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AI in Action: Machines that make sense of the sky

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Science  07 Jul 2017:
Vol. 357, Issue 6346, pp. 26
DOI: 10.1126/science.357.6346.26

System sharpens images by learning how a galaxy should look

This past April, astrophysicist Kevin Schawinski posted fuzzy pictures of four galaxies on Twitter, along with a request: Could fellow astronomers help him classify them? Colleagues chimed in to say the images looked like ellipticals and spirals—familiar species of galaxies.

AI that “knows” what a galaxy should look like transforms a fuzzy image (left) into a crisp one (right).

PHOTOS: KIYOSHI TAKAHASE SEGUNDO/ALAMY STOCK PHOTO

Some astronomers, suspecting trickery from the computation-minded Schawinski, asked outright: Were these real galaxies? Or were they simulations, with the relevant physics modeled on a computer? In truth they were neither, he says. At ETH Zurich in Switzerland, Schawinski, computer scientist Ce Zhang, and other collaborators had cooked the galaxies up inside a neural network that doesn't know anything about physics. It just seems to understand, on a deep level, how galaxies should look.

With his Twitter post, Schawinski just wanted to see how convincing the network's creations were. But his larger goal was to create something like the technology in movies that magically sharpens fuzzy surveillance images: a network that could make a blurry galaxy image look like it was taken by a better telescope than it actually was. That could let astronomers squeeze out finer details from reams of observations. “Hundreds of millions or maybe billions of dollars have been spent on sky surveys,” Schawinski says. “With this technology we can immediately extract somewhat more information.”

The forgery Schawinski posted on Twitter was the work of a generative adversarial network, a kind of machine-learning model that pits two dueling neural networks against each other. One is a generator that concocts images, the other a discriminator that tries to spot any flaws that would give away the manipulation, forcing the generator to get better. Schawinski's team took thousands of real images of galaxies, and then artificially degraded them. Then the researchers taught the generator to spruce up the images again so they could slip past the discriminator. Eventually the network could outperform other techniques for smoothing out noisy pictures of galaxies.

Schawinski's approach is a particularly avant-garde example of machine learning in astronomy, says astrophysicist Brian Nord of Fermi National Accelerator Laboratory in Batavia, Illinois, but it's far from the only one. At the January meeting of the American Astronomical Society, Nord presented a machine-learning strategy to hunt down strong gravitational lenses: rare arcs of light in the sky that form when the images of distant galaxies travel through warped spacetime on the way to Earth. These lenses can be used to gauge distances across the universe and find unseen concentrations of mass.

Strong gravitational lenses are visually distinctive but difficult to describe with simple mathematical rules—hard for traditional computers to pick out, but easy for people. Nord and others realized that a neural network, trained on thousands of lenses, can gain similar intuition. In the following months, “there have been almost a dozen papers, actually, on searching for strong lenses using some kind of machine learning. It's been a flurry,” Nord says.

And it's just part of a growing realization across astronomy that artificial intelligence strategies offer a powerful way to find and classify interesting objects in petabytes of data. To Schawinski, “That's one way I think in which real discovery is going to be made in this age of ‘Oh my God, we have too much data.’”

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