The telescope offers one of the most seductive suggestions a technological object can carry: the idea that humans might pick up a thing, peer into it, and finally solve the riddle of the heavens as a result.
Solving that mystery requires its own kind of refraction in perspective, collapsing the distance between near and far as a way to find out what faraway places might tell us about our planet and its place in the universe.
This is why early astronomers didn’t just gaze up each night to produce detailed sketches of celestial bodies. They also tracked the movement of those bodies across the sky over time. They developed an understanding of Earth’s movement as a result.
But to do that, they had to collect loads of data. It makes sense, then, that computers would be such useful tools in modern astronomy. Computers help us program rocket launches and develop models for flight missions, but they also analyze deep wells of information from distant worlds. Ever larger telescopes have illuminated more about the depths of the universe than the earliest astronomers ever could have dreamed.
Spain’s Gran Telescopio Canarias, in the Canary Islands, is the largest telescope on Earth. It has a lens diameter of 34 feet. The Thirty Meter Telescope planned for Hawaii, if it is built, will be nearly three times larger.
With telescopes, the bigger the lens, the farther you can see. But soon, artificial intelligence may help bypass size constraints and tell us what we’re looking at in outer space—even when it looks, to a telescope, like an indeterminate blob. The idea is to train a neural network so that it can look at a blurry image of space, then accurately reconstruct features of distant galaxies that a telescope could not resolve on its own.
In a paper published in January by the Monthly Notices of the Royal Astronomical Society, a team of researchers led by Kevin Schawinski, an astrophysicist at ETH Zurich, described their successful attempts to do just that. The researchers say they were able to train a neural network to identify galaxies, then sharpen a blurry image into a focused view of what a star-forming regions or galactic dust-lane actually looks like. They used machine-learning technique known as “generative adversarial network,” which involves having two neural nets compete against one another.
