Our planet-hunting telescopes have gotten so good at their jobs that they've located thousands of possible planets outside our solar system. That means scientists have to sift through a whole lot of data to figure out what's a real planet and what's a pretender.
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Telescopes like NASA's Transiting Exoplanet Survey Satellite (TESS) look for a telltale dip in brightness that indicates something is passing by a star. Sometimes this is a planet, sometimes it's a glitch, asteroids, dust or a quirk of a binary star system.
The research team created a machine learning algorithm and trained it using data on confirmed planets and false-positives from NASA's retired Kepler mission. Then they turned it loose to analyze a group of unconfirmed planet candidates, also from the Kepler data. In a first, the AI system confirmed 50 planets out of that bunch.
"The algorithm we have developed lets us take 50 candidates across the threshold for planet validation, upgrading them to real planets," Armstrong said in a Warwick release Tuesday. Validating planets can help scientists direct their resources to interesting spots in space without wasting their time on "fake" planets.
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The ability to confirm planets using this method is a step forward. Scientists had been using machine learning to rank candidates. "Rather than saying which candidates are more likely to be planets, we can now say what the precise statistical likelihood is. Where there is less than a 1% chance of a candidate being a false positive, it is considered a validated planet," said Armstrong.
"We still have to spend time training the algorithm, but once that is done it becomes much easier to apply it to future candidates," Armstrong said. "You can also incorporate new discoveries to progressively improve it."