Polarisation-Inclusive Spiking Neural Networks for Real-Time RFI Detection in Modern Radio Telescopes

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

doi:10.46620/25-0006

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arXiv · 2504.11720

Polarisation-Inclusive Spiking Neural Networks for Real-Time RFI Detection in Modern Radio Telescopes

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

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Abstract

Radio Frequency Interference (RFI) is a known growing challenge for radio astronomy, intensified by increasing observatory sensitivity and prevalence of orbital RFI sources. Spiking Neural Networks (SNNs) offer a promising solution for real-time RFI detection by exploiting the time-varying nature of radio observation and neuron dynamics together. This work explores the inclusion of polarisation information in SNN-based RFI detection, using simulated data from the Hydrogen Epoch of Reionisation Array (HERA) instrument and provides power usage estimates for deploying SNN-based RFI detection on existing neuromorphic hardware. Preliminary results demonstrate state-of-the-art detection accuracy and highlight possible extensive energy-efficiency gains.

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Nicholas J. Pritchard, Andreas Wicenec, Richard Dodson, Mohammed Bennamoun. 2025-04-16. Polarisation-Inclusive Spiking Neural Networks for Real-Time RFI Detection in Modern Radio Telescopes. https://doi.org/10.46620/25-0006

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