For the first four years after its launch in 2009, the Kepler space telescope was used to study potential Earth-like planets passing in front of stars. The telescope observed more than 200,000 stars, but a mechanical malfunction made it unable to focus on a single part of the sky, resulting in far more sporadic data collection. NASA officially retired Kepler last year.
To overcome this setback, more than 30,000 images with promising characteristics were collected and examined, and more than 22,000 were used to train the semi-supervised AI system. AstroNet K2 is reported to be 98 percent accurate in test data sets.
Members of the Google Brain team; the astronomy departments at the University of California, Berkeley and the University of Texas, Austin; and the Harvard-Smithsonian Center for Astrophysics shared the findings in a paper. They conclude that AstroNet K2 is "not yet ready to completely automatically detect and identify planet candidates" due to an abundance of false positives, but it could augment the efforts of astronomers working to better understand the universe.