AI helps boffins clock second Solar System

16 December, 2017, 06:01 | Author: Marjorie Miles
  • A slide from astronomer Andrew Vanderburg's Nov. 9 presentation.       Andrew Vanderburg UT Austin  Caltech

This new Solar system rival provides evidence that a similar process occurred within Kepler-90 that formed our very own planetary neighbourhood: small terrestrial worlds close to the host star, and larger gassy planets further away.

An artist's concept depicts select planetary discoveries made to date by NASA's Kepler space telescope. It's small, "sizzling" hot and rocky, whirling around its star every 14.4 days.

NASA provided the AI with data collected by its Kepler Space Telescope. "Today, Kepler confirms that stars can have large families of planets just like our solar system".

This eighth planet orbits the star known as Kepler-90. Consequently, Kepler-90i has an average surface temperature of 800 degrees Fahrenheit, and is not a likely place for life as we know it.

"The Kepler-90 star system is like a mini version of our solar system". Kepler-90h, the outermost planet in the system, orbits its star at a similar distance as Earth does to the Sun.

With the idea of eventually differentiating among exoplanets, Christopher Shallue, senior software engineer at Google AI in California, and Andrew Vanderburg, astronomer and NASA Sagan postdoctoral fellow at the University of Texas, Austin, trained a computer how to differentiate between images of cats and dogs. And while it was the eighth planet discovered in the system, it is actually third in line from the sun-just like Earth.

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After this discovery, the Kepler-90 solar system has come at par with our own in terms of number of planets revolving around a single star, said NASA.

While machine learning has been applied before to the Kepler telescope΄s data, it is believed to be the first time that the technology has unearthed a new world.

If the researchers lose track of weaker signals or miss them, they would also be losing the possibility to find new exoplanets.

Would humans have found the planet without machine learning?

Planets are detected using the transit method; when an exoplanet passes across a star as seen from a telescope, that star dims slightly.

In this case, software learned differences between planets and other objects by analyzing thousands of data points, achieving 96 percent accuracy, NASA said at a news conference. 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.

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The planets in our system are far more spread out, however. He also compared the finding of exoplanets to sifting through rocks with a fine sieve to find jewels.

First, they trained the neural network to identify transiting exoplanets using a set of 15,000 previously vetted signals from the Kepler exoplanet catalogue.

Researchers had known that seven planets were orbiting the star.

Dive into the details of Shallue and Vanderburg's research paper, accepted for publication in The Astronomical Journal.

Shallue and Vanderburg plan to keep up the hunt, using the program to scour the 150,000-plus stars observed by Kepler.

"This finding shows that our data will be a treasure trove available to innovative researchers for years to come". "I'm sure there are more firsts in the data waiting for people to find them".

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