By applying machine learning to vast TESS datasets, researchers have built one of the most precise catalogs of nearby exoplanets to date.
Astronomers at the University of Warwick have confirmed more than 100 exoplanets, including 31 newly identified worlds, using a new artificial intelligence system applied to data from NASA’s Transiting Exoplanet Survey Satellite. This mission scans the sky for slight dips in starlight that occur when planets pass in front of their host stars.
The findings, published in MNRAS, come from a newly developed AI pipeline called RAVEN. The team used it to analyze observations of more than 2.2 million stars gathered during TESS’s first four years. Their search focused on planets with very short orbits, completing a trip around their stars in under 16 days, to better understand how common these close-in worlds are.
“Using our newly developed RAVEN pipeline, we were able to validate 118 new planets, and over 2,000 high-quality planet candidates, nearly 1,000 of them entirely new,” said first author Dr. Marina Lafarga Magro, Postdoctoral Researcher at the University of Warwick. “This represents one of the best characterized samples of close in planets and will help us identify the most promising systems for future study.”
Among the confirmed planets are several especially important groups:
Ultra-short-period planets that orbit their stars in less than 24 hours
“Neptunian desert” planets, a rare type found in a region where few planets are expected
Multi-planet systems with tight orbits, including newly discovered pairs around the same star.
