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 two-layer 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.

翻译：射频干扰（RFI）源自地球及天体源，对射电望远镜观测造成严重干扰，是射电天文学面临的重大挑战。应对RFI需要复杂的启发式算法、人工核查，以及日益增多的机器学习方法。鉴于射电天文观测的动态时间特性，脉冲神经网络（SNN）成为一种极具前景的研究方向。本研究将RFI检测视为有监督多变量时间序列分割问题，重点探究了适用于SNN推理的射电天文可见度数据编码方案，涵盖六种编码策略：速率编码、延迟编码、增量调制编码及三种步进算法变体。我们利用基于氢再电离纪元阵列（HERA）望远镜的模拟数据，训练了一个小型双层全连接SNN，并开展了广泛的超参数优化。结果表明，延迟编码表现最优，实现了每像素98.8%的准确率和0.761的F1分数。值得注意的是，尽管所提出的网络架构简洁紧凑，这些指标已接近当前主流RFI检测算法的性能水平。本研究凸显了RFI检测作为SNN基准测试问题的潜力，充分证实了SNN在解决射电天文学复杂时间序列分割任务中的有效性。