Search for Cosmic-Ray Events Using Radio Signals and CNNs in Data from the IceTop Enhancement Prototype Station

Cosmic-ray air showers emit radio waves that can be used to measure the properties of cosmic-ray primary particles. The radio detection technique presents several advantages, such as low cost and year-round duty cycle as well as the ability to provide high sensitivity to X_max and energy estimation with minimal theoretical uncertainties, making it a promising tool for studying cosmic rays at the highest energies. However, the primary limitation of radio detection is the irreducible background from various sources that obscure the impulsive signals generated by air showers. To address this issue, we investigated the use of Convolutional Neural Networks (CNNs), trained on CoREAS simulations and radio backgrounds measured by a prototype station at the South Pole. We developed two different CNNs: a Classifier that distinguishes between cosmic ray event radio signals and pure background waveforms, and a Denoiser that mitigates background noise to recover the underlying cosmic-ray signal. After training the networks we apply them to the air-shower data to search for radio events. With two months data, we were able to identify 51 candidate events. The event’s arrival direction reconstructed using CNN denoised radio waveforms is found to be in good agreement with the IceTop reconstruction. Finally, our approach demonstrated improved directional reconstruction compared to traditional methods.