Machines Sort Through Thousands of Stars to Learn About their Basic Properties

Astronomers are turning to machines when it comes to learning more about the stars. Scientists are using computers to sort through thousands of stars in our galaxy and learn their sizes, compositions and other basic traits.

This latest research is just part of the growing field of machine learning. Computers learn from large data sets, finding patterns that humans might not otherwise see. Machine learning, in particular, in everything from media-streaming services that predict what you might want to watch, to the post office, where computers automatically read handwritten addresses and direct mail to the correct zip codes.

Now astronomers have turned to this same technology to help them identify basic properties of stars based on sky survey images.

"It's like video-streaming services not only predicting what you would like to watch in the future, but also your current age, based on your viewing preferences," said Adam Miller, lead author of a new report on the findings, in a news release. "We are predicting fundamental properties of the stars."

This latest effort is the first to predict specific traits of stars, such as size and metal content, using images of the stars taken over time. These traits are essential to learning about when a star was born, and how it's changed since then.

"This is an exciting time to be applying advanced algorithms to astronomy," said Miller. "Machine learning allows us to mine for rare and obscure gems within the deep data sets that astronomers are only now beginning to acquire."

The findings are published in The Astrophysical Journal.

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