To address this issue, AI researchers from Google and the University of Texas at Austin trained a machine-learning algorithm to identify planets using 15,000 signals generated by the Kepler space telescope.
"I became interested in applying neural networks to astronomy when I learned that the Kepler mission had collected so much data that it was impossible for scientists to examine it all manually", said Christopher Shallue, a senior software engineer with Google's research team.
Jessie Dotson, Kepler's project scientist at NASA, said she is certain that this discovery signals similar ones to come in the future.
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"For the first time since our solar system planets were discovered thousands of years ago, we know for sure that our solar system is not the sole record holder for the most planets", Vanderburg said.
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".
NASA's Kepler Space Telescope has been searching for exoplanets, or planets that exist outside our own solar system, since it launched in 2009. So far, the data set has about 35,000 such signals.
The small rocky planets in the planetary system are located nearer to its sun just like Mercury, Venus, Earth, and Mars are closer to the sun. To verify the most promising signals of planets, automated tests, or sometimes human eyes, are typically used, but often the weakest signals are missed during this process.
Enlarge / The dip in brightness of Kepler-80 and Kepler-90 as these planets pass in front of them. Not all of them denote planets, but the program was able to detect these false positives with 96 percent accuracy.
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While machine learning has been used before in the search for exoplanets - planets beyond our solar system - it's believed to be the first time an artificial neural network like this has been used to find a new world.
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. For instance, in the solar system itself, the outer planets formed in the solar system's cooler part because this is where ice can remain solid and clump together to make more massive planets.
However Keppler-90i wasn't the only discovery made through the machine learning sifting. The discovery is notable because five planets in the system (including Kepler-80g) form a resonant chain.
Shallue and Vanderburg plan to use their neural network to search through the Kepler telescope's four-year dataset, which includes more than 150,000 stars. The result is an extremely stable system, similar to the seven planets in the TRAPPIST-1 system.
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