2025/09/22 by Margita Kubátová, Kubátová, Margita
Physics and Astronomy · #Astrophysics and Cosmic Phenomena #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Neutrino Physics Research #Particle Detector Development and Performance
paper · pdf · doi:10.48550/arxiv.2509.18333
openalex publication_date 2025/09/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The Surface Detector (SD) of the Pierre Auger Observatory is a 3000 km2 array of stations, whose main components are Water-Cherenkov Detectors (WCDs) recording ground-level signals from Extensive Air Showers (EASs) initiated by Ultra-High-Energy Cosmic Rays (UHECRs). Understanding the physics of UHECRs requires knowledge of their mass composition, for which the number of ground muons is a key probe. Isolating the muon component is difficult, as different types of particles contribute to the SD signal. We apply a recurrent neural network to estimate the muon content of the SD signals, showing small bias in simulations and weak dependence on selected hadronic interaction model.