Cloud and Haze Parameterization in Atmospheric Retrievals: Reference HDF5 cross-sections for TauREx3

This repository contains the supporting cross-section data for the research article Changeat et al. 2025: Cloud and Haze Parameterization in Atmospheric Retrievals.

The provided cross-sections are intended to be used with TauREx 3.0+ (TauREx3 Github Link)

Abstract: 

Context. Before JWST, telescope observations were not sensitive enough to constrain the nature of clouds in exo-atmospheres. Recent observations, however, have inferred cloud signatures as well as haze-enhanced scattering slopes motivating the need for modern inversion techniques and a deeper understanding of the JWST information content.

Aims. We aim to investigate the information content of JWST exoplanet spectra. We particularly focus on designing an inversion technique able to handle a wide range of cloud and hazes.

Methods. We build a flexible aerosol parameterization within the TauREx framework, enabling us to conduct atmospheric retrievals of planetary atmospheres. The method is evaluated on available Cassini occultations of Titan. We then use the model to interpret the recent JWST data for the prototypical hot Jupiters HAT-P-18 b, WASP-39 b, WASP-96 b, and WASP-107 b. In parallel, we perform complementary simulations on controlled scenarios to further understand the information content of JWST data and provide parameterization guidelines.

Results. Our results use free and kinetic chemistry retrievals to extract the main atmospheric properties of key JWST exoplanets, including their main molecular abundances (and elemental ratios), thermal structures, and aerosol properties. In our investigations, we show the need for a wide wavelength coverage to robustly characterize clouds and hazes—which is necessary to mitigate biases arising from our lack of priors on their composition—and break degeneracies with atmospheric chemical composition. With JWST, the characterization of clouds and hazes might be difficult due to the lack of simultaneous wavelength coverage from visible to mid-infrared by a single instruments and the likely presence of temporal variability between visits (from e.g., observing conditions, instrument systematics, stellar host variability, or planetary weather).

ExoMol (R = 50k):

Dataset compiled by K. Chubb for Changeat et al. 2025, following the procedure descibed in Chubb et al. 2021, A&A 646, A21 (DOI Link). Please also cite the original sources of these datasets:

• H2O: Polyansky et al. (2018)
• CO: Li et al. (2015)
• CO2: Yurchenko et al. (2020)
• CH4: Yurchenko et al. (2017)
• HCN: Barber et al. (2013); Chubb et al. (2021)
• NH3: Al Derzi et al. (2015); Coles et al. (2019)
• SO2: Underwood et al. (2016)
• H2S: Azzam et al. (2016); Chubb et al. (2021)
• Na: Allard et al. (2019)
• K: Allard et al. (2016)

( Raw line-lists and Cross-sections at R = 15k are also available directly at (ExoMol Website). See also references in: Ariel-data Github )

DACE (R = 200k):

Dataset compiled by Q. Changeat as part of Changeat et al. 2021. The sources are obtained from the DACE project (DACE Website) and reformatted for TauREx3 use at R = 200k.

Collision Induced Absorptions:

The CIA files are obtained directly from the HITRAN website (Link).

Aerosols:

The refractive indices files were formated for TauREx-PyMieScatt by Q. Changeat. Please see below for the original sources:

• CH4: Martonchik & Orton (1994)
• KCl: Querry et al. (1987)
• MgSiO3: Scott & Duley (1996)
• Na2S: Khachai et al. (2009)
• SiO: Wetzel et al. (2013)
• SiO2: Palik (1991); Kitzmann & Heng (2018)
• Tholin: Khare et al. (1984); Rannou et al. (2010)
• ZnS: Querry et al. (1987)

( PyMieScatt is also compatible with VIRGA files compiled in Link )