Year: 2025
Type: conference-paper
Abstract: 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.
DOI: 10.22323/1.501.0185
Institutions: Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Université Paris-Saclay, CEA Paris-Saclay - Etablissement de Saclay, Laboratoire d'Intégration des Systèmes et des Technologies, Centre National de la Recherche Scientifique, Sorbonne Université, Laboratoire de Physique Nucléaire et de Hautes Énergies, San Francisco State University, Universidade de Santiago de Compostela, Sorbonne Paris Cité, Université Paris Diderot
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
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
Awards: G1228348679, Astroparticles and pulsars: connections from low to ultrahigh energies, G196419191, G2293383998, New trigger: novel techniques for pure, efficient and scalable autonomous radio detection of high energy neutrinos and cosmic rays, NUTRIG: neuartige Methoden für reine, effiziente und skalierbare autonome Radiodetektion hochenergetischer Neutrinos und kosmischer Strahlung, G430030528, G4409852737, G6141822678, G6649312515, G7321284446, G8543737702, LEAPS-MPS: Unveiling the Ultra-High-Energy Universe with the Giant Radio Array for Neutrino Detection (GRAND), G8747812308, G92209578
OpenAlex ID: W4414420423
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