NCCR PlanetS: Observational and computational characterization of exoplanet atmospheres

TL;DR

This review summarizes observational and theoretical advances in exoplanet atmosphere characterization, emphasizing modeling, data analysis, and instrumentation developments enabled by NCCR PlanetS.

Contribution

It provides a comprehensive overview of current methods, models, and observational strategies, integrating recent progress in theory and data analysis for exoplanet atmospheres.

Findings

Development of advanced atmospheric models from 1D to 3D

Implementation of atmospheric retrieval frameworks including machine learning

Utilization of instruments like HST, JWST, and ground-based spectrographs

Abstract

This chapter reviews the current state of observational and theoretical efforts in the characterization of exoplanet atmospheres, with a focus on developments enabled through the Swiss National Centre for Competence in Research (NCCR) PlanetS. It covers the essential physical and chemical processes that govern atmospheric dynamics, radiative transfer, chemistry, and cloud formation in exoplanets and brown dwarfs. The review discusses the modeling approaches used to simulate these processes, ranging from simplified 1D models to fully coupled 3D general circulation models. Atmospheric retrieval frameworks are presented as tools for inferring atmospheric properties from observational data, highlighting both classical Bayesian techniques and emerging machine learning methods. Observational strategies using instruments like HST, JWST, and ground-based high-resolution spectrographs are also examined. Special emphasis is placed on the interplay between theory and observation, and how developments in modeling, data analysis, and instrumentation collectively advance our understanding of planetary atmospheres beyond the Solar System.