Jaykumar Patel and Dan Wu, University of Windsor, Canada

The field of astronomy has made tremendous progress in recent years thanks to advancements in technology and the development of sophisticated algorithms. One area of interest for astronomers is the classification of galaxy morphology, which involves categorizing galaxies based on their visual appearance. However, with the sheer number of galaxy images available, it would be a daunting task to manually classify them all. To address this challenge, a novel Residual Neural Network (ResNet) model, called ResNet_Var, that can classify galaxy images is proposed in this study. Subsets of the Galaxy Zoo 2 dataset are used in this research, one contains over 28,000 images for the five-class classification task, and the other contains over 25,000 images for the seven-class classification task. The overall classification accuracy of the ResNet_Var model was 95.35% for the five-class classification task and 93.54% for the seven-class classification task.

Galaxy Zoo, Deep Learning, Residual Networks, Galaxy Morphology