Artificial intelligence predicts which planetary systems will survive
An astrophysicist sharpens our view of orbital architectures

Date:
July 13, 2020
Source:
Princeton University
Summary:
An astrophysicist has found a new method for determining the
long-term stability of planetary configurations that is up to
100,000 times faster than the previous approach. This breaks the
computational bottleneck and enables sharper views of the orbital
architectures of exoplanetary systems.



FULL STORY ==========================================================================
Why don't planets collide more often? How do planetary systems --
like our solar system or multi-planet systems around other stars --
organize themselves? Of all of the possible ways planets could orbit,
how many configurations will remain stable over the billions of years
of a star's life cycle?

========================================================================== Rejecting the large range of unstable possibilities -- all the
configurations that would lead to collisions -- would leave behind a
sharper view of planetary systems around other stars, but it's not as
easy as it sounds.

"Separating the stable from the unstable configurations turns out to be a fascinating and brutally hard problem," said Daniel Tamayo, a NASA Hubble Fellowship Program Sagan Fellow in astrophysical sciences at Princeton. To
make sure a planetary system is stable, astronomers need to calculate
the motions of multiple interacting planets over billions of years and
check each possible configuration for stability -- a computationally prohibitive undertaking.

Astronomers since Isaac Newton have wrestled with the problem of orbital stability, but while the struggle contributed to many mathematical
revolutions, including calculus and chaos theory, no one has found a
way to predict stable configurations theoretically. Modern astronomers
still have to "brute-force" the calculations, albeit with supercomputers instead of abaci or slide rules.

Tamayo realized that he could accelerate the process by combining
simplified models of planets' dynamical interactions with machine learning methods. This allows the elimination of huge swaths of unstable orbital configurations quickly -- calculations that would have taken tens of
thousands of hours can now be done in minutes. He is the lead author
on a paper detailing the approach in the Proceedings of the National
Academy of Sciences. Co-authors include graduate student Miles Cranmer
and David Spergel, Princeton's Charles A. Young Professor of Astronomy
on the Class of 1897 Foundation, Emeritus.

For most multi-planet systems, there are many orbital configurations
that are possible given current observational data, of which not all
will be stable.

Many configurations that are theoretically possible would "quickly"
-- that is, in not too many millions of years -- destabilize into a
tangle of crossing orbits. The goal was to rule out those so-called
"fast instabilities." "We can't categorically say 'This system will
be OK, but that one will blow up soon,'" Tamayo said. "The goal instead
is, for a given system, to rule out all the unstable possibilities that
would have already collided and couldn't exist at the present day."


========================================================================== Instead of simulating a given configuration for a billion orbits --
the traditional brute-force approach, which would take about 10 hours -- Tamayo's model instead simulates for 10,000 orbits, which only takes a
fraction of a second. From this short snippet, they calculate 10 summary metrics that capture the system's resonant dynamics. Finally, they train
a machine learning algorithm to predict from these 10 features whether
the configuration would remain stable if they let it keep going out to
one billion orbits.

"We called the model SPOCK -- Stability of Planetary Orbital
Configurations Klassifier -- partly because the model determines whether systems will 'live long and prosper,'" Tamayo said.

SPOCK determines the long-term stability of planetary configurations
about 100,000 times faster than the previous approach, breaking
the computational bottleneck. Tamayo cautioned that while he and his
colleagues haven't "solved" the general problem of planetary stability,
SPOCK does reliably identify fast instabilities in compact systems,
which they argue are the most important in trying to do stability
constrained characterization.

"This new method will provide a clearer window into the orbital
architectures of planetary systems beyond our own," Tamayo said.

But how many planetary systems are there? Isn't our solar system the
only one? In the past 25 years, astronomers have found more than 4,000
planets orbiting other stars, of which almost half are in multi-planet
systems. But since small exoplanets are extremely challenging to detect,
we still have an incomplete picture of their orbital configurations.

"More than 700 stars are now known to have two or more planets orbiting
around them," said Professor Michael Strauss, chair of Princeton's
Department of Astrophysical Sciences. "Dan and his colleagues have found
a fundamentally new way to explore the dynamics of these multi-planet
systems, speeding up the computer time needed to make models by factors
of 100,000. With this, we can hope to understand in detail the full
range of solar system architectures that nature allows." SPOCK is
especially helpful for making sense of some of the faint, far-distant
planetary systems recently spotted by the Kepler telescope, said Jessie Christiansen, an astrophysicist with the NASA Exoplanet Archive who was
not involved in this research. "It's hard to constrain their properties
with our current instruments," she said. "Are they rocky planets, ice
giants, or gas giants? Or something new? This new tool will allow us to
rule out potential planet compositions and configurations that would be dynamically unstable - - and it lets us do it more precisely and on a substantially larger scale than was previously available."

========================================================================== Story Source: Materials provided by Princeton_University. Original written
by Liz Fuller- Wright. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Daniel Tamayo, Miles Cranmer, Samuel Hadden, Hanno Rein, Peter
Battaglia,
Alysa Obertas, Philip J. Armitage, Shirley Ho, David Spergel,
Christian Gilbertson, Naireen Hussain, Ari Silburt, Daniel
Jontof-Hutter and Kristen Menou. Predicting the long-term
stability of compact multi-planet systems. PNAS, 2020 DOI:
10.1073/pnas.2001258117 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/07/200713155000.htm

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