Collaborative Research: Catch the waves - a machine learning approach to map brown dwarf and imaged exoplanet atmospheres in 3D

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Atmospheres and their clouds are crucial for how exoplanet and brown dwarfs cool down over time. Atmospheres and clouds are 3D. The typical techniques used to model them though, require a lot of computer time. To understand their properties they generally need to be simplified to 1D or 2D structures. However, knowing the 3D structure of clouds is important to understand how they affect the atmosphere. A team led by the University of Central Florida and the University of California-Santa Cruz will develop a new method that reduces the computer time needed to model clouds and atmospheres. This technique will allow a characterization of brown dwarf and imaged exoplanet atmospheres in 3D. This technique will help the study of how clouds change with important atmospheric properties (age, gravity, temperature and metallicity). It will also help test techniques that predict the weather in these atmospheres. This work will thus improve understanding of atmospheres. This work will support a graduate and an undergraduate student, and will form the basis for their thesis. Hands-on activities and videos will be created to familiarize K-12 students with atmospheres and programming.The goal of this proposal is to enable the 3D characterization of brown dwarf and imaged exoplanet atmospheres. In order to do this a surrogate radiative transfer code will be created that will use the power of neural networks to model spectra of atmospheres in a fraction of the time current codes need. The surrogate radiative transfer code will be used to create a 3D mapping code that fits time resolved observations in a Bayesian framework. The code will be cross-validated on output from an independent, state-of-the-art General Circulation Model. Ground-based telescopes already give excellent data that enable the 3D mapping of these atmospheres and in the next decades the number of appropriate data will increase considerably. The code will be applied on existing observations to create the first 3D maps of brown dwarf atmospheres. The proposed work will create tools that will uniquely enable comparative climatology in the next decades. The surrogate radiative transfer code and the 3D mapping code will be provided open source to the community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.