Information content of JWST spectra of WASP-39b
Anna Lueber, Aline Novais, Chloe Fisher, Kevin Heng

TL;DR
This study assesses the information content of JWST spectra of exoplanet WASP-39b, revealing how spectral complexity, cloud models, and instrument modes influence atmospheric retrievals and chemical detections.
Contribution
It introduces a comprehensive analysis of JWST spectra using Bayesian methods and machine learning, highlighting mode-dependent retrieval complexities and model ambiguities.
Findings
Non-isothermal temperature-pressure profiles are generally required.
Chemical species detection depends on instrument mode and cloud assumptions.
Bayesian model comparison often cannot definitively favor specific models.
Abstract
WASP-39b was observed using several different JWST instrument modes and the spectra were published in a series of papers by the ERS team. The current study examines the information content of these spectra measured using the different instrument modes, focusing on the complexity of the temperature-pressure profiles and number of chemical species warranted by the data. We examine if H2O, CO, CO2, K, H2S, CH4, and SO2 are detected in each of the instrument modes. Two Bayesian inference methods are used to perform atmospheric retrievals: standard nested sampling and supervised machine learning of the random forest (trained on a model grid). For nested sampling, Bayesian model comparison is used as a guide to identify the set of models with the required complexity to explain the data. Generally, non-isothermal transit chords are needed to fit the transmission spectra of WASP-39b, although…
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
TopicsGeological Studies and Exploration · Methane Hydrates and Related Phenomena
