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. “Machine learning has been used for ranking planetary candidates but never in a probabilistic framework, which is what you need to truly validate a planet.”
Now that the researchers know it works, they hope to use the AI for current and future telescope missions. It can provide a consistent and efficient method of validation, according to the release; once properly trained, the AI is faster than current techniques, and can be automated to perform on its own.
The algorithm could “validate thousands of unseen candidates in seconds,” the study indicated. And because it’s based on machine learning, it can still be improved upon, and can continue to become more effective with each new discovery.
In their study, the research team argues that astronomers should use multiple validation techniques — including this new algorithm — to confirm future exoplanet discoveries. Currently, about 30% of all known planets were validated using only one method, which is “not ideal,” said Armstrong.