An Open-source Bayesian Atmospheric Radiative Transfer (BART) Code. III. Initialization, Atmospheric Profile Generator, Post-processing Routines
Jasmina Blecic, Joseph Harrington, Patricio E. Cubillos, M. Oliver Bowman, P. Rojo, Madison Stemm, Ryan C. Challener, Michael D. Himes, Austin J. Foster, Ian Dobbs‐Dixon, Andrew S. D. Foster, Nathaniel B. Lust, Sarah D. Blumenthal, Dylan Bruce, Thomas J. Loredo
Abstract
Abstract This and companion papers by Harrington et al. and Cubillos et al. describe an open-source retrieval framework, Bayesian Atmospheric Radiative Transfer ( BART ), available to the community under the reproducible-research license via https://github.com/exosports/BART . BART is a radiative transfer code (transit; https://github.com/exosports/transit ; Rojo et al.), initialized by the Thermochemical Equilibrium Abundances (TEA; https://github.com/dzesmin/TEA ) code (Blecic et al.), and driven through the parameter phase space by a differential-evolution Markov Chain Monte Carlo (MC3; https://github.com/pcubillos/mc3 ) sampler (Cubillos et al.). In this paper we give a brief description of the framework and its modules that can be used separately for other scientific purposes; outline the retrieval analysis flow; present the initialization routines, describing in detail the atmospheric profile generator and the temperature and species parameterizations; and specify the post-processing routines and outputs, concentrating on the spectrum band integrator, the best-fit model selection, and the contribution functions. We also present an atmospheric analysis of WASP-43b secondary eclipse data obtained from space- and ground-based observations. We compare our results with the results from the literature and investigate how the inclusion of additional opacity sources influences the best-fit model.