The rise of machine learning in astronomy
Astronomy is one of the oldest sciences and the first science to incorporate maths and geometry. It sits at the centre of humankind's search for its place in the universe.
As we delve deeper into the space surrounding our planet, the tools we use become more complex. Astronomers have come a long way from tracking the night sky with the naked eye or cataloguing the stars with a pen and paper.
Modern astronomers use advanced computer programming techniques in their work—from programming satellites to teaching computers to analyse data like a researcher.
So what do astronomers do with their computers?
Mo' data, mo' problems
Big data is a big problem in astronomy. The next generation of radio and optical telescopes will be able to map huge chunks of the night sky. The Square Kilometre Array (SKA) will push data processing to its limits.
Built in two phases, the SKA will have over 2000 radio dishes and 2 million low-frequency antennas once finished. These antennas combined will produce over an exabyte of data each day—more than the world's internet usage per day. The data is then processed to be made manageable, meaning the size of the data that astronomers have to deal with will be smaller.
Project scientist for the Australian SKA Pathfinder Dr. Aidan Hotan explains.
"Data from a radio telescope array is very much like the flow of water through an ecosystem. The individual antennas each produce data, which is then transmitted over some distance and combined with other antennas in various stages—like smaller tributaries combining into a larger river," says Aidan.
"The largest data rate you can consider is the total raw output from each individual antenna, but in reality, we reduce that total rate to more manageable numbers as we flow through the system. We can combine the signals in ways that retain only the information we want or can make use of."
Jane Merdith, Tendron Systems Ltd, London, UK.
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