Prospecting MeerKAT Continuum Data for Enigmatic Radio Sources with Unsupervised Vector-Quantised Variational Autoencoders

We present a novel application of Vector quantised variational autoencoders (VQ-VAEs) to deep 1.28 GHz radio continuum images taken from the MeerKAT Galaxy Cluster Legacy Survey (MGCLS).VQ-VAEs are deep learning models widely used in modern computer vision applications and pipelines. Designed for image generation, VQ-VAEs are trained to reconstruct the input dataset via a low-dimensional discrete embedding. VQ-VAEs effectively learn the distribution of training data, where samples that do not fit the distribution well yield the highest reconstruction errors. This property makes VQ-VAEs a good candidate for the task of anomaly detection. In this work, we examine the effectiveness of VQ-VAEs in identifying radio continuum sources with anomalous structures in the image-plane domain. We find VQ-VAEs to be useful as part of a solution for searching such large datasets. We observe that they are able to remove a majority of the typical sources in such data, even when trained in an unsupervised manner on unlabelled data. We also provide our testing set of a large sample of manually labelled radio sources, in particular radio galaxies, taken from the MGCLS at 1.28 GHz. Automated approaches to searching through high volumes of data are key in extracting the full scientific potential of the Square Kilometre Array and its pathfinders.