Scientists have identified 50 potential planets with the help of Artificial Intelligence (AI). They used this technique for the first time and succeeded in analysing these planets and determining which ones are real and which are fake. These planets range from worlds as large as Neptune to smaller than the Earth, with orbits as long as 200 days to as little as a single day.

The discovery was made by astronomers and computer scientists at the University of Warwick. They built a machine learning-based algorithm and trained it to differentiate between real planets using two large samples of confirmed planets and false positives from NASA’s Kepler mission.

When the researchers used this algorithm on a dataset of still unconfirmed planetary candidates from Kepler, they found as many as 50 new confirmed planets.

This is the first time machine learning has helped in determining whether a candidate was a true planet, bringing us one step ahead in planet validation. 

Dr David Armstrong, from the University of Warwick Department of Physics, said, “In terms of planet validation, no-one has used a machine learning technique before. 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. 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.”

“Almost 30% of the known planets to date have been validated using just one method, and that’s not ideal. Developing new methods for validation is desirable for that reason alone. But machine learning also lets us do it very quickly and prioritise candidates much faster,” added Dr Armstrong.