Machine Learning in Astronomy

Professor Ray Norris1

1Csiro/wsu, Epping, Australia


Next-generation radio telescopes generate petabytes of data, and extracting the science from the data not only presents formidable challenges, but also remarkable opportunities. The large data volumes enable us to do science in new and innovative ways, replacing detailed studies of individual galaxies by cross-correlation functions of the properties of millions of galaxies. The development of these techniques is in its infancy. In this paper I will discuss some of the machine-learning and “big data” techniques currently being developed to classify and cross-identify galaxies, measure their distances, and use their distributions on the sky to measure fundamental cosmological parameters.


Ray Norris is an astrophysicist at CSIRO and Western Sydney University. His professional life revolves around the question of figuring out how the Universe evolved from the Big Bang to the galaxies and stars that we see around us today. To achieve this, he leads the international “Evolutionary Map of the Universe” team who use CSIRO’s new ASKAP telescope and innovative “big data” techniques to  answer questions like “why do most galaxies have a black hole in their centre, and how does it affect the galaxy’s life-cycle?”. He is particularly interested in using machine learning techniques to extract the science from the data, and his holy grail is to develop a technique for making unexpected discoveries from astronomical data. As well as his mainstream astrophysical research, he is also known for his  research on the astronomy of Australian Aboriginal people.


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