Galaxy morphological classification in deep-wide surveys via
  unsupervised machine learning
  Galaxy morphology is a fundamental quantity, that is essential not only for
the full spectrum of galaxy-evolution studies, but also for a plethora of
science in observational cosmology. While a rich literature exists on
morphological-classification techniques, the unprecedented data volumes,
coupled, in some cases, with the short cadences of forthcoming 'Big-Data'
surveys (e.g. from the LSST), present novel challenges for this field. Large
data volumes make such datasets intractable for visual inspection (even via
massively-distributed platforms like Galaxy Zoo), while short cadences make it
difficult to employ techniques like supervised machine-learning, since it may
be impractical to repeatedly produce training sets on short timescales.
Unsupervised machine learning, which does not require training sets, is ideally
suited to the morphological analysis of new and forthcoming surveys. Here, we
employ an algorithm that performs clustering of graph representations, in order
to group image patches with similar visual properties and objects constructed
from those patches, like galaxies. We implement the algorithm on the
Hyper-Suprime-Cam Subaru-Strategic-Program Ultra-Deep survey, to autonomously
reduce the galaxy population to a small number (160) of 'morphological
clusters', populated by galaxies with similar morphologies, which are then
benchmarked using visual inspection. The morphological classifications (which
we release publicly) exhibit a high level of purity, and reproduce known trends
in key galaxy properties as a function of morphological type at z<1 (e.g.
stellar-mass functions, rest-frame colours and the position of galaxies on the
star-formation main sequence). Our study demonstrates the power of unsupervised
machine learning in performing accurate morphological analysis, which will
become indispensable in this new era of deep-wide surveys.
