To do this, astronomers first use AI to convert theoretical models into observational signatures – including realistic levels of noise. Teams have used this approach to detect new exoplanets, learn about the ancestral stars that led to the formation and growth of the Milky Way, and predict the signatures of new types of gravitational waves. But it is also quite powerful at finding objects or phenomena that are theorized but have not yet been discovered in the real world. NASA, ESA, CSA, Joseph Olmsted (STScI), CC BY Making new discoveriesĪI has proved itself to be excellent at identifying known objects – like galaxies or exoplanets – that astronomers tell it to look for. AI tools can now pick out the signs of an exoplanet with 96% accuracy.ĪI tools can help astronomers discover new exoplanets like TRAPPIST-1 b. Astronomers discovered most of the 5,300 known exoplanets by measuring a dip in the amount of light coming from a star when a planet passes in front of it. This has allowed SETI efforts to cover more ground while also greatly reducing the number of false positive signals.Īnother example is the search for exoplanets. Now, researchers are using AI to sift through reams of data much more quickly and thoroughly than people can. More recently, researchers harnessed 150,000 personal computers and 1.8 million citizen scientists to look for artificial radio signals. Early on, radio astronomers scanned charts by eye to look for anomalies that couldn’t be explained.

Astronomers working on SETI, the Search for Extraterrestrial Intelligence, use radio telescopes to look for signals from distant civilizations. This story has been repeated in other areas of astronomy. Now the algorithms are so effective that they can classify galaxies with an accuracy of 98%. Astronomers began using neural networks to classify galaxies in the early 2010s. About 99% of the pixels in an astronomical image contain background radiation, light from other sources or the blackness of space – only 1% have the subtle shapes of faint galaxies.ĪI algorithms – in particular, neural networks that use many interconnected nodes and are able to learn to recognize patterns – are perfectly suited for picking out the patterns of galaxies. Rigby, CC BY Picking out patternsĪstronomy often involves looking for needles in a haystack. One of the earliest uses of AI in astronomy was to pick out the multitude of faint galaxies hidden in the background of images.

Making Waves by Christina Lee