Starship Asterisk*

Machine-learning methods can predict the long-term stability of planetary configurations 100,000 times faster than previous approaches.

While astronomers have confidently detected three planets in the Kepler-431 system,
little is known about the shapes of the planetary orbits. The left-hand image shows
many superimposed orbits for each planet (yellow, red and blue) consistent with
observations. Using machine learning methods, researchers removed all unstable
configurations that would have resulted in planetary collisions and would not be
observable today, leaving only the stable orbits (right-hand image). Using previous
methods, this process would have taken more than a year of computing time. The
new method instead takes just 14 minutes. Credit: D. Tamayo et al/PNAS 2020

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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,” says Daniel Tamayo ... 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. ...

Tamayo and his colleagues realized that they 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. ...

• arXiv.org > astro-ph > arXiv:2007.06521 > 13 Jul 2020