Radio Frequency Interference (RFI) detection and mitigation is critical for enabling and maximising the scientific output of radio telescopes. The emergence of machine learning methods capable of handling large datasets has led to their application in radio astronomy, particularly in RFI detection. Spiking Neural Networks (SNNs), inspired by biological systems, are well-suited for processing spatio-temporal data. This study introduces the first application of SNNs to an astronomical data-processing task, specifically RFI detection. We adapt the nearest-latent-neighbours (NLN) algorithm and auto-encoder architecture proposed by previous authors to SNN execution by direct ANN2SNN conversion, enabling simplified downstream RFI detection by sampling the naturally varying latent space from the internal spiking neurons. We evaluate performance with the simulated HERA telescope and hand-labelled LOFAR dataset that the original authors provided. We additionally evaluate performance with a new MeerKAT-inspired simulation dataset. This dataset focuses on satellite-based RFI, an increasingly important class of RFI and is, therefore, an additional contribution. Our SNN approach remains competitive with the original NLN algorithm and AOFlagger in AUROC, AUPRC and F1 scores for the HERA dataset but exhibits difficulty in the LOFAR and MeerKAT datasets. However, our method maintains this performance while completely removing the compute and memory-intense latent sampling step found in NLN. This work demonstrates the viability of SNNs as a promising avenue for machine-learning-based RFI detection in radio telescopes by establishing a minimal performance baseline on traditional and nascent satellite-based RFI sources and is the first work to our knowledge to apply SNNs in astronomy.

翻译：射频干扰（RFI）的检测与抑制对于保障和最大化射电望远镜的科学产出至关重要。能够处理大规模数据集的机器学习方法的出现，推动了其在射电天文学中的应用，特别是在RFI检测领域。受生物系统启发的脉冲神经网络（SNN）非常适合处理时空数据。本研究首次将SNN应用于天文数据处理任务，具体为RFI检测。我们通过直接的人工神经网络到脉冲神经网络（ANN2SNN）转换，将先前研究者提出的最近邻潜在（NLN）算法和自编码器架构适配至SNN执行，通过从内部脉冲神经元的自然变化隐空间采样，实现了简化的下游RFI检测。我们使用原始研究者提供的模拟HERA望远镜和人工标注的LOFAR数据集评估性能，同时基于新型MeerKAT模拟数据集（聚焦于日益重要的卫星RFI类别）进行额外评估，这构成了本研究的另一项贡献。在HERA数据集上，我们的SNN方法在AUROC、AUPRC和F1评分方面与原始NLN算法及AOFlagger保持竞争力，但在LOFAR和MeerKAT数据集中表现存在困难。然而，该方法在维持性能的同时，完全消除了NLN中计算与内存密集的隐采样步骤。本研究通过建立传统及新兴卫星RFI源的最小性能基线，证明了SNN作为射电望远镜中基于机器学习的RFI检测可行方向的有效性，据我们所知，这也是首次将SNN应用于天文学领域的研究工作。