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Rare Galaxy Classes Identified In Foundation Model Representations
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We identify rare and visually distinctive galaxy populations by searching for structure within the learned representations of pretrained models. We show that these representations arrange galaxies by appearance in patterns beyond those needed to predict the pretraining labels. We design a clustering approach to isolate specific local patterns, revealing groups of galaxies with rare and scientifically-interesting morphologies.
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From Galaxy Zoo DECaLS to BASS/MzLS: detailed galaxy morphology classification with unsupervised domain adaption
Unsupervised domain adaptation transfers a Galaxy Zoo DECaLS-trained morphology model to BASS/MzLS images, yielding improved classifications and a 248,088-galaxy catalogue.
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