2025ApJ...989...50D

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 μm 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 H₂O, CO₂, CO, H₂S, and SO₂. Absorption features in both NIRSpec PRISM and MIRI data constrained SO₂ log VMRs to values between -6.25 and -5.73 for all models except equilibrium chemistry. High-altitude aerosols, including ZnS and MgSiO₃, 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 $\,\rm{O/H}\,=14.{12}_{-1.82}^{+2.86}\times $solar, $\,\rm{C/H}\,=21.{37}_{-3.18}^{+4.93}\times $solar, and $\,\rm{S/H}\,=5.{37}_{-0.65}^{+0.79}\times $solar, along with a C/O ratio of ${1.35}_{-0.02}^{+0.05}\times $ solar. These results demonstrate NEXOTRANS's potential to enhance JWST data interpretation, advancing comparative exoplanetology efficiently.