Machine learning – a powerful tool used for a variety of tasks in modern life, from Google to Netflix – can help scientists determine whether planetary systems are stable or not, a new study has found. Machine learning is used in fraud detection and sorting spam in Google and in making movie recommendations on Netflix. Now, a team of researchers from the University of Toronto Scarborough in Canada 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,” said Dan Tamayo, postdoctoral fellow in the Centre for Planetary Science at U of T Scarborough. Also Read - Facebook for Android will soon get dark mode and coronavirus tracking featureAlso Read - Scientists develop soft contact lens that can zoom with a blink
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 said. It is 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, noted Tamayo. “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. ALSO READ: Researcher to develop a new machine learning algorithm to make your smartphone smarter
It may also 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,” said Tamayo. The research was published in the Astrophysical Journal Letters. ALSO READ: Machine learning can help identify suicidal behaviour: Study