65 citations · 67 across the 2 of their papers we have counts for
3 papers
astro-ph.IM2019★ 2 cited
ConvoSource: Radio-Astronomical Source-Finding with Convolutional Neural Networks
V. Lukic, F. De Gasperin, M. Brüggen
Finding and classifying astronomical sources is key in the scientific exploitation of radio surveys. Source-finding usually involves identifying the parts of an image belonging to…
astro-ph.IM2019★ 65 cited
Morphological classification of radio galaxies: Capsule Networks versus Convolutional Neural Networks
V. Lukic, M. Brüggen, B. Mingo +3
Next-generation radio surveys will yield an unprecedented amount of data, warranting analysis by use of machine learning techniques. Convolutional neural networks are the deep lear…
astro-ph.IM2018
Radio Galaxy Zoo: Compact and extended radio source classification with deep learning
V. Lukic, M. Brüggen, J. K. Banfield +4
Machine learning techniques have been increasingly useful in astronomical applications over the last few years, for example in the morphological classification of galaxies. Convolu…