In this case, software learned differences between planets and other objects by analysing thousands of data points, achieving 96% accuracy, Nasa said at a news conference. The software’s artificial “neural network” combed through data about 670 stars, which led to the discovery of planets Kepler 80g and Kepler 90i. “As the application of neutral networks to Kepler data matures, who knows what might be discovered,” said Jessie Dotson, a Nasa project scientist for the Kepler space telescope. Machine learning had not been applied to data acquired by the Kepler telescope until Shallue came up with the idea, he said. “In my spare time, I started Googling for ‘finding exoplanets with large data sets’ and found out about the Kepler mission and the huge data set available,” he said.

Source: Mint December 16, 2017 10:07 UTC