Optimising Automatic Morphological Classification of Galaxies with
  Machine Learning and Deep Learning using Dark Energy Survey Imaging
  There are several supervised machine learning methods used for the
application of automated morphological classification of galaxies; however,
there has not yet been a clear comparison of these different methods using
imaging data, or a investigation for maximising their effectiveness. We carry
out a comparison between several common machine learning methods for galaxy
classification (Convolutional Neural Network (CNN), K-nearest neighbour,
Logistic Regression, Support Vector Machine, Random Forest, and Neural
Networks) by using Dark Energy Survey (DES) data combined with visual
classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine
the optimal machine learning methods when using imaging data for galaxy
classification. We show that CNN is the most successful method of these ten
methods in our study. Using a sample of $\sim$2,800 galaxies with visual
classification from GZ1, we reach an accuracy of $\sim$0.99 for the
morphological classification of Ellipticals and Spirals. The further
investigation of the galaxies that have a different ML and visual
classification but with high predicted probabilities in our CNN usually reveals
an the incorrect classification provided by GZ1. We further find the galaxies
having a low probability of being either spirals or ellipticals are visually
Lenticulars (S0), demonstrating that supervised learning is able to rediscover
that this class of galaxy is distinct from both Es and Spirals. We confirm that
$\sim$2.5\% galaxies are misclassified by GZ1 in our study. After correcting
these galaxies' labels, we improve our CNN performance to an average accuracy
of over 0.99 (accuracy of 0.994 is our best result).
