Characterising extrasolar planets with deep learning
The discovery of extrasolar planets - i.e. planets orbiting other stars - has fundamentally transformed our understanding of planets, solar systems and our place in the galaxy. With over 3500 systems discovered to date, characterising these alien worlds is rapidly becoming a big data problem. Future surveys with ground and space based telescopes will simply provide too much data to be analysed in more classical ways. Here I will present how deep learning can be used to rapidly characterise the chemistry and prevalent weather patterns of these extrasolar planets and put our own solar system in the grander galactic context.
Dr. Ingo Waldmann is a senior research scientist at the University College London. He obtained his PhD in astrophysics in 2012 working on blind source-separation algorithms applied to observations of extrasolar planets with the Hubble and Spitzer space-telescopes. He has since specialised in the modelling of non-linear Bayesian inverse problems and deep learning applied to atmospheric physics of extrasolar planets and solar system objects. He is the data analysis lead of the European Space Agency ARIEL mission and the UK-led Twinkle space-mission.