A Next-Generation Exoplanet Atmospheric Retrieval Framework NEXOTRANS for Emission Spectroscopy: New Constraints and Atmospheric Characterization of WASP-69b Using JWST NIRCam and MIRI Observations

Abstract

Thermal emission spectra provide key insights into the atmospheric composition and especially the temperature structure of an exoplanet. With broader wavelength coverage, sensitivity and higher resolution, JWST has enabled robust constraints on these properties, including detections of photochemical products. This advances the need for retrieval frameworks capable of navigating complex parameter spaces for accurate data interpretation. In this work, we introduce the emission retrieval module of NEXOTRANS, which employs both one- and two-stream radiative transfer approximations and leverages Bayesian and machine learning techniques for retrievals. It also incorporates approximate disequilibrium chemistry models to infer photochemical species like SO2. We applied NEXOTRANS to the JWST NIRCam and MIRI emission observations of WASP-69b, covering the 2-12 microns range. The retrievals place robust constraints on the volume mixing ratios (VMR) of H2O, CO2, CO, CH4, and potential SO2. The best-fit model, i.e, free chemistry combined with non-uniform aerosol coverage, yields a log(VMR) = -3.78 (+0.15/-0.17) for H2O and -5.77 (+0.09/-0.10) for CO2 which has a sharp absorption at 4.3 micron. The second best-fit model, the hybrid equilibrium chemistry (utilizing equilibrium chemistry-grids) combined with non-uniform aerosol yields a C/O of 0.42 (+0.17/-0.13) and a metallicity of log[M/H] = 1.24 (+0.17/-0.14), corresponding to approximately 17.38 times the solar value. This hybrid chemistry retrieval also constrain SO2 with a log(VMR) = -4.85 (+0.28/-0.29), indicating possible absorption features in the 7-8 microns range. These results highlight NEXOTRANS's capability to significantly advance JWST emission spectra interpretation, offering broader insights into exoplanetary atmospheres.

Figures (12)

• Figure 1: A schematic representation of the retrieval framework implemented in the NEXOTRANS emission module is shown. This framework comprises two key components: the Forward Model, which includes both one-stream and two-stream radiative transfer approximations, and the Retrieval Framework. The Forward Model simulates the exoplanet's atmosphere to generate a model emission spectrum, while Bayesian inference and machine learning techniques are utilized for reliable parameter estimation.
• Figure 2: Validation of NEXOTRANS’s emission retrieval module against POSEIDON: (a) Best-fit retrieved spectrum and P-T profile using the one-stream approximation, and (b) using the two-stream approximation. JWST MIRI observations are shown with black error bars. Retrievals were performed with a model resolution of 15,000 and 1,000 live points in the nested sampler. For clarity, the best-fit spectra are shown binned to a resolution of 100, with the median, 1$\sigma$ and 2$\sigma$ confidence intervals. The one-stream approximation assumes a clear atmosphere, while the two-stream approximation includes uniform MgSiO$_3$ aerosol. Both retrievals are performed assuming free chemistry.
• Figure 3: Vertical mixing ratio profile model of MgSiO$_3$ aerosol as a function of pressure. The shaded gray region represents the opaque cloud deck, extending from the bottom to log(P$_{\mathrm{MgSiO_3,deck}}$). The slanted yellow line denotes the volume mixing ratio (VMR) profile of the mie scattering aerosol.
• Figure 4: Posterior distributions of free parameters retrieved with the one-stream radiative transfer implementation of NEXOTRANS, compared to those obtained with POSEIDON. The retrievals assume a clear atmosphere and free chemistry. The retrieved parameters from both algorithms show agreement within 1$\sigma$, indicating consistency between the two implementations.
• Figure 5: Posterior distributions of free parameters retrieved with the two-stream radiative transfer implementation of NEXOTRANS, compared to those obtained with POSEIDON. The retrievals assume an atmosphere uniformly covered by MgSiO$_3$ aerosols and adopt a free chemistry framework. The retrieved parameters from both algorithms show agreement within 1$\sigma$, indicating consistency between the two implementations.