AI Boosts Detection of Cosmic Rays by 500%, Sharpening Antarctic Observatory
Researchers used machine learning to filter noise from radio signals at the South Pole, quintupling the number of detectable cosmic-ray events. The breakthrough shows how neural networks can extract signal from chaos in extreme environments—a technique with applications for satellite networks, remote sensing, and any system struggling with interference.
Originaltitel: Identification and denoising of radio signals from cosmic-ray air showers using convolutional neural networks
<p>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 frombackground noise and a denoiser to clean contaminated radio signals. Following the training and testing phases, we applied the networks to airshower 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 coincidencewith 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 referencemethod. Additionally, wefind 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.</p>