The ROAD to discovery: machine learning-driven anomaly detection in radio astronomy spectrograms

TL;DR

This paper introduces ROAD, a machine learning framework using self-supervised learning for real-time anomaly detection and classification in radio telescope spectrograms, significantly improving accuracy and speed.

Contribution

The paper presents a novel SSL-based anomaly detection framework that detects both known and unknown anomalies in radio telescope data, with real-time processing capabilities.

Findings

Achieved an anomaly detection F-2 score of 0.92

Maintains a false positive rate of ~2%

Processes spectrograms in under 1 millisecond

Abstract

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.