2021MNRAS.506..659C

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 (CNN) architectures to classify the morphologies of galaxies in both a 3-class (elliptical, lenticular, and spiral) and a 4-class (+irregular/miscellaneous) schema with a data set of 14 034 visually classified SDSS images. We develop a new CNN architecture that outperforms existing models in both 3-way and 4-way classifications, with overall classification accuracies of 83 and 81 per cent, 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 per cent accuracy), while spirals and irregulars are hardest to differentiate (78 per cent 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 per cent) to the direct 3-class CNN, but considerably worse accuracies in the 4-way case (65 per cent). As an additional verification, we apply our networks to a small sample of Galaxy Zoo images, obtaining accuracies of 92, 82, and 77 per cent for the binary, 3-way, and 4-way classifications, respectively.