Machine learning-based anomaly detection for radio telescopes

Radio telescopes are getting bigger and are generating increasing amounts of data to improve their sensitivity and resolution. The growing system size and resulting complexity increases the likelihood of unexpected events occurring thereby producing datasets containing anomalies. These events include failures in instrument electronics, miscalibrated observations, environmental and astronomical effects such as lightning and solar storms as well as problems in data processing systems among many more. Currently, efforts to diagnose and mitigate these events are performed by human operators, who manually inspect intermediate data products to determine the success or failure of a given observation. The accelerating data-rates coupled with the lack of automation results in operator-based data quality inspection becoming increasingly infeasible.
This thesis focuses on applying machine learning-based anomaly detection to spectrograms obtained from the LOFAR telescope for the purpose of System Health Management (SHM). It does this across several chapters, with each chapter focusing on a different aspect of SHM in radio telescopes. We provide an overview of the data processing systems in LOFAR so to create a workflow for SHM that could effectively be integrated into the scientific data processing pipeline.