Quantifying Non-parametric Structure of High-redshift Galaxies with Deep
  Learning
  At high redshift, due to both observational limitations and the variety of
galaxy morphologies in the early universe, measuring galaxy structure can be
challenging. Non-parametric measurements such as the CAS system have thus
become an important tool due to both their model-independent nature and their
utility as a straightforward computational process. Recently, convolutional
neural networks (CNNs) have been shown to be adept at image analysis, and are
beginning to supersede traditional measurements of visual morphology and
model-based structural parameters. In this work, we take a further step by
extending CNNs to measure well known non-parametric structural quantities:
concentration ($C$) and asymmetry ($A$). We train CNNs to predict $C$ and $A$
from individual images of $\sim 150,000$ galaxies at $0 < z < 7$ in the CANDELS
fields, using Bayesian hyperparameter optimisation to select suitable network
architectures. Our resulting networks accurately reproduce measurements
compared with standard algorithms. Furthermore, using simulated images, we show
that our networks are more stable than the standard algorithms at low
signal-to-noise. While both approaches suffer from similar systematic biases
with redshift, these remain small out to $z \sim 7$. Once trained, measurements
with our networks are $> 10^3$ times faster than previous methods. Our approach
is thus able to reproduce standard measures of non-parametric morphologies and
shows the potential of employing neural networks to provide superior results in
substantially less time. This will be vital for making best use of the large
and complex datasets provided by upcoming galaxy surveys, such as Euclid and
Rubin-LSST.
