Galaxy Classification Using Deep Learning

Abstract

In this paper, the framework of Deep CNN (Deep Convolutional Neural Network) is basically used for classifying the galaxies. It is shown that galaxies can basically be classified with the help of distinct features into the different categories namely Disturbed galaxies, merging galaxies, Round Smooth galaxies, In-between Round Smooth galaxies, Cigar Shaped Spiral galaxies, Barred Tight Spiral galaxies, Unbarred Loose Spiral galaxies, Edge-on galaxies without bulge and Edge-on galaxy with the bulge. The model that we are proposing is a ConvNet galaxy architecture consists of one input layer having 16 filters, followed by 4 hidden layers, 1 penultimate dense layer, along with an Output Softmax layer. we also included data augmentation such as shear, zoom, rotation, rescaling, and flip. we used the activation function. The dataset which is used in the proposed research is Galaxy 10 DECals, which has taken its images from DESI Legacy Imaging Surveys and got labeled by Galaxy Zoo. The dataset used contains 256 × 256 pixel-colored Galaxy images (g, r, and z band). The proposed model and framework is training over 17736 images and accomplished above 84.04% in testing accuracy. When a comparison is made between the results and the testing accuracy was compared with other existing models.