 "Google AI helps Nasa discover solar system like ours")
NASA has used Google's artificial intelligence (AI) to discover a record-tying eighth exoplanet circling a Sun-like star 2,545 light-years from Earth, marking the first finding of an eight-planet solar system like ours.
Kepler-90i - a sizzling hot, rocky planet that orbits its star once every 14.4 days - was found using machine learning from Google to scour data from NASA's planet-hunting Kepler Telescope.
"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.
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.
"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 NASAs Astrophysics Division in Washington.
"This finding shows that our data will be a treasure trove available to innovative researchers for years to come," said Hertz.
The researchers trained a computer to learn how to identify exoplanets in the light readings recorded by Kepler
the minuscule change in brightness captured when a planet passed in front of, or transited, a star.
Inspired by the way neurons connect in the human brain, this artificial "neural network" sifted through Kepler data and found weak transit signals from a previously-missed eighth planet orbiting Kepler-90, in the constellation Draco.
While machine learning has previously been used in searches of the Kepler database, this research demonstrates that neural networks are a promising tool in finding some of the weakest signals of distant worlds.
Other planetary systems probably hold more promise for life than Kepler-90.
About 30 per cent larger than Earth, Kepler-90i is so close to its star that its average surface temperature is believed to exceed 800 degrees Fahrenheit, on par with Mercury.
Its outermost planet, Kepler-90h, orbits at a similar distance to its star as Earth does to the Sun.
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. However, the weakest signals often are missed using these methods.
The researchers first trained the neural network to identify transiting exoplanets using a set of 15,000 previously-vetted signals from the Kepler exoplanet catalogue.
In the test set, the neural network correctly identified true planets and false positives 96 per cent of the time.
Then, with the neural network having "learned" to detect the pattern of a transiting exoplanet, the researchers directed their model to search for weaker signals in 670 star systems that already had multiple known planets.
Their assumption was that multiple-planet systems would be the best places to look for more exoplanets.
Kepler-90i was not 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 neighbouring 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.
Kepler-90i - a sizzling hot, rocky planet that orbits its star once every 14.4 days - was found using machine learning from Google to scour data from NASA's planet-hunting Kepler Telescope.
"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.
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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.
"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 NASAs Astrophysics Division in Washington.
"This finding shows that our data will be a treasure trove available to innovative researchers for years to come," said Hertz.
The researchers trained a computer to learn how to identify exoplanets in the light readings recorded by Kepler
the minuscule change in brightness captured when a planet passed in front of, or transited, a star.
Inspired by the way neurons connect in the human brain, this artificial "neural network" sifted through Kepler data and found weak transit signals from a previously-missed eighth planet orbiting Kepler-90, in the constellation Draco.
While machine learning has previously been used in searches of the Kepler database, this research demonstrates that neural networks are a promising tool in finding some of the weakest signals of distant worlds.
Other planetary systems probably hold more promise for life than Kepler-90.
About 30 per cent larger than Earth, Kepler-90i is so close to its star that its average surface temperature is believed to exceed 800 degrees Fahrenheit, on par with Mercury.
Its outermost planet, Kepler-90h, orbits at a similar distance to its star as Earth does to the Sun.
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. However, the weakest signals often are missed using these methods.
The researchers first trained the neural network to identify transiting exoplanets using a set of 15,000 previously-vetted signals from the Kepler exoplanet catalogue.
In the test set, the neural network correctly identified true planets and false positives 96 per cent of the time.
Then, with the neural network having "learned" to detect the pattern of a transiting exoplanet, the researchers directed their model to search for weaker signals in 670 star systems that already had multiple known planets.
Their assumption was that multiple-planet systems would be the best places to look for more exoplanets.
Kepler-90i was not 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 neighbouring 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.