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Abstract:

The interpretation of experimental data of ultra-high energy cosmic rays (UHECRs) above 10^17 eV is still under controversial debate. The development and improvement of numerical tools to propagate UHECRs in galactic and extragalactic space is a crucial ingredient to interpret data and to draw conclusions on astrophysical parameters. In this contribution the next major release of the publicly available code CRPropa (3.0) is presented. It reflects a complete redesign of the code structure to facilitate high performance computing and comprises new physical features such as an interface for galactic propagation using lensing techniques and inclusion of cosmological effects in a three-dimensional environment. The performance is benchmarked and first applications are presented.

Abstract:

Cosmic rays (CRs) may be used to infer properties of intervening cosmic magnetic fields. Conversely, understanding the effects of magnetic fields on the propagation of high-energy CRs is crucial to elucidate their origin. In the present work we investigate the role of intracluster magnetic fields on the propagation of CRs with energies between $10^{16}$ and $10^{18.5}$ eV. We look for possible signatures of a transition in the CR propagation regime, from diffusive to ballistic. Finally, we discuss the consequences of the confinement of high-energy CRs in clusters and superclusters for the production of gamma rays and neutrinos.

Abstract:

The atmospheric depth of the air shower maximum $X_{\mathrm{max}}$ is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of $X_{\mathrm{max}}$ are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of $X_{\mathrm{max}}$ from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of $X_{\mathrm{max}}$. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed $X_{\mathrm{max}}$ using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than $25~\mathrm{g/cm^{2}}$ at energies above $2\times 10^{19}~\mathrm{eV}$.

Abstract:

The Auger Muon Infill Ground Array (AMIGA) is part of the AugerPrime upgrade of the Pierre Auger Observatory. It consists of particle counters buried 2.3 m underground next to the water-Cherenkov stations that form the 23.5 km$^2$ large infilled array. The reduced distance between detectors in this denser area allows the lowering of the energy threshold for primary cosmic ray reconstruction down to about 10$^{17}$ eV. At the depth of 2.3 m the electromagnetic component of cosmic ray showers is almost entirely absorbed so that the buried scintillators provide an independent and direct measurement of the air showers muon content. This work describes the design and implementation of the AMIGA embedded system, which provides centralized control, data acquisition and environment monitoring to its detectors. The presented system was firstly tested in the engineering array phase ended in 2017, and lately selected as the final design to be installed in all new detectors of the production phase. The system was proven to be robust and reliable and has worked in a stable manner since its first deployment.