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Radio Galaxy Zoo: compact and extended radio source classification with deep learning

Lukic, Vesna; Brüggen, M; Banfield, Julie; Wong, O Ivy; Rudnick, L; Norris, Ray P; Simmons, Brooke D


Machine learning techniques have been increasingly useful in astronomical applications overthe last few years, for example in the morphological classification of galaxies. Convolutionalneural networks have proven to be highly effective in classifying objects in image data. Inthe context of radio-interferometric imaging in astronomy, we looked for ways to identifymultiple components of individual sources. To this effect, we design a convolutional neuralnetwork to differentiate between different...[Show more]

CollectionsANU Research Publications
Date published: 2018
Type: Journal article
Source: Monthly Notices of the Royal Astronomical Society
DOI: 10.1093/mnras/sty163


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