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NASA is using machine learning to predict the characteristics of stars

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With so many stars in our galaxy to discover and catalog, NASA is adopting new machine learning techniques to speed up the process. Even now, telescopes around the world are capturing countless images of the night sky, and new projects such as the Large Synoptic Survey Telescope (LSST) will only increase the amount of data available at NASA's fingertips. To give its analysis a helping hand, the agency has been using some of its prior research and recordings to essentially "teach" computers how to spot patterns in new star data.

NASA's Jet Propulsion Laboratory started with 9,000 stars and used their individual wavelengths to identify their size, temperature and other basic properties. The data was then cross-referenced with light curve graphs, which measure the brightness of the stars, and fed into NASA's machines. The combination of the two, combined with some custom algorithms, means that NASA's computers should be able to make new predictions based on light curves alone. Of course, machine learning isn't new to NASA, but this latest approach is a little different because it can identify specific star characteristics. Once the LSST is fully operational in 2023, it could reduce the number of astronomers pulling all-nighters.

[Image Credit: Image credit: NASA/JPL-Caltech, Flickr]

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