Supervised Radio Frequency Interference Detection with SNNs

Radio Frequency Interference (RFI) poses a significant challenge in radio astronomy, arising from terrestrial and celestial sources, disrupting observations conducted by radio telescopes. Addressing RFI involves intricate heuristic algorithms, manual examination, and, increasingly, machine learning...

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Bibliographic Details

Published in:	2024 International Conference on Neuromorphic Systems (ICONS) pp. 102 - 109
Main Authors:	Pritchard, Nicholas J., Wicenec, Andreas, Bennamoun, Mohammed, Dodson, Richard
Format:	Conference Proceeding
Language:	English
Published:	IEEE 30.07.2024
Subjects:	Accuracy Benchmark testing Encoding multi-variate time-series segmentation Network architecture neuromorphic computing Neuromorphic engineering Optimization Radio astronomy Radiofrequency interference Spiking neural networks supervised learning Telescopes
Online Access:	Get full text
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