Morphological classification of galaxies using Conv-nets

Abstract

Since the beginning of space exploration, the galaxy classification has been a vexing problem that has always muddled the astrophysicists. A number of techniques have proven their remarkable utility in the classification of galaxies, however, upon analysis, these methods revealed certain inefficiencies that cannot be overlooked. The traditional classification of galaxies in the universe contains a significant part of their history in the authority of government agencies where the classifications in the previous years were performed primarily by experts manually. Today’s astronomical research produces large amounts of data and manually labelling the galaxy images based on morphological features can be time-consuming and error-prone. The objective of this paper is to study and analyze the different types of machine learning methodologies used for classifying galaxies. An inference drawn from this study is that using deep learning algorithms in conjunction with some data augmentation techniques provide excellent classification results of galaxies. Considering the aforementioned fact, the authors have proposed a layered CNN based classification model along with certain data augmentation techniques to classify galaxies morphological. “The Galaxy Zoo” dataset has been used from Kaggle which is further handcrafted for ease of classification. The galaxies are classified into three classes: spiral, elliptical, and lenticular (somewhere in-between). It has been observed from the experimental work that the proposed model outperform than its earlier contemporaries and can be used effectively to classify the galaxies.