Galaxy Morphology Classification with Deep Convolutional Neural Networks

Understand

We propose a variant of residual networks (ResNets) for galaxy morphology classification.

• The variant, together with other popular convolutional neural networks (CNNs), are applied to a sample of 28790 galaxy images from Galaxy Zoo 2 dataset, to classify galaxies into five classes, i.e.
• completely round smooth, in-between smooth (between completely round and cigar-shaped), cigar-shaped smooth, edge-on and spiral.
• A variety of metrics, such as accuracy, precision, recall, F1 value and AUC, show that the proposed network achieves the state-of-the-art classification performance among the networks, namely, Dieleman, AlexNet, VGG, Inception and ResNets.