A machine-learning algorithm trained on synthetic planetary systems has been let loose — and in the process has identified nearly four dozen real stars that have a high probability of hosting a rocky planet in their habitable zone.
"The model identified 44 systems that are highly likely to harbor undetected Earth-like planets," said Jeanne Davoult, an astronomer at the German Aerospace Agency DLR, in a statement. "A further study confirmed the theoretical possibility for these systems to host an Earth-like planet."
Often, "Earth-like" worlds — Earth-like in the sense that they have a similar mass to our planet and reside in their star's habitable zone — are found by chance, often in huge surveys that watch thousands of stars for transiting planets. However, astronomers would like to even the odds of finding Earth-size habitable-zone planets, and hence require a more targeted means of finding candidate stars.
This is what led Davoult to develop the algorithm while she was at the University of Bern in Switzerland. Like all models based on machine-learning algorithms that learn to identify patterns and make predictions based on where the algorithm sees those patterns, it had to be trained on data. The problem, however, is that although nearly 6,000 exoplanets have been discovered so far, the information that we have on these worlds is patchy. And in general, even 6,000 worlds is not enough to train the algorithm.
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