We use a contrastive self-supervised learning framework to estimate distances to galaxies from their photometric images. We incorporate data augmentations from computer vision as well as an application-specific augmentation accounting for galactic dust. We find that the resulting visual representations of galaxy images are semantically useful and allow for fast similarity searches, and can be successfully fine-tuned for the task of redshift estimation. We show that (1) pretraining on a large corpus of unlabeled data followed by fine-tuning on some labels can attain the accuracy of a fully-supervised model which requires 2-4x more labeled data, and (2) that by fine-tuning our self-supervised representations using all available data labels in the Main Galaxy Sample of the Sloan Digital Sky Survey (SDSS), we outperform the state-of-the-art supervised learning method.
@article{arxiv.2101.04293,
title = {Estimating Galactic Distances From Images Using Self-supervised Representation Learning},
author = {Md Abul Hayat and Peter Harrington and George Stein and Zarija Lukić and Mustafa Mustafa},
journal= {arXiv preprint arXiv:2101.04293},
year = {2021}
}