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Radio galaxy zoo: towards building the first multipurpose foundation model for radio astronomy with self-supervised learning
Abstract In this work, we apply self-supervised learning with instance differentiation to learn a robust, multipurpose representation for image analysis of resolved extragalactic continuum images. We train a multi-use model which compresses our unlabelled data into a structured, low dimensional representation which can be used for a variety of downstream …