SciPost Submission Page
Estimation of Temporal Muon Signals in Water-Cherenkov Detectors of the Surface Detector of the Pierre Auger Observatory
by Margita Kubátová
Submission summary
| Authors (as registered SciPost users): | Margita Kubátová |
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| Preprint Link: | https://arxiv.org/abs/2509.18333v1 (pdf) |
| Date submitted: | Sept. 24, 2025, 9:29 a.m. |
| Submitted by: | Margita Kubátová |
| Submitted to: | SciPost Physics Proceedings |
| Proceedings issue: | The 2nd European AI for Fundamental Physics Conference (EuCAIFCon2025) |
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| Academic field: | Physics |
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The author(s) disclose that the following generative AI tools have been used in the preparation of this submission:
ChatGPT (OpenAI, GPT-5) was used to correct grammar and improve clarity of text.
Abstract
The Surface Detector (SD) of the Pierre Auger Observatory is a 3000 km$^2$ 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.