“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.
Calculations about the motions of multiple interacting planets over billions of years and finding out each possible configuration for stability will help astronomers to make sure that the planetary system is stable.
Astronomers since Isaac Newton have always struggled with the problem of orbital stability even thought the struggle has lead to mathematical revolutions, including calculus and chaos theory, no one could find a way to predict stable configurations theoretically. Tamayo and his colleagues combined simplified models of planets dynamical interactions with machine learning methods. This leads to the elimination of huge swaths of unstable orbital configurations quickly, which would otherwise taken tens of thousands of hours now can be done in minutes. This helps to predict the fate of an abundant class of known extrasolar planetary systems.
“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. “Machine learning methods have opened up new ground in what we can do in inferring the properties of planetary systems,” Ho said.
Tamayo reminds that that while he and his colleagues couldn’t solve the general problem of planetary stability, SPOCK does 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.
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