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NASA Astronomers Seek ‘Machine’ Help to Study Stars

Scientists of NASA sign up for “machine learning,” seeking help to understand the properties of a large number of stars in our galaxy and learn their basic properties such as size and composition.

The research is part of the growing field of machine learning, in which computers learn from large data sets, finding patterns that humans might not otherwise see.

Machine learning is in everything from media-streaming services that predict what you want to watch, to the post office, where computers automatically read handwritten addresses and direct mail to the correct zip codes.

According to a statement issued by NASA, “With ‘machine learning’, computer algorithms can quickly flip through available stacks of images, identifying patterns that reveal a star’s properties.”

The technique has the potential to gather information on billions of stars in a relatively short time and with less expense.

“It is 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 lead author Adam Miller of NASA’s Jet Propulsion Laboratory in Pasadena, California.

But before the machines can learn, they first need a “training period.” Miller and his colleagues have started with 9,000 stars as their training set.

They obtained spectra for these stars which revealed several of their basic properties: sizes, temperatures and the amount of heavy elements, such as iron.

“We can discover and classify new types of stars without the need for spectra, which are expensive and time-consuming to obtain. With more information about the different kinds of stars in our Milky Way galaxy, we can better map the galaxy’s structure and history,” said Miller.

The varying brightness of the stars had also been recorded by the Sloan Digital Sky Survey, producing plots called light curves.

By feeding the computer both sets of data, it could then make associations between the star properties and the light curves.

Once the training phase was over, the computer was able to make predictions on its own about other stars by only analysing light-curves.

The report was published in the Astrophysical Journal.


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