Investigating the UHECR characteristics from cosmogenic neutrino limits with the measurements of the Pierre Auger Observatory

Cosmogenic neutrinos are expected to originate in the extragalactic propagation of ultra-high-energy cosmic rays (UHECRs), as a result of their interactions with background photons. Due to these reactions, the visible Universe in UHECRs is more limited than in neutrinos, which instead could reach us without interacting after traveling cosmological distances. In this contribution, we exploit a multimessenger approach by computing the expected energy spectrum and mass composition of UHECRs at Earth corresponding to combinations of spectral parameters and mass composition at their sources, as well as parameters related to the UHECR source distribution, and by determining, at the same time, the associated cosmogenic neutrino fluxes. By comparing the expected UHECR observables to the energy spectrum and mass composition measured at the Pierre Auger Observatory above 1017.8 eV and the expected neutrino fluxes to the most updated neutrino limits, we show the dependence of the neutrino fluxes on the characteristics of the the properties of the potential sources of UHECRs, such as their cosmological evolution and maximum redshift. In addition, the fraction of protons compatible with the data is also investigated in terms of expected neutrino fluxes

Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks

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 1018.5, eV and 1020 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

Deep-Learning-Based Cosmic-Ray Mass Reconstruction Using the Water-Cherenkov and Scintillation Detectors of AugerPrime

At the highest energies, cosmic rays can be detected only indirectly by the extensive air showers they create upon interaction with the Earth\u27s atmosphere. While high-statistics measurements of the energy and arrival directions of cosmic rays can be performed with large surface detector arrays like the Pierre Auger Observatory, the determination of the cosmic-ray mass on an event-by-event basis is challenging. Meaningful physical observables in this regard include the depth of maximum of air-shower profiles, which is related to the mean free path of the cosmic ray in the atmosphere and the shower development, as well as the number of muons that rises with the number of nucleons in a cosmic-ray particle. In this contribution, we present an approach to determine both of these observables from combined measurements of water-Cherenkov detectors and scintillation detectors, which are part of the AugerPrime upgrade of the Observatory. To characterize the time-dependent signals of the two detectors both separately as well as in correlation to each other, we apply deep learning techniques. Transformer networks employing the attention mechanism are especially well-suited for this task. We present the utilized network concepts and apply them to simulations to determine the precision of the event-by-event mass reconstruction that can be achieved by the combined measurements of the depth of shower maximum and the number of muons