  We present an unsupervised machine learning technique that automatically
segments and labels galaxies in astronomical imaging surveys using only pixel
data. Distinct from previous unsupervised machine learning approaches used in
astronomy we use no pre-selection or pre-filtering of target galaxy type to
identify galaxies that are similar. We demonstrate the technique on the HST
Frontier Fields. By training the algorithm using galaxies from one field (Abell
2744) and applying the result to another (MACS0416.1-2403), we show how the
algorithm can cleanly separate early and late type galaxies without any form of
pre-directed training for what an 'early' or 'late' type galaxy is. We then
apply the technique to the HST CANDELS fields, creating a catalogue of
approximately 60,000 classifications. We show how the automatic classification
groups galaxies of similar morphological (and photometric) type, and make the
classifications public via a catalogue, a visual catalogue and galaxy
similarity search. We compare the CANDELS machine-based classifications to
human-based classifications from the Galaxy Zoo: CANDELS project. Although
there is not a direct mapping between Galaxy Zoo and our hierarchical
labelling, we demonstrate a good level of concordance between human and machine
classifications. Finally, we show how the technique can be used to identify
rarer objects and present new lensed galaxy candidates from the CANDELS
imaging.
