Galaxy Morphology Classification using Neural Ordinary Differential Equations

We use a continuous depth version of the Residual Network (ResNet) model known as Neural ordinary differential equations (NODE) for the purpose of galaxy morphology classification. We applied this method to carry out supervised classification of galaxy images from the Galaxy Zoo 2 dataset, into five distinct classes, and obtained an accuracy of about 92% for most of the classes. Through our experiments, we show that NODE not only performs as well as other deep neural networks, but has additional advantages over them, which can prove very useful for next generation surveys. We also compare our result against ResNet. While ResNet and its variants suffer problems, such as time consuming architecture selection (e.g. the number of layers) and the requirement of large data for training, NODE does not have these requirements. Through various metrics, we conclude that the performance of NODE matches that of other models, despite using only one-third of the total number of parameters as compared to these other models.