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Register a new userAn open-source Bayesian atmospheric radiative transfer (BART) code: III. Initialization, atmospheric profile generator, post-processing routines, and application to exoplanet WASP-43b
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Jasmina Blecic Mrs.
Publication date
2021
fields
Physics
and research's language is
English
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This and companion papers by Harrington et al. 2021, submitted and Cubillos et al. 2021, submitted 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 2009, 2009ASPC..420..321R), initialized by the Thermochemical Equilibrium Abundances (TEA, https://github.com/dzesmin/TEA , Blecic et al. 2016, arXiv:1505.06392) code, and driven through the parameter phase space by a differential-evolution Markov-chain Monte Carlo (MC3, https://github.com/pcubillos/mc3 , Cubillos et al. 2017, arXiv:1610.01336) sampler. 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 influence the best-fit model.
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We present the open-source Bayesian Atmospheric Radiative Transfer (BART) retrieval package, which produces estimates and uncertainties for an atmospheres thermal profile and chemical abundances from observations. Several BART components are also stand-alone packages, including the parallel Multi-Core Markov chain Monte Carlo (MC3), which implements several Bayesian samplers; a line-by-line radiative-transfer model, transit; a code that calculates Thermochemical Equilibrium Abundances, TEA; and a test suite for verifying radiative-transfer and retrieval codes, BARTTest. The codes are in Python and C. BART and TEA are under a Reproducible Research (RR) license, which requires reviewed-paper authors to publish a compendium of all inputs, codes, and outputs supporting the papers scientific claims. BART and TEA produce the compendiums content. Otherwise, these codes are under permissive open-source terms, as are MC3 and BARTTest, for any purpose. This paper presents an overview of the code, BARTTest, and an application to eclipse data for exoplanet HD 189733 b. Appendices address RR methodology for accelerating science, a reporting checklist for retrieval papers, the spectral resolution required for synthetic tests, and a derivation of the effective sample size required to estimate any Bayesian posterior distribution to a given precision, which determines how many iterations to run. Paper II, by Cubillos et al., presents the underlying radiative-transfer scheme and an application to transit data for exoplanet HAT-P-11b. Paper III, by Blecic et al., discusses the initialization and post-processing routines, with an application to eclipse data for exoplanet WASP-43b. We invite the community to use and improve BART and its components at http://GitHub.com/ExOSPORTS/BART/.
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.
Over the past decade, the study of extrasolar planets has evolved rapidly from plain detection and identification to comprehensive categorization and characterization of exoplanet systems and their atmospheres. Atmospheric retrieval, the inverse modeling technique used to determine an exoplanetary atmospheres temperature structure and composition from an observed spectrum, is both time-consuming and compute-intensive, requiring complex algorithms that compare thousands to millions of atmospheric models to the observational data to find the most probable values and associated uncertainties for each model parameter. For rocky, terrestrial planets, the retrieved atmospheric composition can give insight into the surface fluxes of gaseous species necessary to maintain the stability of that atmosphere, which may in turn provide insight into the geological and/or biological processes active on the planet. These atmospheres contain many molecules, some of them biosignatures, spectral fingerprints indicative of biological activity, which will become observable with the next generation of telescopes. Runtimes of traditional retrieval models scale with the number of model parameters, so as more molecular species are considered, runtimes can become prohibitively long. Recent advances in machine learning (ML) and computer vision offer new ways to reduce the time to perform a retrieval by orders of magnitude, given a sufficient data set to train with. Here we present an ML-based retrieval framework called Intelligent exoplaNet Atmospheric RetrievAl (INARA) that consists of a Bayesian deep learning model for retrieval and a data set of 3,000,000 synthetic rocky exoplanetary spectra generated using the NASA Planetary Spectrum Generator. Our work represents the first ML retrieval model for rocky, terrestrial exoplanets and the first synthetic data set of terrestrial spectra generated at this scale.
HYPERION is a new three-dimensional dust continuum Monte-Carlo radiative transfer code that is designed to be as generic as possible, allowing radiative transfer to be computed through a variety of three-dimensional grids. The main part of the code is problem-independent, and only requires an arbitrary three-dimensional density structure, dust properties, the position and properties of the illuminating sources, and parameters controlling the running and output of the code. HYPERION is parallelized, and is shown to scale well to thousands of processes. Two common benchmark models for protoplanetary disks were computed, and the results are found to be in excellent agreement with those from other codes. Finally, to demonstrate the capabilities of the code, dust temperatures, SEDs, and synthetic multi-wavelength images were computed for a dynamical simulation of a low-mass star formation region. HYPERION is being actively developed to include new features, and is publicly available (http://www.hyperion-rt.org).
We present TransitFit, an open-source Python~3 package designed to fit exoplanetary transit light-curves for transmission spectroscopy studies (Available at https://github.com/joshjchayes/TransitFit and https://github.com/spearnet/TransitFit, with documentation at https://transitfit.readthedocs.io/). TransitFit employs nested sampling to offer efficient and robust multi-epoch, multi-wavelength fitting of transit data obtained from one or more telescopes. TransitFit allows per-telescope detrending to be performed simultaneously with parameter fitting, including the use of user-supplied detrending alogorithms. Host limb darkening can be fitted either independently (uncoupled) for each filter or combined (coupled) using prior conditioning from the PHOENIX stellar atmosphere models. For this TransitFit uses the Limb Darkening Toolkit (LDTk) together with filter profiles, including user-supplied filter profiles. We demonstrate the application of TransitFit in three different contexts. First, we model SPEARNET broadband optical data of the low-density hot-Neptune WASP-127~b. The data were obtained from a globally-distributed network of 0.5m--2.4m telescopes. We find clear improvement in our broadband results using the coupled mode over uncoupled mode, when compared against the higher spectral resolution GTC/OSIRIS transmission spectrum obtained by Chen et al. (2018). Using TransitFit, we fit 26 transit observations by TESS to recover improved ephemerides of the hot-Jupiter WASP-91~b and a transit depth determined to a precision of 170~ppm. Finally, we use TransitFit to conduct an investigation into the contested presence of TTV signatures in WASP-126~b using 126 transits observed by TESS, concluding that there is no statistically significant evidence for such signatures from observations spanning 31 TESS sectors.
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