Supervised Radio Frequency Interference Detection with SNNs

TL;DR

This paper demonstrates that small, simple Spiking Neural Networks can effectively detect radio frequency interference in radio astronomy data, achieving high accuracy with various encoding schemes, especially latency encoding.

Contribution

The study introduces a supervised SNN approach for RFI detection, exploring multiple encoding schemes and achieving near state-of-the-art performance with a compact network architecture.

Findings

Latency encoding outperforms other schemes with 98.8% accuracy.

The SNN approach approaches the performance of existing algorithms.

The network architecture is simple and highly effective.

Abstract

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 methods. Given the dynamic and temporal nature of radio astronomy observations, Spiking Neural Networks (SNNs) emerge as a promising approach. In this study, we cast RFI detection as a supervised multi-variate time-series segmentation problem. Notably, our investigation explores the encoding of radio astronomy visibility data for SNN inference, considering six encoding schemes: rate, latency, delta-modulation, and three variations of the step-forward algorithm. We train a small twolayer fully connected SNN on simulated data derived from the Hydrogen Epoch of Reionization Array (HERA) telescope and perform extensive hyper-parameter optimization. Results reveal that latency encoding exhibits superior performance, achieving a per-pixel accuracy of 98.8% and an f1-score of 0.761. Remarkably, these metrics approach those of contemporary RFI detection algorithms, notwithstanding the simplicity and compactness of our proposed network architecture. This study underscores the potential of RFI detection as a benchmark problem for SNN researchers, emphasizing the efficacy of SNNs in addressing complex time-series segmentation tasks in radio astronomy.