Inter-university Institute for Data Intensive Astronomy

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

Abstract We present a novel application of Vector quantised variational autoencoders (VQ-VAEs) as an unsupervised tool to probe deep 1.28 GHz radio continuum images taken from the MeerKAT Galaxy Cluster Legacy Survey (MGCLS) and compare their performance to other machine learning...