Application of equivariant transformations in neural networks to classification of galaxy images

Abstract We train a deep neural network with SE (2) equivariant layers on galaxy images grouped in two classes - cigar-shaped and round. We use a steerable basis to reduce the memory and computational load produced by the lifting correlations at different orientations of the images from the training dataset. The trained model performs with 99% accuracy in classifying images from the test dataset.