A catalogue of complex radio sources in the Rapid ASKAP Continuum Survey created using a Self-Organising Map

Abstract: Next generations of radio surveys are expected to identify tens of millions of new sources, and identifying and classifying their morphologies will require novel and more efficient methods. Self-Organising Maps (SOMs), a type of unsupervised machine learning, can be used to address this problem. We map 251,259 multi-Gaussian sources from Rapid ASKAP Continuum Survey (RACS) onto a SOM with discrete neurons. Similarity metrics, such as Euclidean distances, can be used to identify the best-matching neuron or unit (BMU) for each input image. We establish a reliability threshold by visually inspecting a subset of input images and their corresponding BMU. We label the individual neurons based on observed morphologies and these labels are included in our value-added catalogue of RACS sources. Sources for which the Euclidean distance to their BMU is $\lesssim$ 5 (accounting for approximately 79$\%$ of sources) have an estimated $>90\%$ reliability for their SOM-derived morphological labels. This reliability falls to less than 70$\%$ at Euclidean distances $\gtrsim$ 7. Beyond this threshold it is unlikely that the morphological label will accurately describe a given source. Our catalogue of complex radio sources from RACS with their SOM-derived morphological labels from this work will be made publicly available.