Collecting data without a good way to analysis it is arguably a huge waste of time, computing power and server space. The problem is that for humans to wade through big data it is almost impossible. This is where machine learning or artificial intelligence programs can really shine.
This is the case for NASA and the data they have been collecting from their Kepler Space Telescope. They have years worth of data but no real way to mine it to look for previously unknown planets. So NASA turned to who else but Google!
Google has a division name Google AI dedicated to just artificial intelligence and machine learning science. Here is there mission statement according to Google. This does open up a whole new meaning for the term ” Google It” right?
Our mission is to organize the world’s information and make it universally accessible and useful, and AI is enabling us to do that in incredible new ways – solving problems for our users, our customers, and the world.
More from Google Than Google AI.
Google was also one of the first companies to develop a commercial version of virtual reality glasses and an almost free version called Google Cardboard to allow people to be introduced to the world of 3D and Virtual Reality. Who knew they are also into artificial intelligence and a government department like NASA would need their help.
I have to admit when officials at NASA and Google AI, made a press release that in a few days they would be making a statement about new findings from the Keplar Space Telescope, I hoped it was going to be that they had found life on other planets, or maybe that the strange cigar shaped object that is racing across our solar system is really an alien craft. Oh, well. They probably would not tell us if it was…
A space rock whose strange cigar-shape led to speculations it could be an alien spacecraft is simply an asteroid, British scientists have concluded. The object, known as ‘Oumuamua’ was spotted on October 19th by the University of Hawaii’s Pan-STARRS1
Our solar system now is tied for most number of planets around a single star, with the recent discovery of an eighth planet circling Kepler-90, a Sun-like star 2,545 light-years from Earth. The planet was discovered in data from NASA’s Kepler Space Telescope.
The newly-discovered Kepler-90i which is a sizzling hot, rocky planet that orbits its star once every 14.4 days was found using machine learning from Google AI. 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 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 came about after researchers Christopher Shallue and Andrew Vanderburg 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.
With the discovery of an eighth planet, the Kepler-90 system is the first to tie with our solar system in number of planets. Credits: NASA/Wendy Stenzel
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 percent 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.
“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 Vanderburg, a NASA Sagan Postdoctoral Fellow and astronomer at the University of Texas at Austin.
Shallue, a senior software engineer with Google’s research team Google AI, came up with the idea to apply a neural network to Kepler data. He became interested in exoplanet discovery after learning that astronomy, like other branches of science, is rapidly being inundated with data as the technology for data collection from space advances.
“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. However, the weakest signals often are missed using these methods. Shallue and Vanderburg thought there could be more interesting exoplanet discoveries faintly lurking in the data.
First, they 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 percent 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.
“We got lots of false positives of planets, but also potentially more real planets,” said Vanderburg. “It’s like sifting through rocks to find jewels. If you have a finer sieve then you will catch more rocks but you might catch more jewels, as well.”
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 paper reporting these 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.
Ames manages the Kepler and K2 missions for NASA’s Science Mission Directorate in Washington. NASA’s Jet Propulsion Laboratory in Pasadena, California, managed Kepler mission development. Ball Aerospace & Technologies Corporation operates the flight system with support from the Laboratory for Atmospheric and Space Physics at the University of Colorado in Boulder. This work was performed through the Carl Sagan Postdoctoral Fellowship Program executed by the NASA Exoplanet Science Institute.