high-energy photons in the range of 10[sup]18 to 10[sup]20 eV if the flux is sufficiently high. However, no clear candidates for ultra-high-energy photons have been identified yet, so simulations must be used to study typical trigger patterns and observables for discriminating photons from hadrons, e.g., with neural networks. Thinning algorithms are applied to keep the computation time and file sizes in a manageable range since the simulation of ultra-high-energy particle showers is computationally expensive. In CORSIKA, particles with energies below a certain fraction of the primary energy, the thinning level, are exposed to thinning. In the case of thinning, only one of the particles emerging from an interaction is tracked. By assigning a corresponding weight, this particle then represents a number of its siblings. However, the weights of particles that originate from electromagnetic interactions can be 100 times larger than for hadronic interactions. In contrast to hadronic showers, where a major part of the signal in a surface detector is produced by muons, photon showers are almost purely electromagnetic. Using simulations of photon-induced showers with two different thinning levels, the influence on different observables used for photon-hadron discrimination is investigated. Effects deriving from both statistical sampling and detector simulations are considered. Possible influences on station-level as well as event-level observables are probed. With this study, we are reassured that the optimal thinning parameters determined for hadron-induced showers are also sufficient for photon-induced showers.
Keywords: ultra-high-energy cosmic rays, Pierre Auger Observatory, extensive air showers, CORSIKA air-shower simulator
Published in RUNG: 16.05.2025; Views: 63; Downloads: 0
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2.
Investigations of a novel energy estimator using deep learning for the surface detector of the Pierre Auger Observatory
Fiona Ellwanger, Andrej Filipčič, Jon Paul Lundquist, Shima Ujjani Shivashankara, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, 2023, published scientific conference contribution

Abstract: Exploring physics at energies beyond the reach of human-built accelerators by studying cosmic rays requires an accurate reconstruction of their energy. At the highest energies, cosmic rays are indirectly measured by observing a shower of secondary particles produced by their interaction in the atmosphere. At the Pierre Auger Observatory, the energy of the primary particle is either reconstructed from measurements of the emitted fluorescence light, produced when secondary particles travel through the atmosphere, or shower particles detected with the surface detector at the ground. The surface detector comprises a triangular grid of water-Cherenkov detectors that measure the shower footprint at the ground level. With deep learning, large simulation data sets can be used to train neural networks for reconstruction purposes. In this work, we present an application of a neural network to estimate the energy of the primary particle from the surface detector data by exploiting the time structure of the particle footprint. When evaluating the precision of the method on air shower simulations, we find the potential to significantly reduce the composition bias compared to methods based on fitting the lateral signal distribution. Furthermore, we investigate possible biases arising from systematic differences between simulations and data.
Keywords: ultra-high energy cosmic rays, Pierre Auger Observatory, surface detector, neural network
Published in RUNG: 22.01.2024; Views: 2306; Downloads: 5
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