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In recent decades, large-scale sky surveys such as Sloan Digital Sky Survey(SDSS) have resulted in generation of tremendous amount of data. Theclassification of this enormous amount of data by astronomers is timeconsuming. To simplify this process, in 2007 a volunteer-based citizen scienceproject called Galaxy Zoo was introduced, which has reduced the time forclassification by a good extent. However, in this modern era of deep learning,automating this classification task is highly beneficial as it reduces the timefor classification. For the last few years, many algorithms have been proposedwhich happen to do a phenomenal job in classifying galaxies into multipleclasses. But all these algorithms tend to classify galaxies into less than sixclasses. However, after considering the minute information which we know aboutgalaxies, it is necessary to classify galaxies into more than eight classes. Inthis study, a neural network model is proposed so as to classify SDSS data into10 classes from an extended Hubble Tuning Fork. Great care is given to discedge and disc face galaxies, distinguishing between a variety of substructuresand minute features which are associated with each class. The proposed modelconsists of convolution layers to extract features making this method fullyautomatic. The achieved test accuracy is 84.73 per cent which happens to bepromising after considering such minute details in classes. Along withconvolution layers, the proposed model has three more layers responsible forclassification, which makes the algorithm consume less time.

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