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Discovery of peculiar radio morphologies with ASKAP using unsupervised machine learning

Journal Article · · Publications of the Astronomical Society of Australia
DOI:https://doi.org/10.1017/pasa.2022.44· OSTI ID:2419771
Abstract

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 Organization:
Univ. of Pittsburgh, PA (United States)
Sponsoring Organization:
USDOE Office of Science (SC)
DOE Contract Number:
SC0007914
OSTI ID:
2419771
Journal Information:
Publications of the Astronomical Society of Australia, Journal Name: Publications of the Astronomical Society of Australia Vol. 39; ISSN 1323-3580
Publisher:
CSIRO
Country of Publication:
United States
Language:
English

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