Sky surveys are the largest data generators in astronomy, imaging vast numbers of galaxies at high resolutions. To date, machine learning investigations of sky-survey data have provided a large number of high-impact results, including the detection of a large number of strongly gravitationally lensed systems and the classification of millions of galaxies. However, existing methods used in the field of astrophysics suffer from the standard limitations of supervised learning; they require extensive compute resources and development time to target singular objectives, and the performance is limited by the small amount of labeled data on which to train models. With this ADSP project, the team will use their recently developed self-supervised learning framework to extract meaningful representations from galaxy images in the Dark Energy Camera Legacy Survey dataset, providing a scalable data-driven approach capable of learning from unlabeled data. The team’s work aims to serve the broader community by accelerating sky survey discoveries following the release of image representations, trained models, and software. Researchers will be able to simply download the low-dimensional representations of galaxies to perform scientific analysis, or use the team’s pre-trained model and quickly fine-tune it to carry out a specific task.