As radio telescopes increase in sensitivity and flexibility, so do their complexity and data-rates. For this reason automated system health management approaches are becoming increasingly critical to ensure nominal telescope operations. We propose a new machine learning anomaly detection framework for classifying both commonly occurring anomalies in radio telescopes as well as detecting unknown rare anomalies that the system has potentially not yet seen. To evaluate our method, we present a dataset consisting of 7050 autocorrelation-based spectrograms from the Low Frequency Array (LOFAR) telescope and assign 10 different labels relating to the system-wide anomalies from the perspective of telescope operators. This includes electronic failures, miscalibration, solar storms, network and compute hardware errors among many more. We demonstrate how a novel Self Supervised Learning (SSL) paradigm, that utilises both context prediction and reconstruction losses, is effective in learning normal behaviour of the LOFAR telescope. We present the Radio Observatory Anomaly Detector (ROAD), a framework that combines both SSL-based anomaly detection and a supervised classification, thereby enabling both classification of both commonly occurring anomalies and detection of unseen anomalies. We demonstrate that our system is real-time in the context of the LOFAR data processing pipeline, requiring <1ms to process a single spectrogram. Furthermore, ROAD obtains an anomaly detection F-2 score of 0.92 while maintaining a false positive rate of ~2\%, as well as a mean per-class classification F-2 score 0.89, outperforming other related works.

翻译：随着射电望远镜灵敏度和灵活性的提升，其复杂性和数据速率也相应增加。因此，自动化系统健康管理方法对确保望远镜标称运行变得日益关键。我们提出了一种新的机器学习异常检测框架，用于分类射电望远镜中常见的异常，同时检测系统可能尚未见过的未知稀有异常。为评估该方法，我们构建了一个数据集，包含来自低频阵列（LOFAR）望远镜的7050张基于自相关的频谱图，并从望远镜操作员视角分配了10种与系统级异常相关的标签，涵盖电子故障、校准错误、太阳风暴、网络与计算硬件错误等。我们证明了一种新颖的自监督学习（SSL）范式——结合上下文预测与重建损失——能有效学习LOFAR望远镜的正常行为。我们提出射电天文台异常检测器（ROAD）框架，它融合了基于SSL的异常检测与监督分类，从而既能对常见异常进行分类，也能检测未知异常。实验表明，在LOFAR数据处理流水线中，我们的系统可实现实时处理，单张频谱图处理时间低于1毫秒。此外，ROAD在保持约2%假阳性率的同时，获得了0.92的异常检测F-2分数，以及0.89的每类平均分类F-2分数，性能优于现有相关工作。