Work

Spiking neural networks for radio frequency interference detection in radio astronomy

Year: 2025

Type: article

Abstract: Automated systems capable of real-time operation with minimal energy consumption are increasingly important in modern radio telescopes. 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. 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) establishing a baseline for future work. 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.

Source: Communications Physics

DOI: 10.1038/s42005-025-02420-7

Authors: Nicholas J. Pritchard, Andreas Wicenec, Mohammed Bennamoun, Richard Dodson

Institutions: The University of Western Australia, International Centre for Radio Astronomy Research

Language: English

Topic: Radio Astronomy Observations and Technology

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,990

Funders: Australian Government, Government of Western Australia, Westpac Scholars Trust