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STEVE INSKEEP, HOST:
Astronomers2 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, BYLINE3: Anne Dattilo is a senior at The University of Texas at Austin. Last year, an astronomer1 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 decided4 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 fluctuations5 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?
CHRISTIANSEN: Absolutely.
PALCA: In fact, Michelle Ntampaka at the Harvard-Smithsonian Institute for Astrophysics in Cambridge says she's seen something remarkable6 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 galaxies7 as digital data about these celestial8 objects.
NTAMPAKA: We're just going to see unprecedented9 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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1 astronomer | |
n.天文学家 | |
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2 astronomers | |
n.天文学者,天文学家( astronomer的名词复数 ) | |
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3 byline | |
n.署名;v.署名 | |
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4 decided | |
adj.决定了的,坚决的;明显的,明确的 | |
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5 fluctuations | |
波动,涨落,起伏( fluctuation的名词复数 ) | |
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6 remarkable | |
adj.显著的,异常的,非凡的,值得注意的 | |
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7 galaxies | |
星系( galaxy的名词复数 ); 银河系; 一群(杰出或著名的人物) | |
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8 celestial | |
adj.天体的;天上的 | |
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9 unprecedented | |
adj.无前例的,新奇的 | |
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