Description
Our rapidly increasing observational capabilities for exoplanet characteristics increasingly challenge current modeling tools. Simplifications we make to our atmospheric modeling cause biases in the derived parameters. Especially the unprecedented accuracy of JWST observations show us that often taking into account inhomogeneous, 3D and cloudy atmospheres is no longer a luxury, but a requirement for proper interpretations. Here we present the current state of the generic atmospheric modeling and retrieval code ARCiS. The code philosophy is to combine fast and flexible physical and chemical modules on 3D structures, cloud formation and chemistry to provide accurate predictions of observables. These can be coupled through observations using Bayesian statistics or machine learning methods. In addition, the models can be used to inform future telescope and instrument design by coupling self-consistent physical/chemical models to accurate simulation of observables.
In this contribution I will showcase some of the newest features of the code and provide some applications to JWST observations and HWO science case development.
| Talk category | NOVA Network 2 |
|---|---|
| Preference for a talk or poster | Poster |
