The Kepler system is an nearly picture-perfect image of our own with inner rocky planets and outer gaseous giants, NASA have said, but its capacity for life is not as high.
Paul Hertz, Director of the Astrophysics Division, said: "The planet Kepler-90i is not likely to have life since it is so close to the star Kepler-90 that the surface temperature is 800 Fahrenheit".
An eighth planet has been discovered orbiting the distant star Kepler-90, proving that our eight-planet solar system isn't unique after all.
Just as we expected, there are exciting discoveries lurking in our archived Kepler data, waiting for the right tool or technology to unearth them. "This finding shows that our data will be a treasure trove available to innovative researchers for years to come", said Hertz.
He believes that machine learning promises the possibility of discovering more distant worlds based on the signals they transmit no matter how weak they may be.
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"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", said Andrew Vanderburg, a NASA Sagan Postdoctoral Fellow and astronomer at the University of Texas at Austin.
"As the application of neutral networks to Kepler data matures, who knows what might be discovered", said Jessie Dotson, a NASA project scientist for the Kepler space telescope. He became engaged in exoplanet findings after studying that astronomy, like other departments of science, is quickly being inundated with data as the technology for data acquisition from space advances.
"Kepler-90i is not a place I'd like to go visit, though", said Vanderburg.
Like all machine learning systems, this one was fed measurements from previously identified exoplanets to work out what differentiates real signals from coincidental blips.
Kepler's four-year dataset consists of 35,000 probable planetary signals. Automated tests, and sometimes human eyes, are used to verify the most promising signals in the data. Some planets are more obvious than others, and the goal here was to turn the algorithm loose on digging through past measurements for weak signals that had been missed. The astronomers trained the programme on a set of about 15,000 signals, and it identified planets correctly. In the test set, the neural network accurately recognized true planets and false positives 96 percent of the time. Together with Vanderburg, he then trained a computer to look for dips in brightness as these are often clues that a planet is transiting a star.
"It would nearly be surprising to me if there weren't any more planets in around that star". "It's like filtering through rocks to find jewels".