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Azimuthal asymmetry in the risetime of the surface detector signals of the Pierre Auger Observatory
Marko Zavrtanik, Danilo Zavrtanik, Lili Yang, Serguei Vorobiov, Darko Veberič, Marta Trini, Samo Stanič, Ahmed Saleh, Gašper Kukec Mezek, Andrej Filipčič, A. Aab, 2016, original scientific article

Abstract: The azimuthal asymmetry in the risetime of signals in Auger surface detector stations is a source of information on shower development. The azimuthal asymmetry is due to a combination of the longitudinal evolution of the shower and geometrical effects related to the angles of incidence of the particles into the detectors. The magnitude of the effect depends upon the zenith angle and state of development of the shower and thus provides a novel observable, (secθ)max, sensitive to the mass composition of cosmic rays above 3×1018  eV. By comparing measurements with predictions from shower simulations, we find for both of our adopted models of hadronic physics (QGSJETII-04 and EPOS-LHC) an indication that the mean cosmic-ray mass increases slowly with energy, as has been inferred from other studies. However, the mass estimates are dependent on the shower model and on the range of distance from the shower core selected. Thus the method has uncovered further deficiencies in our understanding of shower modeling that must be resolved before the mass composition can be inferred from (secθ)max.
Found in: osebi
Keywords: ultra-high energy cosmic rays (UHECR), UHECR mass composition, Pierre Auger Observatory, extensive air showers, Auger Surface Detector signals risetime, azimuthal symmetry
Published: 15.04.2016; Views: 2975; Downloads: 0
.pdf Fulltext (698,19 KB)

110.
Mass composition of ultra-high energy cosmic rays at the Pierre Auger Observatory
Gašper Kukec Mezek, 2019, doctoral dissertation

Abstract: Cosmic rays with energies above 10^18 eV, usually referred to as ultra-high energy cosmic rays (UHECR), have been a mystery from the moment they have been discovered. Although we have now more information on their extragalactic origin, their direct sources still remain hidden due to deviations caused by galactic magnetic fields. Another mystery, apart from their production sites, is their nature. Their mass composition, still uncertain at these energies, would give us a better understanding on their production, acceleration, propagation and capacity to produce extensive air showers in the Earth's atmosphere. Mass composition studies of UHECR try to determine their nature from the difference in development of their extensive air showers. In this work, observational parameters from the hybrid detection system of the Pierre Auger Observatory are used in a multivariate analysis to obtain the mass composition of UHECR. The multivariate analysis (MVA) approach combines a number of mass composition sensitive variables and tries to improve the separation between different UHECR particle masses. Simulated distributions of different primary particles are fitted to measured observable distributions in order to determine individual elemental fractions of the composition. When including observables from the surface detector, we find a discrepancy in the estimated mass composition between a mixed simulation sample and the Pierre Auger data. Our analysis results from the Pierre Auger data are to a great degree independent on hadronic interaction models. Although they differ at higher primary masses, the different models are more consistent, when combining fractions of oxygen and iron. Compared to previously published results, the systematic uncertainty from hadronic interaction models is roughly four times smaller. Our analysis reports a predominantly heavy composition of UHECR, with more than a 50% fraction of oxygen and iron at low energies. The composition is then becoming heavier with increasing energy, with a fraction of oxygen and iron above 80% at the highest energies.
Found in: osebi
Keywords: astroparticle physics, ultra-high energy cosmic rays, extensive air showers, mass composition, Pierre Auger Observatory, machine learning, multivariate analysis
Published: 03.04.2019; Views: 1725; Downloads: 104
.pdf Fulltext (17,53 MB)

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