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Machine-learning algorithms have been used to uncover two previously unseen exoplanets in the archive of data amassed by NASA’s retired Kepler space telescope.
Launched in 2009, Kepler was sent to survey the dark reaches of the Milky Way. Its job was to hunt for alien worlds by scrutinizing the light emitted by faraway stars. Armed with a photometer, Kepler looked out for the characteristic dip in brightness as a planet crossed in front of its parent star. It was retired last October.
Spacecraft - Scientists - Planets - Team - Astronomers
The spacecraft has helped scientists discover more than 2,000 distant planets, and there are still many to be found yet. A team of astronomers and engineers, led by the University of Texas, Austin, and Google, worked together to sniff out potential candidate exoplanets using a convolutional neural network. This software was trained using a dataset of stars and planets observed by Kepler, so that when looking at readings of other stars' brightness, it could predict the presence of an alien planet for each of them.
Two previously unknown worlds were found by the neural network, as it chugged through Kepler data looking for signs of orbiting planets, and their existence has been confirmed using telescopes in Arizona and Hawaii in the US. Christened K2-293b and K2-294b, both bodies are close to one another, and are located 1,300 light years and 1,230 light years away in the constellation Aquarius, respectively, and they’re both slightly bigger and hotter than Earth. The full findings will be published in the next edition of The Astrophysical Journal (here's a free version of the writeup).
Data - Pair - Planets - Kepler - K2
The data revealing the pair of planets came from Kepler’s K2 phase of its mission. In 2013, two of the spacecraft’s four reaction wheels broke down and it could no longer stay pointed at a particular star, so NASA reconfigured it so that...
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