Speaker
Description
Ultra-high-energy photons have long been sought as tracers of the most energetic processes in the universe. Several components contribute to an expected diffuse photon flux, including interactions of cosmic rays with Galactic matter and radiation fields, as well as more exotic scenarios such as the decay of super-heavy dark matter. Regardless of their origin, the expected flux is extremely low, making direct detection impractical and requiring large ground-based detector arrays. In this contribution, we present a novel method for photon–hadron discrimination in the energy range of 50 to $200\,\text{PeV}$ based on deep learning algorithms. Our approach relies on information from both the Surface Detector (SD) and the Underground Muon Detector (UMD) of the Pierre Auger Observatory. The SD, consisting of an array of water-Cherenkov detectors, is used to measure secondary particles at ground level, capturing both the electromagnetic and muonic components of extensive air showers. Meanwhile, the UMD, composed of buried scintillator modules, is sensitive to air-shower muons with energies above $\sim 1\,\text{GeV}$, providing direct information on the shower’s muonic content and enhancing the identification of muon-poor air showers as initiated by photon primaries. The method represents events as graphs, and consequently, the network architecture is composed of graph attention layers. This approach is particularly well-suited for handling any irregularity in the detector layout. We applied our method to a data subset employed in previous studies to discuss the implications of unblinding the full current dataset, as well as the prospects of the increasing data volume expected in the coming years, particularly in terms of sensitivity to various diffuse fluxes from theoretical predictions.
| Collaboration(s) | The Pierre Auger Collaboration |
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