Imagine you’re Edwin Hubble in 1923, about to prove that the Milky Way is just one galaxy in a universe filled with them. You have just spotted a faraway variable star. You write down a note about that star on a photographic plate: “VAR!”
Or imagine it’s 1977, and you’re reading a printout from a radio telescope that listens for aliens, red pen in hand, when you find a long, strange, still-unexplained signal. “Wow!” you write.
Or imagine it’s August 2017, you’re signed on to Slack, and you’ve just seen the smoldering wreckage of a collision of two neutron stars. “!” you type to your colleagues, unable to muster anything else.
Each of these astronomical classics highlights one particular aspect of discovery: the thrill of knowing something about nature that no one else does. But these moments from the highlight reel of astronomy’s history minimize the more prosaic aspects of research, the tedium of peering at a screen for hours on end, blinking, clicking, or executing a computer script, again and again, forever, and maybe not finding anything noteworthy at all.
But now AI is here to do the boring part.
In a new paper published by the journal Monthly Notices of the Royal Astronomical Society, a neural network has successfully flipped through images of more than 20,000 galaxies and pulled out a few hundred of the most intriguing. “I think it will become the norm since future astronomical surveys will produce an enormous quantity of data,” said Carlo Petrillo of the University of Groningen in the Netherlands, in a statement. “We don’t have enough astronomers to cope with this.”