| Description: | In modern radio telescopes such as LOFAR, system health management is crucial for early detection of errors and for remedying them. As radio telescope data volumes are ever increasing, manually searching for system errors is becoming untenable. AI approaches to detect and classify error patterns are potentially much more accurate and complete than the manual inspection route. In the Efficient Deep Learning (EDL) PhD project at the UvA and ASTRON, Michael Mesarcik developed AI tools to detect and cluster different error patterns and regular events in LOFAR spectrogram data. The picture shows an example of nine clusters of event types that were detected with high accuracy. Following this success, a pilot proposal was presented to the LOFAR stakeholders and is awaiting implementation and testing. All details can be found in Michael Mesarcik's thesis (*) which he successfully defended Wednesday April 24. Misha, congrats!
(*) https://hdl.handle.net/11245.1/4e90ce44-5d87-402b-a33c-60297be96dae |
