Denoising radio pulses from air showers using machine-learning methods
The Giant Radio Array for Neutrino Detection (GRAND) aims to detect radio signals from extensive air showers (EAS) caused by ultra-high-energy (UHE) cosmic particles. Galactic, hardware-like, and anthropogenic noise are expected to contaminate these signals. To address this problem, we propose training a supervised convolutional network known as an encoder-decoder. This network is used to learn a coded representation of the data and remove specific features from it. This denoiser is trained using high-fidelity air shower simulations specifically tailored to replicate the characteristics of signals detected by GRAND. In this contribution, we describe our machine-learning model and report initial results demonstrating the sensitivity enhancement resulting from our denoising algorithm when applied to realistically simulated GRAND signals with varying signal-to-noise ratios.
Authors
- Oscar Macías (ORCID: https://orcid.org/0000-0001-8867-2693)
- A. Benoit-Lévy (ORCID: https://orcid.org/0000-0003-1801-6409)
- Arsène Ferrière (ORCID: https://orcid.org/0009-0001-2630-6679)
- Zhisen Lai
Publication Details
- Published
- 2025-09-23
- DOI
- https://doi.org/10.22323/1.501.0185
- Primary Topic
- Astrophysics and Cosmic Phenomena
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- Villum Fonden
- Deutsche Forschungsgemeinschaft
- Agence Nationale de la Recherche
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
- Chinese Academy of Sciences
- Centre National d’Etudes Spatiales
- Conselho Nacional de Desenvolvimento Científico e Tecnológico
- China Scholarship Council
- Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro
- Centre National de la Recherche Scientifique
- Narodowa Agencja Wymiany Akademickiej
- Sorbonne Université
- Institut National de Physique Nucléaire et de Physique des Particules
- National Astronomical Observatories, Chinese Academy of Sciences