Morphological classification of galaxies with deep learning: comparing 3-way and 4-way CNNs

Understand

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.