Abstract The advent of JWST has marked a new era in exoplanetary atmospheric studies, offering higher-resolution data and greater precision across a broader spectral range than previous space-based telescopes. Accurate analysis of these data sets requires advanced retrieval frameworks capable of navigating complex parameter spaces. We present NEXOTRANS , an atmospheric retrieval framework that integrates Bayesian inference using UltraNest / PyMultiNest with four machine learning algorithms: Random Forest , Gradient Boosting , K-Nearest Neighbor , and Stacking Regressor . This hybrid approach enables a comparison between traditional Bayesian methods and computationally efficient machine learning techniques. Additionally, NEXOTRANS incorporates NEXOCHEM , a module for solving equilibrium chemistry. We applied NEXOTRANS to JWST observations of the Saturn-mass exoplanet WASP-39 b, spanning wavelengths from 0.6 to 12.0 μ m using NIRISS, NIRSpec PRISM, and MIRI. Four chemistry models—free, equilibrium, modified hybrid equilibrium, and modified equilibrium-offset chemistry—were explored to retrieve precise volume mixing ratios (VMRs) for H 2 O, CO 2 , CO, H 2 S, and SO 2 . Absorption features in both NIRSpec PRISM and MIRI data constrained SO 2 log VMRs to values between −6.25 and −5.73 for all models except equilibrium chemistry. High-altitude aerosols, including ZnS and MgSiO 3 , were inferred, with constraints on their VMRs, particle sizes, and terminator coverage fractions, providing insights into cloud composition. For the best-fit modified hybrid equilibrium model, we derived supersolar elemental abundances of O/H = 14 . 12 − 1 . 82 + 2 . 86 × solar, C/H = 21 . 37 − 3 . 18 + 4 . 93 × solar, and S/H = 5 . 37 − 0.65 + 0.79 × solar, along with a C/O ratio of 1.35 − 0.02 + 0.05 × solar. These results demonstrate NEXOTRANS ’s potential to enhance JWST data interpretation, advancing comparative exoplanetology efficiently.
Deka et al. (Mon,) studied this question.
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