Big Data and Machine Learning Algorithms in Radio Astronomy

Astronomy is facing an exponential growth of data driven by the enormous technological advances in telescopes, detectors, electronics and computer technologies. Likewise, the complexity and dimension of astronomical data are growing rapidly. Moreover with the upcoming next-generation radio telescope facilities such as the SKA/MeerKat and its related pathfinder (e.g., ASKAP), billions of radio sources will be detected. Handling and exploring such massive amount of data and extracting useful information and knowledge from such data is not possible through manual and traditional data analytic techniques: automation of data processing is therefore essential, and a new generation of algorithms are needed using machine learning and other data mining techniques. In this talk. I will provide an overview of the state of massive datasets in astronomy/radio astronomy, presentfour applications of machine learning to radio astronomical data, along with data mining soft wares and toolkits related to astronomical applications.