Radio signal generation in milliseconds: enabling multi-parameter reconstruction of ultra-high-energy cosmic rays

TL;DR

This paper introduces a machine-learning emulator for radio signals from ultra-high-energy cosmic rays, enabling rapid and accurate reconstruction of cosmic-ray properties from measured data.

Contribution

The work presents a novel machine-learning-based emulator that reproduces detailed radio signal simulations in milliseconds, significantly speeding up cosmic-ray analysis.

Findings

Achieves 8.9% resolution on electromagnetic energy

Attains 0.08° angular resolution

Successfully reconstructs cosmic-ray candidates from real data

Abstract

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{\deg} 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.