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Home   /   Thesis   /   Disequilibrium chemistry of exoplanet atmospheres in the JWST era. An opportunity for Machine Learning.

Disequilibrium chemistry of exoplanet atmospheres in the JWST era. An opportunity for Machine Learning.

Astrophysics Corpuscular physics and outer space


In little more than one year of scientific operations, JWST has already revolutionized our understanding of exoplanets and their atmospheres. The ARIEL space mission, to be launched in 2029, will contribute in due course to this revolution. A main finding that has been enabled by the exquisite quality of the JWST data is that exoplanet atmospheres are in chemical disequilibrium. A full treatment of disequilibrium is both complex and computationally expensive. In a first step, our project will numerically investigate the extent of chemical disequilibrium in the atmospheres of JWST targets. We will use towards that end an in-house photochemical model. In a second step, our project will explore Machine Learning (ML) techniques to emulate the outputs of the full photochemical model at a reduced computational cost. The performance of the ML-based emulator will be analyzed with the ultimately goal of its integration into atmospheric retrieval models. The proposed project combines the sophisticated physics and chemistry of exoplanet atmospheres with developments in new numerical techniques.


Institut de recherche sur les lois fondamentales de l’univers
Direction d’Astrophysique
Laboratoire de dynamique des étoiles des (Exo) planètes et de leur environnement
Université de Paris
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