29 citations · 79 across the 4 of their papers we have counts for
4 papers
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…
Improving Novelty Detection using the Reconstructions of Nearest Neighbours
Michael Mesarcik, Elena Ranguelova, Albert-Jan Boonstra +1
We show that using nearest neighbours in the latent space of autoencoders (AE) significantly improves performance of semi-supervised novelty detection in both single and multi-clas…
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…