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Application of machine learning techniques for cosmic ray event classification and implementation of a real-time ultra-high energy photon search with the surface detector of the Pierre Auger Observatory : dissertation
Lukas Zehrer, 2021, doctoral dissertation

Abstract: Despite their discovery already more than a century ago, Cosmic Rays (CRs) still did not divulge all their properties yet. Theories about the origin of ultra-high energy (UHE, > 10^18 eV) CRs predict accompanying primary photons. The existence of UHE photons can be investigated with the world’s largest ground-based experiment for detection of CR-induced extensive air showers (EAS), the Pierre Auger Observatory, which offers an unprecedented exposure to rare UHE cosmic particles. The discovery of photons in the UHE regime would open a new observational window to the Universe, improve our understanding of the origin of CRs, and potentially uncloak new physics beyond the standard model. The novelty of the presented work is the development of a "real-time" photon candidate event stream to a global network of observatories, the Astrophysical Multimessenger Observatory Network (AMON). The stream classifies CR events observed by the Auger surface detector (SD) array as regards their probability to be photon nominees, by feeding to advanced machine learning (ML) methods observational air shower parameters of individual CR events combined in a multivariate analysis (MVA). The described straightforward classification procedure further increases the Pierre Auger Observatory’s endeavour to contribute to the global effort of multi-messenger (MM) studies of the highest energy astrophysical phenomena, by supplying AMON partner observatories the possibility to follow-up detected UHE events, live or in their archival data.
Keywords: astroparticle physics, ultra-high energy cosmic rays, ultra-high energy photons, extensive air showers, Pierre Auger Observatory, multi-messenger, AMON, machine learning, multivariate analysis, dissertations
Published in RUNG: 27.10.2021; Views: 2732; Downloads: 147
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Calibration of the underground muon detector of the Pierre Auger Observatory
A. Aab, Andrej Filipčič, Jon Paul Lundquist, Samo Stanič, Marta Trini, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Lukas Zehrer, 2021, original scientific article

Abstract: To obtain direct measurements of the muon content of extensive air showers with energy above 10[sup]16.5 eV, the Pierre Auger Observatory is currently being equipped with an underground muon detector (UMD), consisting of 219 10 m[sup]2-modules, each segmented into 64 scintillators coupled to silicon photomultipliers (SiPMs). Direct access to the shower muon content allows for the study of both of the composition of primary cosmic rays and of high-energy hadronic interactions in the forward direction. As the muon density can vary between tens of muons per m[sup]2 close to the intersection of the shower axis with the ground to much less than one per m[sup]2 when far away, the necessary broad dynamic range is achieved by the simultaneous implementation of two acquisition modes in the read-out electronics: the binary mode, tuned to count single muons, and the ADC mode, suited to measure a high number of them. In this work, we present the end-to-end calibration of the muon detector modules: first, the SiPMs are calibrated by means of the binary channel, and then, the ADC channel is calibrated using atmospheric muons, detected in parallel to the shower data acquisition. The laboratory and field measurements performed to develop the implementation of the full calibration chain of both binary and ADC channels are presented and discussed. The calibration procedure is reliable to work with the high amount of channels in the UMD, which will be operated continuously, in changing environmental conditions, for several years.
Keywords: ultra-high energy cosmic rays, extensive air showers (EAS), EAS muonic component, Pierre Auger Observatory, underground muon detector, detector calibration
Published in RUNG: 14.04.2021; Views: 2638; Downloads: 139
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