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We present the use of self-supervised learning to explore and exploit largeunlabeled datasets. Focusing on 42 million galaxy images from the latest datarelease of the Dark Energy Spectroscopic Instrument (DESI) Legacy ImagingSurveys, we first train a self-supervised model to distill low-dimensionalrepresentations that are robust to symmetries, uncertainties, and noise in eachimage. We then use the representations to construct and publicly release aninteractive semantic similarity search tool. We demonstrate how our tool can beused to rapidly discover rare objects given only a single example, increase thespeed of crowd-sourcing campaigns, and construct and improve training sets forsupervised applications. While we focus on images from sky surveys, thetechnique is straightforward to apply to any scientific dataset of anydimensionality. The similarity search web app can be found athttps://github.com/georgestein/galaxy_search

Self-supervised similarity search for large scientific datasets