Spiking Neural Networks for Radio Frequency Interference Detection in Radio Astronomy

TL;DR

This paper demonstrates that Spiking Neural Networks can effectively perform real-time Radio Frequency Interference detection in radio astronomy, offering energy-efficient and dynamic processing suitable for modern telescopes.

Contribution

It reformulates RFI detection as a time-series segmentation task for SNNs, introduces a normalization pre-processing step, and shows promising results on real and synthetic data.

Findings

Achieved competitive detection performance on synthetic data.

Obtained promising initial results on LOFAR real data.

Highlighted SNNs as a viable approach for real-time RFI detection.

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.