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An Open-source Bayesian Atmospheric Radiative Transfer (BART) Code: II. The Transit Radiative-transfer Module and Retrieval of HAT-P-11b

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arxiv 2104.12524 v2 pith:NLVWIBLE submitted 2021-04-26 astro-ph.EP astro-ph.IM

classification astro-ph.EPastro-ph.IM
keywords barttransitcoderadiative-transferatmosphericbayesianabundancesagree
verification ladder T0 review T1 audit T2 compute T3 formal
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This and companion papers by Harrington et al. and Blecic et al. present the Bayesian Atmospheric Radiative Transfer (BART) code, an open-source, open-development package to characterize extrasolar-planet atmospheres. BART combines a thermochemical equilibrium abundances (TEA), a radiative-transfer (Transit), and a Bayesian statistical (MC3) module to constrain atmospheric temperatures and molecular abundances for given spectroscopic observations. Here, we describe the Transit radiative-transfer package, an efficient line-by-line radiative-transfer C code for one-dimensional atmospheres, developed by P. Rojo and further modified by the UCF exoplanet group. This code produces transmission and hemisphere-integrated emission spectra. Transit handles line-by-line opacities from HITRAN, Partridge \& Schwenke ({\water}), Schwenke (TiO), and Plez (VO); and collision-induced absorption from Borysow, HITRAN, and ExoMol. Transit emission-spectra models agree with models from C. Morley (priv. comm.) within a few percent. We applied BART to the {\Spitzer} and {\Hubble} transit observations of the Neptune-sized planet HAT-P-11b. Our results generally agree with those from previous studies, constraining the {\water} abundance and finding an atmosphere enhanced in heavy elements. Different conclusions start to emerge when we make different assumptions from other studies. The BART source code and documentation are available at https://github.com/exosports/BART.

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  1. Supervised Machine Learning Methods with Uncertainty Quantification for Exoplanet Atmospheric Retrievals from Transmission Spectroscopy

    astro-ph.EP 2025-08 conditional novelty 5.0 of 10

    On a synthetic JWST/Ariel-style spectral database, XGBoost and SVM with per-spectrum normalization and log-abundance targets outperform random forests and other classical regressors for exoplanet atmospheric retrievals.

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