  Classifying the morphologies of galaxies is an important step in
understanding their physical properties and evolutionary histories. The advent
of large-scale surveys has hastened the need to develop techniques for
automated morphological classification. We train and test several convolutional
neural network architectures to classify the morphologies of galaxies in both a
3-class (elliptical, lenticular, spiral) and 4-class (+irregular/miscellaneous)
schema with a dataset of 14034 visually-classified SDSS images. We develop a
new CNN architecture that outperforms existing models in both 3 and 4-way
classification, with overall classification accuracies of 83% and 81%
respectively. We also compare the accuracies of 2-way / binary classifications
between all four classes, showing that ellipticals and spirals are most easily
distinguished (>98% accuracy), while spirals and irregulars are hardest to
differentiate (78% accuracy). Through an analysis of all classified samples, we
find tentative evidence that misclassifications are physically meaningful, with
lenticulars misclassified as ellipticals tending to be more massive, among
other trends. We further combine our binary CNN classifiers to perform a
hierarchical classification of samples, obtaining comparable accuracies (81%)
to the direct 3-class CNN, but considerably worse accuracies in the 4-way case
(65%). As an additional verification, we apply our networks to a small sample
of Galaxy Zoo images, obtaining accuracies of 92%, 82% and 77% for the binary,
3-way and 4-way classifications respectively.
