Detecting and mitigating Radio Frequency Interference (RFI) is critical for enabling and maximising the scientific output of radio telescopes. The emergence of machine learning methods has led to their application in radio astronomy, and in RFI detection. Spiking Neural Networks (SNNs), inspired by biological systems, are well-suited for processing spatio-temporal data. This study introduces the first exploratory 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. Our subsequent evaluation aims to determine whether SNNs are viable for future RFI detection schemes. We evaluate detection performance with the simulated HERA telescope and hand-labelled LOFAR observation dataset the original authors provided. We additionally evaluate detection performance with a new MeerKAT-inspired simulation dataset that provides a technical challenge for machine-learnt RFI detection methods. This dataset focuses on satellite-based RFI, an increasingly important class of RFI and is an additional contribution. Our approach remains competitive with existing methods in AUROC, AUPRC and F1 scores for the HERA dataset but exhibits difficulty in the LOFAR and Tabascal datasets. Our method maintains this accuracy 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：检测并缓解射频干扰（RFI）对于提升射电望远镜的科学产出至关重要。机器学习方法的兴起推动了其在射电天文学及RFI检测领域的应用。受生物系统启发的脉冲神经网络（SNNs）擅长处理时空数据。本研究首次探索性地将SNNs应用于天文数据处理任务——具体为RFI检测。我们通过直接的ANN2SNN转换，将前人提出的最近潜在邻居（NLN）算法与自编码器架构适配至SNN执行，通过采样内部脉冲神经元自然变化的潜在空间，实现简化的下游RFI检测。后续评估旨在确定SNNs在未来RFI检测方案中的可行性。我们使用模拟HERA望远镜数据及原作者提供的手工标注LOFAR观测数据集评估检测性能，并额外引入基于MeerKAT启发的模拟数据集（聚焦日益重要的卫星射频干扰）作为技术挑战与贡献。在HERA数据集上，本方法在AUROC、AUPRC及F1分数上均能与现有方法保持竞争力，但在LOFAR及Tabascal数据集中表现困难。该方法在完全移除NLN中计算与内存密集的潜在采样步骤的同时，仍能维持检测精度。本工作通过建立传统及新兴卫星RFI源的最低性能基线，证明了SNNs作为射电望远镜中基于机器学习的RFI检测手段的可行性，据我们所知，这也是SNNs在天文学中的首次应用。