8 papers
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…
Simulation-Based Inference for Direction Reconstruction of Ultra-High-Energy Cosmic Rays with Radio Arrays
Oscar Macias, Zachary Mason, Matthew Ho +3
Ultra-high-energy cosmic-ray (UHECR) observatories require unbiased direction reconstruction to enable multi-messenger astronomy with sparse, nanosecond-scale radio pulses. Explici…
Deep-Learning Denoising of Radio Signals for Ultra-High-Energy Cosmic-Ray Detection
Zhisen Lai, Oscar Macias, Aurélien Benoit-Lévy +5
Extensive air showers initiated by ultra-high-energy cosmic rays (UHECRs) produce broadband radio pulses detectable over large areas, but Galactic and instrumental backgrounds limi…
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…