Technology used in Netflix and Google can Help Scientists to Determine whether Planetary Systems are Stable or not

Machine learning offers a powerful way to tackle a problem in astrophysics, and that's predicting whether planetary systems are stable

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Technology Giant Google. Pixabay

Toronto, December 2, 2016: Machine learning — a powerful tool used for a variety of tasks in modern life, from fraud detection and sorting spam in Google, to making movie recommendations on Netflix — can help scientists determine whether planetary systems are stable or not, a study says.

“Machine learning offers a powerful way to tackle a problem in astrophysics, and that’s predicting whether planetary systems are stable,” said study lead author Dan Tamayo from the University of Toronto Scarborough in Canada.

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Machine learning is a form of artificial intelligence that gives computers the ability to learn without having to be constantly programmed for a specific task. The benefit is that it can teach computers to learn and change when exposed to new data, not to mention it’s also very efficient.

The researchers found that the same class of algorithms used by Google and Netflix can also tell us if distant planetary systems are stable or not.

The method developed by Tamayo and his team is 1,000 times faster than traditional methods in predicting stability.

“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,” Tamayo said.

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It’s important to know whether planetary systems are stable or not because it can tell us a great deal about how these systems formed. It can also offer valuable new information about exoplanets that is not offered by current methods of observation.

There are several current 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, Tamayo noted.

The method developed by Tamayo and his team was published online in the Astrophysical Journal Letters.

“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 added.

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It may also come in handy when analysing 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,” Tamayo said. (IANS)

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