Astronomers Use Machine Learning to Mine Radio Telescope Data

New telescopes with unprecedented sensitivity and resolution are being unveiled around the world - and beyond. Among them are the Giant Magellan Telescope under construction in Chile, and the James Webb Space Telescope, which is parked a million and a half kilometres out in space, writes Michelle Lochner for The Conversation.

This means there is a wealth of data available to scientists that simply wasn't there before. The raw data off just a single observation from the MeerKAT radio telescope in South Africa's Northern Cape province can measure a terabyte. That's enough to fill a laptop computer's hard drive. MeerKAT is an array of 64 large antenna dishes. It uses radio signals from space to study the evolution of the universe and everything it contains - galaxies, for example. Each dish is said to generate as much data in one second as you'd find on a DVD.

Machine learning is helping astronomers to work through this data quickly and more accurately than poring over it manually. Perhaps surprisingly, despite increasing reliance on computers, up until recently the discovery of rare or new astrophysical phenomena has completely relied on human inspection of the data.