The media was abuzz last week with the latest NASA news conference. A neural network — a form of artificial intelligence or machine learning — developed at Google had found two planets in data previously collected by NASA’s prolific Kepler Space Telescope. It’s a technique that could ultimately track-down our most Earth-like planets.
The new exoplanets orbit stars already known to host planetary systems, Kepler-90 and Kepler-80. While both are only slightly larger than the Earth, their two-week orbits makes these worlds too hot to be considered likely candidates for hosting life. Moreover, the systems are thousands of light years away, putting the planets out of range of atmospheric studies that could test their habitability.
With over 3,500 exoplanets already discovered, you might be forgiven for finding these additions underwhelming. However, while other planets in the same system have been known about for several years, these two Earth-sized worlds were previously overlooked. The difference is not a new telescope, but an exploration of the data with a different kind of brain.
The Kepler Space Telescope searches for planets using the transit technique; detecting small dips in amount of starlight as the planet passes in front of the star. As planets are much smaller than stars, picking out this tiny light drop is a tricky task. For a Jupiter-sized planet orbiting a star like our Sun, the decrease in brightness is only about 1%. For an Earth-sized planet, the signal becomes so small it is right on the edge of what Kepler is able to detect. This makes their dim wink extremely difficult to spot in the data.
The discovery paper published in the Astronomical Journal combined the expertise of Christopher Shallue from Google’s artificial intelligence project, Google Brain, and Andrew Vanderburg, a NASA Sagan Postdoctoral Fellow and astronomer at the University of Texas at Austin.… Read more