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1.
Mass composition from 3 EeV to 100 EeV using the depth of the maximum of air-shower profiles estimated with deep learning using surface detector data of the Pierre Auger Observatory
Jonas Glombitza, Andrej Filipčič, Jon Paul Lundquist, Shima Ujjani Shivashankara, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, 2023, published scientific conference contribution

Abstract: We present a new analysis for estimating the depth of the maximum of air-shower profiles, �max, to investigate the evolution of the ultra-high-energy cosmic ray mass composition from 3 to 100 EeV. We use a recently developed deep-learning-based technique for the reconstruction of �max from the data of the surface detector of the Pierre Auger Observatory. To avoid systematic uncertainties arising from hadronic interaction models in the simulation of surface detector data, we calibrate the new reconstruction technique with observations of the fluorescence detector. Using the novel analysis, we have a 10-fold increase of statistics at � > 5 EeV with respect to fluorescence detector data. We are able, for the first time, to study the evolution of the mean and standard deviation of the �max distributions up to 100 EeV. We find an excellent agreement with fluorescence observations and confirm the increase of the mean logarithmic mass ⟨ln(�)⟩ and a decrease of the �max fluctuations with energy. The �max measurement at the highest — so far inaccessible — energies is consistent with a pure mass composition and a mean logarithmic mass of around ∼ 3 (estimated using the Sibyll 2.3d and the EPOS-LHC hadronic interaction models). Furthermore, with the increase in statistics, we find indications for a structure beyond a constant elongation rate in the evolution of �max.
Keywords: ultra-high energy cosmic rays, Pierre Auger Observatory, surface detector, flourescence detector
Published in RUNG: 22.01.2024; Views: 595; Downloads: 6
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2.
Event-by-event reconstruction of the shower maximum Xmax with the Surface Detector of the Pierre Auger Observatory using deep learning
J. Glombitza, Andrej Filipčič, Jon Paul Lundquist, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Lukas Zehrer, 2022, published scientific conference contribution

Abstract: The measurement of the mass composition of ultra-high energy cosmic rays constitutes a prime challenge in astroparticle physics. Most detailed information on the composition can be obtained from measurements of the depth of maximum of air showers, Xmax, with the use of fluorescence telescopes, which can be operated only during clear and moonless nights. Using deep neural networks, it is now possible for the first time to perform an event-by-event reconstruction of Xmax with the Surface Detector (SD) of the Pierre Auger Observatory. Therefore, previously recorded data can be analyzed for information on Xmax, and thus, the cosmic-ray composition. Since the SD operates with a duty cycle of almost 100% and its event selection is less strict than for the Fluorescence Detector (FD), the gain in statistics with respect to the FD is almost a factor of 15 for energies above 10^19.5 eV. In this contribution, we introduce the neural network particularly designed for the SD of the Pierre Auger Observatory. We evaluate its performance using three different hadronic interaction models, verify its functionality using Auger hybrid measurements, and find that the method can extract mass information on an event level.
Keywords: Pierre Auger Observatory, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, composition, neural network, machine learning
Published in RUNG: 29.09.2023; Views: 857; Downloads: 5
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