Modeling the gravitational interaction between celestial bodies is hard work. Two objects is relatively easy, but add a third one in and things get so complex that physicists and mathematicians have been discussing it for the last 253 years. And when you have a whole stellar system with many planets, things get extra tricky.
To understand how our own solar system formed and how systems like TRAPPIST-1 exist, we ought to understand what orbital configuration allows planets to not smash into each other or spiral down into their star. As reported in the Proceedings of the National Academy of Sciences, researchers have used the power of machine learning algorithms to model star systems more quickly.
“Separating the stable from the unstable configurations turns out to be a fascinating and brutally hard problem,” lead author Dr Daniel Tamayo, a NASA Hubble Fellowship Program Sagan Fellow in astrophysical sciences at Princeton, said in a statement.
“We can’t categorically say ‘This system will be OK, but that one will blow up soon,’” Tamayo explained. “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. We called the model SPOCK — Stability of Planetary Orbital Configurations Klassifier — partly because the model determines whether systems will ‘live long and prosper!’”
SPOCK's powers lie in its ability to make calculations that would normally take tens of thousands of hours in just minutes. The secret is a mixture of assumptions and the ability of machine learning to make educated guesses. Usual approaches tend to simulate a billion orbits, which is computationally taxing and takes around 10 hours.
The SPOCK system instead focuses on just 10,000 orbits in a fraction of a second. From those 10,000 orbits, 10 measurements were extracted that characterized how stable the system has been over those orbits. The algorithm was then trained to extrapolate how stable the system will be for the 1 billion orbits. The final prediction is achieved 100,000 times faster than previous methods.
The algorithm is not a complete solution of planetary stability but it can provide important insights, especially in the case of systems that are currently too far and faint to be characterized in detail.
“It’s hard to constrain their properties with our current instruments,” explained Dr Jessie Christiansen, an astrophysicist with the NASA Exoplanet Archive who was not involved in this research. “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.”
So far, astronomers have confirmed 4,281 exoplanets in 3,163 systems. Of these, 701 stellar systems have more than one planet.