Why don't planets collide more often? Of all the possible ways a planet orbits, how many configurations will remain stable for a billion years of a star’s life cycle? Questions like these are processed by artificial intelligence to predict which planetary systems will survive.
Rejecting the possibility of instability (all configurations that would cause a collision), will leave a clearer view of the planetary system around other stars. But it's not as easy as it sounds.
"Separating a stable from an unstable configuration turns out to be an interesting and very difficult problem," said Daniel Tamayo, NASA Hubble Fellowship Program Sagan Fellow, an astrophysicist at Princeton.
To ensure a stable planetary system, astronomers need to calculate the motions of several planets that have interacted with each other for billions of years and examine every possible configuration for stability. This is an effort that is difficult to calculate.
Since the time of Isaac Newton, astronomers have struggled with the problem of orbital stability. Although their struggles contributed to many mathematical revolutions, including calculus and chaos theory, no one found a way to predict stable configurations theoretically. Modern astronomers still have to “force” calculations, even with supercomputers, not abacus or slide rules.
Calculation in a short time
Tamayo realized that he was able to speed up this process by combining a simple planetary dynamic interaction model with machine learning methods. This allows for the rapid removal of large unstable orbital configurations. Calculations that initially took tens of thousands of hours can now be done in minutes.
"We can't say definitively 'this planetary system will be fine, the others will explode soon.' He said.
Instead of simulating a specific configuration for one billion orbits (e.g., a brute force approach takes about 10 hours), the Tamayo model simulates 10,000 orbits and only takes a fraction of a second.
From this brief snippet, they calculated 10 summary metrics that capture the resonance dynamics of the system. Finally, they trained machine learning algorithms to predict from these 10 features whether the configuration would remain stable if they let it continue to reach one billion orbits.
“We call the model SPOCK - Planetary Orbital Configuration Classification Stability - in part because it determines whether the system will‘ live long and prosper, ’Tamayo said.
SPOCK determines the long-term stability of planetary configurations about 100,000 times faster than previous approaches, solving computational problems.
Tamayo warns that he and his colleagues have not yet “solved” the general problem of planetary stability. SPOCK reliably identifies rapid instability in compact systems, which they consider most important in an effort to perform stability -constrained characterization.
“This new method will provide a clearer window into the orbital architecture of planetary systems beyond our own methods,” Tamayo said.
Jessie Christiansen, an astrophysicist at NASA’s Exoplanet Archives who was not involved in the study, said: ‘SPOCK is very helpful in understanding some of the faint and distant planetary systems recently seen by the Kepler telescope.
"It's hard to limit their nature with existing instruments. Are they rocky planets, ice giants, or gas giants? Or something new? At the very least, these new tools will allow us to push the composition and configuration of dynamically unstable planets, and allows us to do it more accurately and on a much larger scale than ever before. "
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