Speaker
Description
The Pierre Auger Observatory collects vast amounts of complex spatial-temporal data from extensive air showers induced by ultra-high-energy cosmic rays (UHECRs), i.e., those with energies above 10^18 eV. Determining the mass composition of the primary particle is a key challenge, as direct measurements are impossible and traditional analytical methods struggle with the complexity of shower footprints recorded by ground-based detectors. To address this, we leverage machine learning and deep learning techniques to extract high-level observables from the Surface Detector Array, including Water-Cherenkov and Surface-Scintillator detectors. These models outperform classical reconstruction methods in simulations and show promising results, enabling more accurate inference of UHECR properties.