Identification and Denoising of Radio Signals from Cosmic-Ray Air Showers using Convolutional Neural Networks

TL;DR

This paper demonstrates that convolutional neural networks can effectively identify and denoise radio signals from cosmic-ray air showers, significantly improving detection sensitivity and accuracy over traditional methods.

Contribution

The authors develop CNN-based classifiers and denoisers that enhance cosmic-ray radio detection, reducing thresholds and false positives compared to existing techniques.

Findings

CNNs identified five times more cosmic-ray events than traditional methods.

CNN denoising improved the accuracy of signal power and timing reconstruction.

The approach reliably distinguished cosmic-ray signals from background noise.

Abstract

Radio pulses generated by cosmic-ray air showers can be used to reconstruct key properties like the energy and depth of the electromagnetic component of cosmic-ray air showers. Radio detection threshold, influenced by natural and anthropogenic radio background, can be reduced through various techniques. In this work, we demonstrate that convolutional neural networks (CNNs) are an effective way to lower the threshold. We developed two CNNs: a classifier to distinguish radio signal waveforms from background noise and a denoiser to clean contaminated radio signals. Following the training and testing phases, we applied the networks to air-shower data triggered by scintillation detectors of the prototype station for the enhancement of IceTop, IceCube's surface array at the South Pole. Over a four-month period, we identified 554 cosmic-ray events in coincidence with IceTop, approximately five times more compared to a reference method based on a cut on the signal-to-noise ratio. Comparisons with IceTop measurements of the same air showers confirmed that the CNNs reliably identified cosmic-ray radio pulses and outperformed the reference method. Additionally, we find that CNNs reduce the false-positive rate of air-shower candidates and effectively denoise radio waveforms, thereby improving the accuracy of the power and arrival time reconstruction of radio pulses.