Spiking Neural Networks for Radio Frequency Interference Detection in Radio Astronomy
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917216154288128 |
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| author | Pritchard, Nicholas J. Wicenec, Andreas Bennamoun, Mohammed Dodson, Richard |
| author_facet | Pritchard, Nicholas J. Wicenec, Andreas Bennamoun, Mohammed Dodson, Richard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06124 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spiking Neural Networks for Radio Frequency Interference Detection in Radio Astronomy Pritchard, Nicholas J. Wicenec, Andreas Bennamoun, Mohammed Dodson, Richard Neural and Evolutionary Computing Instrumentation and Methods for Astrophysics 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. |
| title | Spiking Neural Networks for Radio Frequency Interference Detection in Radio Astronomy |
| topic | Neural and Evolutionary Computing Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2412.06124 |