  Unsupervised learning, a branch of machine learning that can operate on
unlabelled data, has proven to be a powerful tool for data exploration and
discovery in astronomy. As large surveys and new telescopes drive a rapid
increase in data size and richness, these techniques offer the promise of
discovering new classes of objects and of efficient sorting of data into
similar types. However, unsupervised learning techniques generally require
feature extraction to derive simple but informative representations of images.
In this paper, we explore the use of self-supervised deep learning as a method
of automated representation learning. We apply the algorithm Bootstrap Your Own
Latent (BYOL) to Galaxy Zoo DECaLS images to obtain a lower dimensional
representation of each galaxy, known as features. We briefly validate these
features using a small supervised classification problem. We then move on to
apply an automated clustering algorithm, demonstrating that this fully
unsupervised approach is able to successfully group together galaxies with
similar morphology. The same features prove useful for anomaly detection, where
we use the framework astronomaly to search for merger candidates. While the
focus of this work is on optical images, we also explore the versatility of
this technique by applying the exact same approach to a small radio galaxy
dataset. This work aims to demonstrate that applying deep representation
learning is key to unlocking the potential of unsupervised discovery in future
datasets from telescopes such as the Vera C. Rubin Observatory and the Square
Kilometre Array.
