Machine Learning Can Help Decode Alien Skies--Up to a Point - Eos

Future telescopes like the James Webb Space Telescope (JWST) and the Atmospheric Remote-sensing Infrared Exoplanet Large-survey (ARIEL) are designed to sample the chemistry of exoplanet atmospheres. Ten years from now, spectra of alien skies will be coming in by the hundreds, and the data will be of a higher quality than is currently possible. Astronomers agree that new analysis techniques, including machine learning algorithms, will be needed to keep up with the flow of data and have been testing options in advance. An upcoming study in Monthly Notices of the Royal Astronomical Society trialed one such algorithm against the current gold standard method for decoding exoplanet atmospheres to see whether the algorithm could tackle this future big-data problem. "We got really good agreement between [the answers from] our machine learning method and the traditional Bayesian method that most people are using," said Matthew Nixon.