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Machine learning is used today for everything from detecting fraud and sorting spam in Google to recommending movies on Netflix. 

Now a team of University of Toronto researchers has developed a novel approach to using algorithms to determine whether planetary systems are stable. 

“Machine learning offers a powerful way to tackle a problem in astrophysics, and that’s predicting whether planetary systems are stable,” says Dan Tamayo, lead author of a report published online in the .

The postdoctoral researcher in the Centre for Planetary Science at �鶹��Ƶ Scarborough and his team have developed a method that's 1,000 times faster than traditional means of predicting solar system stability.

Planetary system stability can tell us a great deal about how these systems formed, Tamayo said, adding that it can also offer valuable new information about exoplanets that is not offered by current methods of observation. 

The findings could come in handy when analyzing data from NASA’s Transiting Exoplanet Survey Satellite (TESS) set to launch next year.  The two-year mission will focus on discovering new exoplanets by focusing on the brightest stars near our solar system. 

“It could be a useful tool because predicting stability would allow us to learn more about the system from the upper limits of mass to the eccentricities of these planets,” says Tamayo. “It could be a very useful tool in better understanding those systems.” 

�鶹��Ƶ Scarborough planetary science researcher Dan Tamayo wants to use algorithms to study solar system stability (photo by Ken Jones)

There are several methods of detecting exoplanets that provide information such as the size of the planet and its orbital period. But they may not provide the planet’s mass or how elliptical their orbit is, which are all factors that affect stability, notes Tamayo. 

“In the past we’ve been hamstrung in trying to figure out whether planetary systems are stable by methods that couldn’t handle the amount of data we were throwing at it,” he says. 

The method developed by Tamayo and his team is the result of a series of workshops at �鶹��Ƶ Scarborough covering how machine learning could help tackle specific scientific problems. 

“What’s encouraging is that our findings tell us that investing weeks of computation to train machine learning models is worth it because not only is this tool accurate, it also works much faster,” he adds.