Galaxy Morphology Classification Based On An Improved Deep Convolutional Neural Network

Abstract

With each passing day telescopes around and above the Earth capture more and more images of distant galaxies. As better and bigger telescopes continue to collect these images, the datasets begin to explode in size. In order to better understand how the different shapes (or morphologies) of galaxies relate to the physics that create them, such images need to be sorted and classified. Combining with Kaggle public datasets called Galaxy Zoo, this paper devised a deep convolutional neural network for galaxy morphology image classification. The network contains eight convolutional layers, five max_pooling layers, one flatten layer, a fully connected layer contains 150 neurons, as well as the final layer which outputs galaxy category of probability distribution of each image. The experiment results show that our neural network on the validation set of cosine similarity reached to -0.8652 (the closer to -1 means that the closer the predicted output and the expected output), implying that our model for galaxy morphology classification is very effective.