2 citations · 2 across the 4 of their papers we have counts for
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Deep ensemble graph neural networks for probabilistic cosmic-ray direction and energy reconstruction in autonomous radio arrays
Arsène Ferrière, Aurélien Benoit-Lévy, Olivier Martineau-Huynh +1
Using advanced machine learning techniques, we developed a method for reconstructing precisely the arrival direction and energy of ultra-high-energy cosmic rays from the voltage tr…
Towards the Giant Radio Array for Neutrino Detection (GRAND): the GRANDProto300 and GRAND@Auger prototypes
GRAND Collaboration, Jaime Ãlvarez-Muniz, Rafael Alves Batista +118
The Giant Radio Array for Neutrino Detection (GRAND) is a proposed multi-messenger observatory of Ultra-High-Energy (UHE) particles of cosmic origin. Its main goal is to find the l…
Reconstruction of cosmic-ray properties with GNN in GRAND
Arsène Ferrière, Aurélien Benoit-Lévy
The Giant Radio Array for Neutrino Detection (GRAND) aims to detect and study ultra-high-energy (UHE) neutrinos by observing the radio emissions produced in extensive air showers.…
The Giant Radio Array for Neutrino Detection (GRAND) Collaboration -- Contributions to the 39th International Cosmic Ray Conference (ICRC 2025)
Jaime Ãlvarez-Muñiz, Rafael Alves Batista, Aurélien Benoit-Lévy +133
The Giant Radio Array for Neutrino Detection (GRAND) is an envisioned observatory of ultra-high-energy particles of cosmic origin, with energies in excess of 100 PeV. GRAND uses la…
Denoising radio pulses from air showers using machine-learning methods
Aurélien Benoit-Lévy, Zhisen Lai, Oscar Macias +1
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, har…