British researchers have identified 50 new planets using artificial intelligence, marking a technological breakthrough in astronomy, Report says, citing TASS.
Astronomers and computer scientists from the University of Warwick built a machine-learning algorithm to dig through old NASA data containing thousands of potential planet candidates.
It's not always clear, however, which of these candidates is genuine. When scientists search for exoplanets (planets outside our solar system), they look for dips in light that indicate a world passing between the telescope and their star. But these dips could also be caused by other factors, like background interference or even errors in the camera.
The research team trained the algorithm by having it go through data collected by NASA's now-retired Kepler Space Telescope, which spent nine years in deep space on a world-hunting mission. Once the algorithm learned to separate real planets from false positives accurately, it was used to analyze old data sets that had not yet been confirmed — which is where it found the 50 exoplanets.
These 50 exoplanets, which orbit around other stars, range in size from Neptune to smaller than Earth, the university said in a news release. Some of their orbits are as long as 200 days, and some as short as a single day. And now that astronomers know the planets are real, they can prioritize them for further observation.
The researchers' findings were published last week in the Monthly Notices of the Royal Astronomical Society.
"In terms of planet validation, no-one has used a machine learning technique before," said David Armstrong of the University of Warwick, one of the study's lead authors, in the news release. "Machine learning has been used for ranking planetary candidates but never in a probabilistic framework, which is what you need to validate a planet truly."