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Morphology of Radio Sources in Representation Space

Perez, Nicolas Baron et al.
arXiv (All) · Papers
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astrophysics-of-galaxies
Understanding radio source morphologies and their classification remains challenging. We previously developed a deep clustering method based on self-supervised learning to classify a subsample of radio sources from the LOFAR Two-meter Sky Survey DR2. This yielded a labelled subset used to fine-tune
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