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1.
Studies of the UHECR Mass Composition and Hadronic Interactions with the FD and SD of the Pierre Auger Observatory
J.M. Carceller, Andrej Filipčič, Gašper Kukec Mezek, Ahmed Saleh, Samo Stanič, Marta Trini, Serguei Vorobiov, Lili Yang, Danilo Zavrtanik, Marko Zavrtanik, Lukas Zehrer, 2019, objavljeni znanstveni prispevek na konferenci

Opis: With data on the depth of maximum Xmax collected during more than a decade of operation of the Pierre Auger Observatory, we report on the inferences on the mass composition of UHECRs in the energy range E = 10[sup]17.2 − 10[sup]19.6 eV and on the measurements of the proton-air cross section for energies up to 10[sup]18.5 eV. We also present the results on Xmax obtained using the information on the particle arrival times recorded by the SD stations allowing us to extend the Xmax measurements up to 10[sup]20 eV. The inferences on mass composition, in particular using the data of the SD, are subject to systematic uncertainties due to uncertainties in the description of hadronic interactions at ultra-high energies. We discuss this problem with respect to the properties of the muonic component of extensive air-showers as derived from the SD data.
Ključne besede: ultra-high-energy cosmic rays (UHECRs), extensive air showers (EAS), EAS muonic component, EAS electromagnetic component, Pierre Auger Observatory, UHECR mass composition, UHECR hadronic interactions
Objavljeno v RUNG: 11.10.2023; Ogledov: 520; Prenosov: 8
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2.
Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks
J.M. Carceller, Andrej Filipčič, Jon Paul Lundquist, Samo Stanič, Serguei Vorobiov, Danilo Zavrtanik, Marko Zavrtanik, Lukas Zehrer, 2022, objavljeni znanstveni prispevek na konferenci

Opis: We present a method based on the use of Recurrent Neural Networks to extract the muon component from the time traces registered with water-Cherenkov detector (WCD) stations of the Surface Detector of the Pierre Auger Observatory. The design of the WCDs does not allow to separate the contribution of muons to the time traces obtained from the WCDs from those of photons, electrons and positrons for all events. Separating the muon and electromagnetic components is crucial for the determination of the nature of the primary cosmic rays and properties of the hadronic interactions at ultra-high energies. We trained a neural network to extract the muon and the electromagnetic components from the WCD traces using a large set of simulated air showers, with around 450 000 simulated events. For training and evaluating the performance of the neural network, simulated events with energies between 10^18.5 eV and 10^20 eV and zenith angles below 60 degrees were used. We also study the performance of this method on experimental data of the Pierre Auger Observatory and show that our predicted muon lateral distributions agree with the parameterizations obtained by the AGASA collaboration.
Ključne besede: Pierre Auger Observatory, indirect detection, surface detection, ground array, ultra-high energy, cosmic rays, muons, machine learning, recurrent neural network
Objavljeno v RUNG: 04.10.2023; Ogledov: 530; Prenosov: 5
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