When Kepler was first launched in 2009, astronomers didn’t know how common planets around other stars were. But within four years, Kepler generated a dataset of 35,000 possible planetary signals from just one relatively small area of the sky. Though some basic algorithms were used to sort and filter the data, human brains were still the primary workhorses for identifying exoplanets. This not only took a lot of valuable time, but also meant the weakest signals were often overlooked.Before the new neural network could analyze the Kepler data, researchers had to first train it to spot transiting exoplanets from Kepler’s light curves. Light curves show how the brightness of a star drops off when an orbiting planet passes in front of it.Using 15,000 previously confirmed exoplanet signals as flash cards, the neural network “learned” to correctly identify true planets. After the neural network knew what patterns it was looking for, the researchers turned it loose on 670 of Kepler’s weaker signals. In these weak signals, the AI found two likely exoplanets — Kepler-90i and Kepler-80g. The researchers pointed out the probability that the exoplanet detections were false positives is only 1 in 10,000.“Just as we expected, there are exciting discoveries lurking in our archived Kepler data, waiting for the right tool or technology to unearth them,” said Paul Hertz, Astrophysics Division director at NASA, in a press release . “This finding shows that our data will be a treasure trove available to innovative researchers for years to come.