A unique, ring-like radio source with quadrilateral structure detected with machine learning

Author:

Lochner M12ORCID,Rudnick L3ORCID,Heywood I245ORCID,Knowles K25ORCID,Shabala S S6ORCID

Affiliation:

1. Department of Physics and Astronomy, University of the Western Cape , Bellville, Cape Town 7535, South Africa

2. South African Radio Astronomy Observatory , 2 Fir Street, Black River Park, Observatory, Cape Town 7925, South Africa

3. Minnesota Institute for Astrophysics, University of Minnesota , 116 Church St SE, Minneapolis, MN 55455, USA

4. Astrophysics, University of Oxford , Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK

5. Centre for Radio Astronomy Techniques and Technologies, Department of Physics and Electronics, Rhodes University , PO Box 94, Makhanda 6140, South Africa

6. School of Natural Sciences , Private Bag 37, University of Tasmania, Hobart, TAS 7001, Australia

Abstract

ABSTRACT We report the discovery of a unique object in the MeerKAT Galaxy Cluster Legacy Survey (MGCLS) using the machine learning anomaly detection framework astronomaly. This strange, ring-like source is 30′ from the MGCLS field centred on Abell 209, and is not readily explained by simple physical models. With an assumed host galaxy at redshift 0.55, the luminosity (1025 W Hz−1) is comparable to powerful radio galaxies. The source consists of a ring of emission 175 kpc across, quadrilateral enhanced brightness regions bearing resemblance to radio jets, two ‘ears’ separated by 368 kpc, and a diffuse envelope. All of the structures appear spectrally steep, ranging from −1.0 to −1.5. The ring has high polarization (25 per cent) except on the bright patches (<10 per cent). We compare this source to the Odd Radio Circles recently discovered in ASKAP data and discuss several possible physical models, including a termination shock from starburst activity, an end-on radio galaxy, and a supermassive black hole merger event. No simple model can easily explain the observed structure of the source. This work, as well as other recent discoveries, demonstrates the power of unsupervised machine learning in mining large data sets for scientifically interesting sources.

Funder

National Research Foundation

Publisher

Oxford University Press (OUP)

Subject

Space and Planetary Science,Astronomy and Astrophysics

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