162 citations · 1.2k across the 54 of their papers we have counts for
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astro-ph.IM2024★ 8 cited
Rotation and flipping invariant self-organizing maps with astronomical images: A cookbook and application to the VLA Sky Survey QuickLook images
A. N. Vantyghem, T. J. Galvin, B. Sebastian +12
Modern wide field radio surveys typically detect millions of objects. Techniques based on machine learning are proving to be useful for classifying large numbers of objects. The se…
astro-ph.IM2020★ 53 cited
Cataloging the radio-sky with unsupervised machine learning: a new approach for the SKA era
T. J. Galvin, M. Huynh, R. P. Norris +6
We develop a new analysis approach towards identifying related radio components and their corresponding infrared host galaxy based on unsupervised machine learning methods. By expl…