Classification of Radio Sources Through Self-Supervised Learning
The morphology of radio galaxies is indicative of their interaction with their surroundings, among other effects. Since modern radio surveys contain a large number of radio sources that would be impossible to analyse and classify manually, it is important to develop automatic schemes. Unlike other fields, which benefit from established theoretical frameworks and simulations, there are no such comprehensive models built for radio galaxies. This stands as a challenge to data analysis in this field and novel approaches are required. In this study, we investigate the classification of radio galaxies from the LOFAR Two-meter Sky Survey Data Release 2 (LoTSS-DR2) using self-supervised learning. Our deep clustering classification strategy involves three main steps: (i) self-supervised pre-training; (ii) fine-tuning using a labelled subsample created from the learned representations; and (iii) performing a final classification of the selected unlabelled sample. To enhance morphological information in the representations, we developed an additional random augmentation, called a random structural view (RSV). Our results demonstrate that the learned representations contain rich morphological information, enabling the creation of a labelled subsample that effectively captures the morphological diversity within the unlabelled sample. Additionally, the classification of the unlabelled sample into 12 morphological classes yields robust class probabilities. We successfully demonstrated that a subset of radio galaxies from LoTSS-DR2, encompassing diverse morphologies, can be classified using deep clustering based on self-supervised learning. The methodology developed here bridges the gap left by the absence of simulations and theoretical models, offering a framework that can readily be applied to astronomical image analyses in other bands.