Deep-Learning Denoising of Radio Observations for Ultra-High-Energy Cosmic-Ray Detection

Ultra-high-energy cosmic rays (UHECRs) can be detected via the broadband radio pulses produced by their extensive air showers. The Giant Radio Array for Neutrino Detection (GRAND) is a planned radio observatory that aims to deploy autonomous antenna arrays over areas of order $\sim 10^5\,\mathrm{km}^2$ to detect this emission. However, Galactic and instrumental radio backgrounds make the identification of low signal-to-noise ratio (SNR) pulses a central challenge. Here, we present a deep convolutional denoiser model that jointly processes each GRAND antenna trace in the time and frequency domains, allowing the network to learn transient pulse morphology and broadband spectral features while suppressing background noise. By training the model on $4.1\times 10^5$ simulated traces that include detailed UHECR radio emission and realistic detector response and noise, we find a median output-SNR improvement of $\sim 15-23\,\mathrm{dB}$ in the $50-200~\mathrm{MHz}$ band and a reduction of the normalized mean squared error of the waveform by about an order of magnitude relative to a Hilbert-envelope denoiser baseline. We also verify that applying the denoiser to noise-only windows does not produce spurious pulse candidates. Near the detection threshold, the denoiser increases the number of antennas contributing reliable pulse timing by a factor of $\sim 2-3$, which correspondingly tightens direction reconstruction uncertainties. When we additionally require accurate recovery of the waveform shape, the denoiser yields a median gain of $\sim 3-4$ antennas usable for energy reconstruction at SNR$\simeq 5-6$, strengthening event-level direction and energy estimates in sparse radio arrays.