Identifying habitable planets from 21-cm telescope data using machine learning

Modern astronomers together with engineers have built large array of radio telescopes to look for faint signals generated billions of years back buried in big chaotic datasets, like a needle in a haystack. The contributors of noise in those signals range from humans to all celestial events happening in the path the signal is traveling. To be able to extract a signal successfully, they need to first simulate the complete pipeline of an observation so that each element within the pipeline can be understood and taken care of separately. It used to be dose based on some predefined models developed by astrophysicists, however in recent times the amount and scope of data accumulated by these telescopes have increased exponentially in amount. In this project we intend to develop or modify existing techniques of noise removal in radio telescope signals, specially 21-cm signals using various machine learning and deep learning techniques. This project is collaborated with South African Radio Astronomy Observatory (https://www.sarao.ac.za) and Low Frequency Array (LOFAR) telescope (http://www.lofar.org).

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