Artificial intelligence helps NASA finds exoplanets in constellation Draco

Our solar system is now tied for the most number of planets around a single star, with the new discovery of an eighth planet circling Kepler-90, a Sun-like star more than 2,545 light years away.

The planet was discovered in data from NASA’s Kepler Space Telescope. The new planet is thought to be a “sizzling hot, rocky” and orbits its Sun just over every two weeks, and was found through a machine learning from Google. Machine learning is an approach to artificial intelligence in which computers “learn.” In this case, computers learned to identify planets by finding in Kepler data instances where the telescope recorded signals from planets beyond our solar system, known as exoplanets, according to a NASA news release on the findings.

“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, director of NASA’s Astrophysics Division in Washington. “This finding shows that our data will be a treasure trove available to innovative researchers for years to come.” 

The discovery was made possible after researchers Christopher Shale and Andrew Vanderburg trained a computer to “learn” how to identify exoplanets in the light readings from the telescope. Light readings observed the minuscule change in brightness are captured when a planet passes in front of, or transited, a star. Both researchers said the concept was inspired by the way neurons connect in the human brain, with the artificial neural network sifting through data from the previously-missed planet in Draco.

AI has been used previously in Kepler data analysis, the new type of network approach could be a promising tool in finding some of the weakest signals of distant worlds. But don’t get excited for any hopes of sustainable life in the Kepler system. The new exoplanet is so close to its star that its average surface temperature is believe to exceed 800 degrees Fahrenheit, similar to Mercury. But the straggler of the system, Kepler-90h, orbits at a similar distance to its star as Earth to our Sun.

“The Kepler-90 star system is like a mini version of our solar system. You have small planets inside and big planets outside, but everything is scrunched in much closer,” Vanderburg said. 

Vanderburg is a NASA Sagan Postdoctoral Fellow and astronomer at the University of Texas at Austin. Shallue is a senior software engineer with Google’s research team Google AI. He came up with the idea to apply the neural network concept to Kepler data.

“In my spare time, I started googling for ‘finding exoplanets with large data sets’ and found out about the Kepler mission and the huge data set available,” said Shallue. “Machine learning really shines in situations where there is so much data that humans can’t search it for themselves.”

Kepler’s four-year dataset consists of 35,000 possible planetary signals. Automated tests, and sometimes human eyes, are used to verify the most promising signals in the data.

Kepler-90i wasn’t the only jewel this neural network sifted out. In the Kepler-80 system, they found a sixth planet. This one, the Earth-sized Kepler-80g, and four of its neighboring planets form what is called a resonant chain – where planets are locked by their mutual gravity in a rhythmic orbital dance. The result is an extremely stable system, similar to the seven planets in the TRAPPIST-1 system.

Their research reporting the findings has been accepted for publication in The Astronomical Journal. Shallue and Vanderburg plan to apply their neural network to Kepler’s full set of more than 150,000 stars.

Kepler has produced an unprecedented data set for exoplanet hunting. After gazing at one patch of space for four years, the spacecraft now is operating on an extended mission and switches its field of view every 80 days.

“These results demonstrate the enduring value of Kepler’s mission,” said Jessie Dotson, Kepler’s project scientist at NASA’s Ames Research Center in California’s Silicon Valley. “New ways of looking at the data – such as this early-stage research to apply machine learning algorithms – promises to continue to yield significant advances in our understanding of planetary systems around other stars. I’m sure there are more firsts in the data waiting for people to find them.”

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