29 citations · 79 across the 4 of their papers we have counts for
3 papers · 1 filter
The ROAD to discovery: machine learning-driven anomaly detection in radio astronomy spectrograms
Michael Mesarcik, Albert-Jan Boonstra, Marco Iacobelli +3
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 incre…
Learning to detect RFI in radio astronomy without seeing it
Michael Mesarcik, Albert-Jan Boonstra, Elena Ranguelova +1
Radio Frequency Interference (RFI) corrupts astronomical measurements, thus affecting the performance of radio telescopes. To address this problem, supervised segmentation models h…
Deep Learning Assisted Data Inspection for Radio Astronomy
Michael Mesarcik, Albert-Jan Boonstra, Christiaan Meijer +3
Modern radio telescopes combine thousands of receivers, long-distance networks, large-scale compute hardware, and intricate software. Due to this complexity, failures occur relativ…