Galaxy classification: a deep learning approach for classifying Sloan Digital Sky Survey images

In recent decade, large scale sky surveys such as Sloan Digital Sky Survey (SDSS) has resulted in generation of tremendous amount of data. The classification of this enormous amount of data by astronomers is time consuming. To simplify this process, in 2007 a volunteer based citizen science project called ‘Galaxy Zoo’ was introduced which has reduced the time for classification by a good extent. However, in this modern era of deep learning, automating this classification task is highly beneficial as it reduces the time for classification. Since last few years, many algorithms have been proposed which happen to do a phenomenal job in classifying galaxies into multiple classes. But all these algorithms tend to classify galaxies into less than 6 classes. However after considering the minute information which we know about galaxies, it is necessary to classify galaxies into more than 8 classes. In this study, a neural network model is proposed so as to classify SDSS data into 10 classes from an extended Hubble Tuning Fork. Great care is given to disk edge and disk face galaxies, distinguishing between a variety of substructures and minute features which are associated to each class. The proposed model consists of convolution layers to extract features making this method fully automatic. The achieved test accuracy is 84.73 per cent which happens to be promising after considering such minute details in classes. Along with convolution layers, the proposed model has 3 more layers responsible for classification which makes the algorithm consume less time.