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Astronomers present 60 new 'hot Jupiter' exoplanet candidates

"It's amazing how the latest techniques in machine learning, compounded with high-performance computing, are allowing us to mine classic data sets for extraordinary discoveries," said researcher Greg Laughlin.

By Brooks Hays
Astronomers present 60 new 'hot Jupiter' exoplanet candidates
A diagram shows the spectral variability of light reflecting off the surface of a hot Jupiter. Photo Courtesy Millholland/Yale University

July 6 (UPI) -- Researchers have identified 60 new "hot Jupiter" exoplanet candidates.

Hot Jupiters are a unique class of gas giants found circling only 1 percent of sun-like stars. They're found following intimate orbits around their host stars. Their short paths around suns account for their extremely hot temperatures.

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Sarah Millholland, a Ph.D. student at Yale University, with the assistance of astronomy professor Greg Laughlin, adapted an algorithm used for the analysis of big data to identify hot Jupiter candidates among astronomical data recorded by NASA's Kepler mission.

Most exoplanets are found by identifying spectral variations caused by transits, the movement of an alien world across the face of its host star. Millholland and Laughlin pinpointed hot Jupiter exoplanets by spotting spectral variations produced by the reflection of starlight off the exoplanet's surface.

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Researchers trained a machine learning algorithm to recognize the amplitude variations reflected by gas giants with intimate orbits.

"Sarah's work has given us what amounts to a 'class portrait' of extrasolar planets at their most alien," Laughlin said in a news release. "It's amazing how the latest techniques in machine learning, compounded with high-performance computing, are allowing us to mine classic data sets for extraordinary discoveries."

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Laughlin and Millholland published their latest work in the Astronomical Journal this week.

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The spectral variations caused by light reflecting off the surface of a passing hot Jupiter are difficult to distinguish from random variations in instrument measurements and a star's spectral output. But researchers used machine learning to generate random datasets of spectral variations, allowing the algorithm to learn the differences between different types of minuscule variations.

"I've been told by members of the Kepler science team that a search for reflected star-shine was part of the early renditions of the Kepler pipeline," Millholland said. "They called it the Reflected Light Search, or RLS module. In this sense, we're simply addressing one of the original intentions for the Kepler data."

The research will continue to offer astronomers new opportunities for exploration, as the reflected light data contains information about each planet's atmosphere and climate.

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