Data augmentation based morphological classification of galaxies using deep convolutional neural network

Abstract

From the early stages of Astronomy, the classification of galaxies has been a conundrum that has left astrophysicists in a situation of quandary. Although, previous methods did a phenomenal job in classifying galaxies but while analysing them, certain inefficiencies had been revealed which cannot be overlooked. The objective had been to conduct an analysis of different types of machine learning techniques that have been used to classify galaxies. This analysis had been conducted on the basis of different attributes taken for different types of classification of galaxies. A method had been proposed to classify galaxies with higher accuracy than previous methods. The configuration for the literature analysis used datasets such as ESO-LV and SDSS and discussed the antecedent techniques for classifying galaxies. It had been inferred that a Convolutional Neural Network with certain data augmentation for irregular Galaxies (Irr) gives the best result of all the algorithms that have been discussed in the literature and its analysis. Owing to the aforementioned, an implementation accentuating the use of deep learning algorithms with certain Data Augmentation techniques and certain different activation functions, named daMCOGCNN (data augmentation-based MOrphological Classifier Galaxy Using Convolutional Neural Networks) had been proposed for morphological classification of galaxies. The dataset comprises of 4614 images from SDSS Image Gallery, Galaxy Zoo challenge and Hubble Image Gallery. The efficient implementation of this method gave a testing accuracy of approximately 98% and 97.92% accuracy had been achieved on a dataset taken from different websites such as AstroBin and other such sources. The model introduced here outperforms its earlier contemporaries.