Gamma-ray astronomy

Abstract:

Recent developments in machine learning (ML) techniques present a promising new analysis method for high-speed imaging in astroparticle physics experiments, for example with imaging atmospheric Cherenkov telescopes (IACTs). In particular, the use of timing information with new machine learning techniques provides a novel method for event classification. Previous work in this field has utilised images of the integrated charge from IACT camera photomultipliers, but the majority of current and upcoming IACT cameras have the capacity to read out the entire photo-sensor waveform following a trigger. As the arrival times of Cherenkov photons from extensive air showers (EAS) at the camera plane are dependent upon the altitude of their emission, these waveforms contain information useful for IACT event classification. In this work, we investigate the potential for using these waveforms with ML techniques, and find that a highly effective means of utilising their information is to create a set of seven additional two dimensional histograms of waveform parameters to be fed into the machine learning algorithm along with the integrated charge image. This appears to be superior to using only these new ML techniques with the waveform integrated charge alone. We also examine these timing-based ML techniques in the context of other experiments.

Abstract:

One of the most promising ways to probe intergalactic magnetic fields (IGMFs) is through gamma rays produced in electromagnetic cascades initiated by high-energy gamma rays or cosmic rays in the intergalactic space. Because the charged component of the cascade is sensitive to magnetic fields, gamma-ray observations of distant objects such as blazars can be used to constrain IGMF properties. Ground-based and space-borne gamma-ray telescopes deliver spectral, temporal, and angular information of high-energy gamma-ray sources, which carries imprints of the intervening magnetic fields. This provides insights into the nature of the processes that led to the creation of the first magnetic fields and into the phenomena that impacted their evolution. Here we provide a detailed description of how gamma-ray observations can be used to probe cosmic magnetism. We review the current status of this topic and discuss the prospects for measuring IGMFs with the next generation of gamma-ray observatories.

Abstract:

The Pierre Auger Observatory, at present the largest cosmic-ray observatory ever built, is instrumented with a ground array of 1600 water-Cherenkov detectors, known as the Surface Detector (SD). The SD samples the secondary particle content (mostly photons, electrons, positrons and muons) of extensive air showers initiated by cosmic rays with energies ranging from 1017 eV up to more than 1020 eV. Measuring the independent contribution of the muon component to the total registered signal is crucial to enhance the capability of the Observatory to estimate the mass of the cosmic rays on an event-by-event basis. However, with the current design of the SD, it is difficult to straightforwardly separate the contributions of muons to the SD time traces from those of photons, electrons and positrons. In this paper, we present a method aimed at extracting the muon component of the time traces registered with each individual detector of the SD using Recurrent Neural Networks. We derive the performances of the method by training the neural network on simulations, in which the muon and the electromagnetic components of the traces are known. We conclude this work showing the performance of this method on experimental data of the Pierre Auger Observatory. We find that our predictions agree with the parameterizations obtained by the AGASA collaboration to describe the lateral distributions of the electromagnetic and muonic components of extensive air showers.

Abstract:

Clusters of galaxies can potentially produce cosmic rays (CRs) up to very-high energies via large-scale shocks and turbulent acceleration. Due to their unique magnetic-field configuration, CRs with energy ≤1017 eV can be trapped within these structures over cosmological time scales, and generate secondary particles, including neutrinos and gamma rays, through interactions with the background gas and photons. In this work we compute the contribution from clusters of galaxies to the diffuse neutrino background. We employ three-dimensional cosmological magnetohydrodynamical simulations of structure formation to model the turbulent intergalactic medium. We use the distribution of clusters within this cosmological volume to extract the properties of this population, including mass, magnetic field, temperature, and density. We propagate CRs in this environment using multi-dimensional Monte Carlo simulations across different redshifts (from z ∼ 5 to z = 0), considering all relevant photohadronic, photonuclear, and hadronuclear interaction processes. We find that, for CRs injected with a spectral index α = 1.5 − 2.7 and cutoff energy Emax = 1016 − 5 × 1017 eV, clusters contribute to a sizeable fraction to the diffuse flux observed by the IceCube Neutrino Observatory, but most of the contribution comes from clusters with M ≳ 1014 M⊙ and redshift z ≲ 0.3. If we include the cosmological evolution of the CR sources, this flux can be even higher.