Astronomical big data processing using machine learning: A comprehensive review

Abstract

Astronomy, being one of the oldest observational sciences, has collected a lot of data over the ages. In recent times, it is experiencing a huge data surge due to advancements in telescopic technologies with automated digital outputs. The main driver behind this article is to present various relevant Machine Learning (ML) algorithms and big data frameworks or tools being applied and can be employed in large astronomical data-set analysis to assist astronomers in solving multiple vital intriguing problems. Throughout this survey, we attempt to review, evaluate and summarize diverse astronomical data sources, gain knowledge of structure, the complexity of the data, and challenges in the data processing. Additionally, we discuss ample technologies being developed to handle and process this voluminous data. We also look at numerous activities being carried out all over the world enriching this domain. While going through existing literature, we perceived a limited number of comprehensive studies reported so far analyzing astronomy data-sets from the viewpoint of parallel processing and machine learning collectively. This motivated us to pursue this extensive literature review task by outlining up-to-date contributions and opportunities available in this area. Besides, this article also discusses briefly a cloud-based machine learning approach to estimate the extra-galactic object redshifts considering photometric data as input features. As the intersection of big data, machine learning and astronomy is a quite new paradigm, this article will create a strong awareness among interested young scientists for future research and provide an appropriate insight on how these algorithms and tools are becoming inevitable to the astronomy community day by day.