Exploiting Convolutional Neural Networks for Galaxy Classification

Abstract

Galaxy classification is an important work in the field of astronomy. The efficiency of manually solving this problem is too low, and deep learning provides many methods for image classification. Using deep learning methods to automatically classify galaxies can greatly improve the efficiency of this task. Therefore, this paper uses mature Convolutional Neural Networks (CNN) models and self-designed simple CNN models, and compares their performance on the Galaxy10 DECals Dataset. In the experiment, the Inception network performed best, with an accuracy rate of 0.63 on the test set, but the training time was as long as about 2 hours. The simple CNN model ranked second, with an accuracy rate of 0.54, and the training time was only 20 minutes. The accuracy of other models is about 0.5, and the training time also takes 2 hours. The result performance of the model is worse than expected, which may be due to the less data used. The result of Inception is much ahead, and its solution to the problem provides a solution for the task of dealing with small data sets.