University of Warwick, UK | 2020 Aug 25
New machine learning algorithm designed by astronomers and computer scientists from University of Warwick confirms new exoplanets in telescope data. Sky surveys find thousands of planet candidates, and astronomers have to separate the true planets from fake ones. Algorithm was trained to distinguish between signs of real planets and false positives. New technique is faster than previous techniques, can be automated, and improved with further training.
Fifty potential planets have had their existence confirmed by a new machine learning algorithm developed by University of Warwick scientists.
For the first time, astronomers have used a process based on machine learning, a form of artificial intelligence, to analyse a sample of potential planets and determine which ones are real and which are ‘fakes,’ or false positives, calculating the probability of each candidate to be a true planet.
Their results are reported in a new study ... where they also perform the first large scale comparison of such planet validation techniques. Their conclusions make the case for using multiple validation techniques, including their machine learning algorithm, when statistically confirming future exoplanet discoveries.
Many exoplanet surveys search through huge amounts of data from telescopes for the signs of planets passing between the telescope and their star, known as transiting. This results in a telltale dip in light from the star that the telescope detects, but it could also be caused by a binary star system, interference from an object in the background, or even slight errors in the camera. These false positives can be sifted out in a planetary validation process. ...
Exoplanet Validation with Machine Learning:
50 New Validated Kepler Planets ~ David J. Armstrong, Jevgenij Gamper, Theodoros Damoulas
- Monthly Notices of the RAS (online 20 Aug 2020) DOI: 10.1093/mnras/staa2498
- arXiv.org > astro-ph > arXiv:2008.10516 > 24 Aug 2020