Galaxy morphology classification using VGG16

Abstract

Abstract There are billions of galaxies in the universe, each with millions or billions of stars. Galaxies vary in shape and size, and they interact with one another, sometimes colliding and merging, sometimes tearing apart, resulting in different features such as bars, rings, pseudo-pillars, and so on. These morphological properties play an important role in the study of galaxy evolution, so the classification of galaxy morphology has always been crucial in astrophysics. Earlier, using traditional methods or manual inspection to accomplish the classification task was a great challenge for astronomers. Today, however, machine learning is widely used for galaxy morphology classification because of its evolving efficiency and accuracy. In this paper, a popular convolutional neural network model (VGG16) will be used to classify galaxy morphology with an accuracy of 81%, which means that VGG16 can be used for correct classification.