Astronomers at the University of Warwick have identified more than 100 exoplanets, including 31 newly identified worlds, using a new artificial intelligence system. The team applied this tool to data from NASA’s Transiting Exoplanet Survey Satellite (TESS). This mission scans the sky for the slight dip in starlight that occurs when a planet passes in front of its host star.
Their findings are: MNRASThese are based on a detailed analysis of observations from more than 2.2 million stars collected during the first four years of TESS. The researchers focused on planets that orbit very close to their stars, completing a complete orbit within 16 days. This approach yielded one of the most accurate measurements to date of how common these short-period planets are.
“Using the newly developed RAVEN pipeline, we were able to validate 118 new planets and over 2,000 high-quality planet candidates, of which nearly 1,000 were completely new,” said lead author Dr. Marina Lafarga-Magro, postdoctoral researcher at the University of Warwick. “This is one of the best-characterized samples of planets and will help identify the most promising systems for future research.”
Rare and extreme planet types identified
The newly identified planets include some particularly interesting categories. Some planets are extremely short-period planets, orbiting the star in less than 24 hours. The other planets belong to the so-called “Neptunian desert,” an area where current theory would predict that there would be very few planets. The study also revealed a tightly packed multi-planetary system, including a previously unknown pair of planets orbiting the same star.
How RAVEN improves planet detection
Modern planetary exploration missions often flag thousands of potential planets, but it’s still difficult to determine which signals are real. Many false signals can mimic planets, including eclipsed binary stars.
“The challenge lies in determining whether the dimming is actually caused by planets orbiting the star or by something else, such as a binary eclipse, and RAVEN is trying to answer that. Its strength lies in the fact that it has hundreds of thousands of realistically simulated planets, We trained a machine learning model to identify patterns in the data that can tell us the types of events we detect. This is what the AI is good at. ” said Dr. Andreas Hadjigeorgiou of the University of Warwick, who led the pipeline development.
“Furthermore, RAVEN is designed to handle the entire process at once, from signal detection to machine learning scrutiny and statistical validation. This gives pipelines an added advantage over modern tools that focus only on specific parts of the workflow.”
Dr David Armstrong, Associate Professor at the University of Warwick and senior co-author of the RAVEN study, said: “RAVEN allows us to consistently and objectively analyze huge data sets. The pipeline is well tested and carefully validated, so this is not just a list of potential planets, but is also reliable enough to serve as a sample for mapping the prevalence of different types of planets around Sun-like stars.”
Measuring how much planets actually have in common
Using this carefully validated dataset, researchers were able to look beyond individual findings and examine broader patterns. within one’s circle MNRAS The study measured how often close planets occur around Sun-like stars and mapped the results by orbital period and planet size in an unprecedented level of detail.
The researchers found that about 9 to 10 percent of Sun-like stars have planets approaching them. This is consistent with earlier results obtained by NASA’s Kepler mission, a space telescope that previously measured planet occurrence rates, but the new analysis reduces the uncertainty by up to a factor of 10.
The researchers also directly measured for the first time how rare “Neptunian desert” planets are, finding that they occur in only 0.08% of cases around Sun-like stars.
“For the first time, we have been able to give an accurate figure of how empty this ‘desert’ is,” said Dr Caimin Quy, a postdoctoral fellow at the University of Warwick and lead author of the population study. “These measurements show that TESS can rival, and in some cases surpass, Kepler in studying planetary populations.”
A new era of planet discovery
Taken together, these studies highlight how advances in artificial intelligence are changing astronomy. By combining massive datasets with machine learning, researchers can discover new planets while challenging real-world data to improve the tools themselves.
The team also released an interactive catalog and tools to help other scientists explore the results and identify promising targets for follow-up observations using ground-based telescopes and future missions such as ESA’s PLATO.
What is Raven?
RAVEN is an automated system designed to address one of astronomy’s biggest challenges, turning vast amounts of space telescope data into trusted discoveries. It scans data from millions of stars to find small dips in brightness caused by planets passing in front of us. The system then uses artificial intelligence trained on realistic simulations to filter out false signals such as binary stars and instrument noise before reviewing the statistically strongest candidates.
Importantly, RAVEN will also assess which types of planets are easier or harder to detect, helping researchers correct for hidden biases. This means not only speeding up the discovery of new worlds, but also producing cleaner, more reliable data sets that can be used to answer larger questions about how common different types of planets are across the galaxy.
