SIMBAD references

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-69 b, covering the 2-12 μm 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 nonuniform aerosol coverage, yields a log(VMR) = $-3.7{8}_{-0.17}^{+0.15}$ for H₂O and $-5.7{7}_{-0.10}^{+0.09}$ for CO₂, which has a sharp absorption at 4.3 μm. The second best-fit model, the hybrid equilibrium chemistry (utilizing equilibrium chemistry-grids) combined with nonuniform aerosol, yields a C/O of $0.4{2}_{-0.13}^{+0.17}$ and a metallicity of log[M/H] = $1.2{4}_{-0.14}^{+0.17}$, corresponding to approximately 17.38 times the solar value. This hybrid chemistry retrieval also constrains SO₂ with a log(VMR) = $-4.8{5}_{-0.29}^{+0.28}$, indicating possible absorption features in the 7-8 μm range. These results highlight NEXOTRANS's capability to significantly advance JWST emission spectra interpretation, offering broader insights into exoplanetary atmospheres.