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Discovery of peculiar radio morphologies with ASKAP using unsupervised machine learning

Published online by Cambridge University Press:  20 October 2022

Nikhel Gupta*
CSIRO Space & Astronomy, PO Box 1130, Bentley, WA 6102, Australia
Minh Huynh
CSIRO Space & Astronomy, PO Box 1130, Bentley, WA 6102, Australia International Centre for Radio Astronomy Research (ICRAR), M468, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia
Ray P. Norris
Western Sydney University, Locked Bag 1797, Penrith, NSW 2751, Australia CSIRO Space & Astronomy, P.O. Box 76, Epping, NSW 1710, Australia
X. Rosalind Wang
Western Sydney University, Locked Bag 1797, Penrith, NSW 2751, Australia
Andrew M. Hopkins
Western Sydney University, Locked Bag 1797, Penrith, NSW 2751, Australia Australian Astronomical Optics, Macquarie University, 105 Delhi Rd, North Ryde, NSW 2113, Australia
Heinz Andernach
Depto. de Astronomía, DCNE, Universidad de Guanajuato, Cjón. de Jalisco s/n, Guanajuato, CP 36023, Mexico
Bärbel S. Koribalski
Western Sydney University, Locked Bag 1797, Penrith, NSW 2751, Australia CSIRO Space & Astronomy, P.O. Box 76, Epping, NSW 1710, Australia
Tim J. Galvin
International Centre for Radio Astronomy Research, Curtin University, Bentley, WA 6102, Australia
Corresponding author: Nikhel Gupta, Email:


We present a set of peculiar radio sources detected using an unsupervised machine learning method. We use data from the Australian Square Kilometre Array Pathfinder (ASKAP) telescope to train a self-organizing map (SOM). The radio maps from three ASKAP surveys, Evolutionary Map of Universe pilot survey (EMU-PS), Deep Investigation of Neutral Gas Origins pilot survey (DINGO), and Survey With ASKAP of GAMA-09 + X-ray (SWAG-X), are used to search for the rarest or unknown radio morphologies. We use an extension of the SOM algorithm that implements rotation and flipping invariance on astronomical sources. The SOM is trained using the images of all ‘complex’ radio sources in the EMU-PS which we define as all sources catalogued as ‘multi-component’. The trained SOM is then used to estimate a similarity score for complex sources in all surveys. We select 0.5% of the sources that are most complex according to the similarity metric and visually examine them to find the rarest radio morphologies. Among these, we find two new odd radio circle (ORC) candidates and five other peculiar morphologies. We discuss multiwavelength properties and the optical/infrared counterparts of selected peculiar sources. In addition, we present examples of conventional radio morphologies including: diffuse emission from galaxy clusters, and resolved, bent-tailed, and FR-I and FR-II type radio galaxies. We discuss the overdense environment that may be the reason behind the circular shape of ORC candidates.

Research Article
© The Author(s), 2022. Published by Cambridge University Press on behalf of the Astronomical Society of Australia

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