by Angela Guess
A recent press release out of the University of Toronto Scarborough reports, “Machine learning is 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. Now a team of researchers from the University of Toronto Scarborough have developed a novel approach in using it 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,’ says Dan Tamayo, lead author of the research and a postdoctoral fellow in the Centre for Planetary Science at U of T Scarborough.”
The release goes on, “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 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,’ he says. 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.”
Read more at EurekAlert.
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