  Sky surveys are the largest data generators in astronomy, making automated
tools for extracting meaningful scientific information an absolute necessity.
We show that, without the need for labels, self-supervised learning recovers
representations of sky survey images that are semantically useful for a variety
of scientific tasks. These representations can be directly used as features, or
fine-tuned, to outperform supervised methods trained only on labeled data. We
apply a contrastive learning framework on multi-band galaxy photometry from the
Sloan Digital Sky Survey (SDSS) to learn image representations. We then use
them for galaxy morphology classification, and fine-tune them for photometric
redshift estimation, using labels from the Galaxy Zoo 2 dataset and SDSS
spectroscopy. In both downstream tasks, using the same learned representations,
we outperform the supervised state-of-the-art results, and we show that our
approach can achieve the accuracy of supervised models while using 2-4 times
fewer labels for training.
