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

TL;DR

This paper reports the discovery of a unique, complex radio source with a ring-like and quadrilateral structure in a galaxy cluster, identified using machine learning anomaly detection, challenging existing physical models.

Contribution

It introduces a novel application of unsupervised machine learning to discover unusual astrophysical objects in large radio survey datasets.

Findings

Discovery of a unique, quadrilateral, ring-like radio source with complex morphology.

Spectrally steep emission with high polarization in the source.

Discussion of multiple physical models, none of which fully explain the observed structure.

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 (10^25 W/Hz) 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%) except on the bright patches (<10%). 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 datasets for scientifically interesting sources.