Spiking Neural Networks for Radio Frequency Interference Detection in Radio Astronomy

Nicholas J. Pritchard; Andreas Wicenec; Mohammed Bennamoun; Richard Dodson

doi:10.1038/s42005-025-02420-7

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arXiv · 2412.06124

Spiking Neural Networks for Radio Frequency Interference Detection in Radio Astronomy

Nicholas J. Pritchard·Andreas Wicenec·Mohammed Bennamoun·Richard Dodson

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Abstract

Spiking Neural Networks (SNNs) promise efficient and dynamic spatio-temporal data processing. This paper reformulates a significant challenge in radio astronomy, Radio Frequency Interference (RFI) detection, as a time-series segmentation task suited for SNN execution. Automated systems capable of real-time operation with minimal energy consumption are increasingly important in modern radio telescopes. We explore several spectrogram encoding methods and network parameters, applying first and second-order leaky integrate and fire SNNs to tackle RFI detection. We introduce a divisive normalisation-inspired pre-processing step, improving detection performance across multiple encodings strategies. Our approach achieves competitive performance on a synthetic dataset and compelling initial results on real data from the Low-Frequency Array (LOFAR). We position SNNs as a viable path towards real-time RFI detection, with many possibilities for follow-up studies. These findings highlight the potential for SNNs performing complex time-series tasks, paving the way towards efficient, real-time processing in radio astronomy and other data-intensive fields.

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Nicholas J. Pritchard, Andreas Wicenec, Mohammed Bennamoun, Richard Dodson. 2024-12-09. Spiking Neural Networks for Radio Frequency Interference Detection in Radio Astronomy. https://doi.org/10.1038/s42005-025-02420-7

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