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

• URL: http://arxiv.org/abs/2504.11720v1
• Date: Wed, 16 Apr 2025 02:45:00 GMT
• Title: Polarisation-Inclusive Spiking Neural Networks for Real-Time RFI Detection in Modern Radio Telescopes
• Authors: Nicholas J. Pritchard, Andreas Wicenec, Richard Dodson, Mohammed Bennamoun,
• Abstract summary: Spiking Neural Networks (SNNs) offer a promising solution for real-time RFI detection.<n>Preliminary results demonstrate state-of-the-art detection accuracy and highlight possible extensive energy-efficiency gains.
• Score: 25.08630315149258
• License: http://creativecommons.org/licenses/by/4.0/
• 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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