Galaxy Morphological Classification of the Legacy Surveys with Deformable Convolutional Neural Networks

Abstract

The ongoing and forthcoming surveys will result in an unprecedented increase in the number of observed galaxies. As a result, data-driven techniques are now the primary methods for analyzing and interpreting this vast amount of information. While deep learning using computer vision has been the most effective for galaxy morphology recognition, there are still challenges in efficiently representing spatial and multi-scale geometric features in practical survey images. In this paper, we incorporate layer attention and deformable convolution into a convolutional neural network (CNN) to bolster its spatial feature and geometric transformation modeling capabilities. Our method was trained and tested on seven classifications of a data set from Galaxy Zoo DECaLS, achieving a classification accuracy of 94.5%, precision of 94.4%, recall of 94.2%, and an F1 score of 94.3% using macroscopic averaging. Our model outperforms traditional CNNs, offering slightly better results while substantially reducing the number of parameters and training time. We applied our method to Data Release 9 of the Legacy Surveys and present a galaxy morphological classification catalog including approximately 71 million galaxies and the probability of each galaxy to be categorized as Round, In-between, Cigar-shaped, Edge-on, Spiral, Irregular, and Error. The code detailing our proposed model and the catalog are publicly available in doi:10.5281/zenodo.10018255 and GitHub (https://github.com/kustcn/legacy_galaxy).