Project title: Modelling Exoplanet Atmospheres with Machine Learning
PI: Suzanne Aigrain
Modelling the atmospheres of exoplanets is a computationally demanding task, and becoming more demanding in the JWST (James Webb Space Telescope, launched in December 2021) era. The project proposes a unique method of using convolutional neural networks to emulate the calculation of molecular opacity. Combining this new tool with the GPU programming commonly used in machine learning will significantly improve our ability to quickly and accurately interpret the atmospheres of these alien worlds.
