Artificial Intelligence, NASA Data Used to Discover Eighth Planet Circling Distant Star

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 – a sizzling
hot, rocky planet
that orbits its star once every 14.4 days – was found using
machine learning from Google. 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 changes in
starlight caused by planets beyond our solar system, known
as
exoplanets.

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.

NASA will host a
Reddit Ask Me Anything
at noon PST (3
p.m. EST) today on
this discovery.

“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 miniscule
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.

Machine learning has
previously been used in searches of the
Kepler database, and this continuing
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 – promise 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.

For more information on this announcement,
visit:

https://www.nasa.gov/mediaresources

For
more information about the Kepler mission, visit:

https://www.nasa.gov/kepler

News Media Contact

Felicia Chou
NASA Headquarters, Washington
202-358-0257
felicia.chou@nasa.gov

Alison Hawkes
Ames Research Center, California’s Silicon Valley
650-604-0281
alison.j.hawkesbak@nasa.gov

Elizabeth Landau
Jet Propulsion Laboratory, Pasadena, California
818-354-6425
elizabeth.r.landau@jpl.nasa.gov

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