Learning useful representations for radio astronomy 'in the wild' with contrastive learning

Unknown class distributions in unlabelled astrophysical training data have previously been shown to detrimentally affect model performance due to dataset shift between training and validation sets. For radio galaxy classification, we demonstrate in this work that removing low angular extent sources from the unlabelled data before training produces qualitatively different training dynamics for a contrastive model. By applying the model on an unlabelled data-set with unknown class balance and sub-population distribution to generate a representation space of radio galaxies, we show that with an appropriate cut threshold we can find a representation with FRI/FRII class separation approaching that of a supervised baseline explicitly trained to separate radio galaxies into these two classes. Furthermore we show that an excessively conservative cut threshold blocks any increase in validation accuracy. We then use the learned representation for the downstream task of performing a similarity search on rare hybrid sources, finding that the contrastive model can reliably return semantically similar samples, with the added bonus of finding duplicates which remain after pre-processing.
Comment: Accepted in: Machine Learning for Astrophysics Workshop at the Thirty Ninth International Conference on Machine Learning (ICML 2022); final version