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Astronomers know of about 4,000 planets orbiting stars outside our solar system. Now they know of two more, thanks to an undergraduate college student using artificial intelligence. Here's NPR's Joe Palca.
JOE PALCA, BYLINE: Anne Dattilo is a senior at The University of Texas at Austin. Last year, an astronomer talked to her class about his research using a NASA satellite called Kepler to hunt for planets orbiting distant stars.
ANNE DATTILO: And at the very end, he was like, I'm taking undergrads, if any of you want to do research on the subject, finding planets, and I decided that's what I wanted to do. So I emailed him, and a year and a half later, here I am.
PALCA: She led a team that discovered two Earth-sized planets orbiting stars more than 1,200 light-years from Earth. To find the planets, Dattilo used an artificial intelligence approach called machine learning to comb through a Kepler data set called K2; K2 contains measurements of the light coming from tens of thousands of stars. Dattilo says when a star is what she calls boring, the light coming from it is constant.
DATTILO: But if you can imagine something passing in front of that star, the light we receive would dim. And so if you see that periodically, that would be a signal that a planet is in front of that.
PALCA: The artificial intelligence program looks for these fluctuations in a star's light that might be associated with a planet passing in front. Now, you don't have to be a NASA scientist to use data from a NASA satellite.
JESSIE CHRISTIANSEN: NASA makes all of the data publicly available. You just have to think of a new idea to do with the data that no one's done before.
PALCA: Jessie Christiansen is a research scientist at the NASA Exoplanet Science Institute at Caltech in Pasadena.
CHRISTIANSEN: This is the first time someone's gone through and done a machine learning process on the K2 data to come up with a uniform list of planet candidates.
PALCA: And that will be valuable beyond just getting a good grade on an undergraduate class?
PALCA: In fact, Michelle Ntampaka at the Harvard-Smithsonian Institute for Astrophysics in Cambridge says she's seen something remarkable happen in the last five years or so.
MICHELLE NTAMPAKA: And that is that there has been a dramatic increase in the amount of machine learning research that's happening for astronomy applications.
PALCA: That's because newer telescopes don't so much collect images of stars and galaxies as digital data about these celestial objects.
NTAMPAKA: We're just going to see unprecedented data volumes, and we're going to have to come up with new ways to deal with that.
PALCA: Ntampaka says the next generation of astronomers will have to be comfortable working with artificial intelligence to make sense of all this data. So writing a machine learning program as an undergrad is good preparation for Anne Dattilo as she heads off to get her graduate degree in astronomy. Joe Palca, NPR News.
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