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Computer Science News Read more from Computer Science News

Exoplanet Validation with Machine Learning: 50 new validated Kepler planets

Dr Theo Damoulas (Department of Computer Science) along with Dr David Armstrong (Department of Physics) and Jevgenij Gamper (Department of Mathematics) have developed probabilistic machine learning algorithms that can separate out real planets from fake ones in the large samples of thousands of candidates found by telescope missions such as NASA’s Kepler and TESS. The results of which have led to fifty new confirmed planets, the first to be not only ranked but also probabilistically validated by machine learning.

The paper "Exoplanet Validation with Machine Learning: 50 new validated Kepler planets" has been accepted to the Monthly Notice of the Royal Astronomical Society, DOI: 10.1093/mnras/staa2498

Thu 10 Sep 2020, 13:30 | Tags: Research Artificial Intelligence

Statistics News and Events Read more from Statistics News and Events

Physics Department News Read more from Physics Department News

News @ Warwick Chemistry Read more from News @ Warwick Chemistry

Life Sciences News Read more from Life Sciences News

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News from Medical School Read more from Latest News

Psychology Read more from Psychology News