Cataloging the radio-sky with unsupervised machine learning: a new approach for the SKA era

TL;DR

This paper introduces an unsupervised machine learning approach using self-organising maps to catalog radio sources and their infrared hosts, enabling the identification of complex and rare radio morphologies without prior training labels.

Contribution

The authors develop a novel unsupervised method that associates radio and infrared sources, producing comprehensive catalogs and identifying unique radio galaxy morphologies in the SKA era.

Findings

Successfully cataloged over 800,000 objects with associated radio and infrared data.

Identified rare and complex radio morphologies such as X-shaped galaxies and giant radio galaxies.

Introduced a 'curliness' statistic to quantify bent and disturbed radio structures.

Abstract

We develop a new analysis approach towards identifying related radio components and their corresponding infrared host galaxy based on unsupervised machine learning methods. By exploiting PINK, a self-organising map algorithm, we are able to associate radio and infrared sources without the a priori requirement of training labels. We present an example of this method using $894,415$ images from the FIRST and WISE surveys centred towards positions described by the FIRST catalogue. We produce a set of catalogues that complement FIRST and describe 802,646 objects, including their radio components and their corresponding AllWISE infrared host galaxy. Using these data products we (i) demonstrate the ability to identify objects with rare and unique radio morphologies (e.g. 'X'-shaped galaxies, hybrid FR-I/FR-II morphologies), (ii) can identify the potentially resolved radio components that are associated with a single infrared host and (iii) introduce a "curliness" statistic to search for bent and disturbed radio morphologies, and (iv) extract a set of 17 giant radio galaxies between 700-1100 kpc. As we require no training labels, our method can be applied to any radio-continuum survey, provided a sufficiently representative SOM can be trained.