A machine learning model is scouring the cosmos for undiscovered planets.

What’s new: Astronomers from the University of Warwick developed a system that learned to identify faraway worlds in a dataset of thousands of candidates.

How it works: Astronomers often find planets outside Earth’s solar system, or exoplanets, by scanning the sky for stars that flicker, which indicates that a planet might pass in front of them. Given a set of possible planets, the researchers used machine learning to sift out false positives caused by camera errors, cosmic rays, or stars eclipsing one another to identify the real deal.

  • The researchers trained several models using data that represents thousands of verified exoplanets among thousands of false positives, gathered by the retired Kepler space telescope. They tested the models on a large dataset of confirmed candidates.
  • Out of nine different models, four — a Gaussian process classifier, random forest, extra trees classifier, and neural network — achieved top scores for area under the curve (AUC), precision, and recall.
  • The authors double-checked their models’ conclusions against an established exoplanet validation technique, which didn’t always agree. They advocate using both approaches rather than relying on one or the other.

Results: In some test cases when the authors’ models and the earlier technique disagreed strongly, their approach identified confirmed exoplanets that the old approach missed. Similarly, the authors identified a preponderance of confirmed false positives that the earlier approach classified as planets with greater than 99 percent confidence.

What’s next: The authors’ models analyzed 2,680 unconfirmed candidates and classified 83 likely exoplanets. The earlier technique agreed that 50 of them were bona fide exoplanets — prime targets for further study. The authors hope to apply their method to the dataset collected from NASA’s recent Transiting Exoplanet Survey Satellite mission, which contains thousands more unconfirmed candidates.

Why it matters: Any indirect method of determining an exoplanet’s existence is bound to be imperfect. By combining approaches, researchers aim to improve the likelihood that what they take to be planets really are, so scientists can proceed with deeper investigations.

We’re thinking: Outer space offers an endless supply of data, and machine learning is the ultimate tool for crunching it. A match made in the heavens!


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