ExoLyn: a golden mean approach to multi-species cloud modelling in atmospheric retrieval
Helong Huang, Chris W. Ormel, Michiel Min

TL;DR
ExoLyn is an efficient 1D cloud model for exoplanet atmospheres that accurately predicts multi-species cloud structures and compositions, aiding spectroscopic data interpretation.
Contribution
We developed ExoLyn, a computationally efficient, physically consistent 1D cloud model for atmospheric retrievals that captures multi-species cloud layering and composition.
Findings
Clouds in hot Jupiters are layered with magnesium-silicates over iron.
Cloud composition can be constrained from spectral features at 8-10 micrometers.
Cloud structure depends on planetary elemental abundance, with SiO2-rich and Fe-rich clouds forming on different planets.
Abstract
Context. Clouds are ubiquitous in exoplanets' atmospheres and play an important role in setting the opacity and chemical inventory of the atmosphere. Understanding clouds is a critical step in interpreting exoplanets' spectroscopic data. Aims. The aim is to model the multi-species nature of clouds in atmospheric retrieval studies. To this end, we develop ExoLyn - a 1D cloud model that balances physical consistency with computational efficiency. Methods. ExoLyn solves the transport equation of cloud particles and vapor under cloud condensation rates that are self-consistently calculated from thermodynamics. ExoLyn is a standalone, open source package capable to be combined with \texttt{optool} to calculate solid opacities and with \texttt{petitRADTRANS} to generate transmission or emission spectra. Results. With ExoLyn we find that the compositional structure of clouds in hot Jupiter…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsScience and Climate Studies
