13 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…
Deep-Learning Denoising of Radio Signals for Ultra-High-Energy Cosmic-Ray Detection
Zhisen Lai, Oscar Macias, Aurélien Benoit-Lévy +2
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…
Radio Morphing: Fast computation of inclined air shower radio emission
Simon Chiche, Olivier Martineau-Huynh, Matias Tueros +1
The preparation of the next-generation of large-scale radio experiments requires running a large number of simulations to explore multiple detector configurations over vast areas a…
Towards the Giant Radio Array for Neutrino Detection (GRAND): the GRANDProto300 and GRAND@Auger prototypes
GRAND Collaboration, Jaime Álvarez-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…
End-to-end reconstruction of ultra-high energy particle observables from radio detection of extensive air showers
Kewen Zhang, Duan Kaikai, Ramesh Koirala +3
The radio detection of very inclined air showers offers a promising avenue for studying ultra-high-energy cosmic rays (UHECRs) and neutrinos. Accurate reconstruction methods are es…