Fifty potential planets have had their existence confirmed by a new machine learning algorithm.

For the first time, astronomers have used a process based on machine learning, a form of artificial intelligence, to analyse a sample of potential planets and determine which ones are real and which are ‘fakes’, or false positives, calculating the probability of each candidate to be a true planet.

Their results are reported in a new study published in the Monthly Notices of the Royal Astronomical Society, where they also perform the first large scale comparison of such planet validation techniques. Their conclusions make the case for using multiple validation techniques, including their machine learning algorithm, when statistically confirming future exoplanet discoveries.

Many exoplanet surveys search through huge amounts of data from telescopes for the signs of planets passing between the telescope and their star, known as transiting. This results in a telltale dip in light from the star that the telescope detects, but it could also be caused by a binary star system, interference from an object in the background, or even slight errors in the camera. These false positives can be sifted out in a planetary validation process.

Researchers from Warwick’s Departments of Physics and Computer Science, as well as The Alan Turing Institute, built a machine learning based algorithm that can separate out real planets from fake ones in the large samples of thousands of candidates found by telescope missions such as NASA’s Kepler and TESS.

It was trained to recognise real planets using two large samples of confirmed planets and false positives from the now retired Kepler mission. The researchers then used the algorithm on a dataset of still unconfirmed planetary candidates from Kepler, resulting in fifty new confirmed planets and the first to be validated by machine learning. Previous machine learning techniques have ranked candidates, but never determined the probability that a candidate was a true planet by themselves, a required step for planet validation.

Story Source: Materials provided by University of Warwick. Note: Content may be edited for style and length.