Arsène Ferrière

In recent years, radio detection of ultra-high-energy cosmic rays (UHECRs), with energies above $10^{18}$ eV, has become an established technique. The radio emissions can be simulated with high accuracy using Monte Carlo codes such as ZHAireS and CoREAS. These simulations are essential but are computationally intensive. In this work, we present a machine-learning-based emulator that reproduces radio signal simulations with high accuracy in milliseconds rather than hours. Primary particle properties can then be reconstructed by comparing measured signals to emulated traces using a Markov Chain Monte Carlo approach. Using ZHAireS simulations carried out over the GRANDProto300 experiment layout, the method achieves an 8.9\% resolution on electromagnetic energy and a 0.08° angular resolution, matching state-of-the-art reconstruction performance. Finally, we apply the method on real data, successfully reconstructing cosmic-ray candidates detected by the GP300 prototype.