The Pyrat Bay Framework for Exoplanet Atmospheric Modeling: A Population Study of Hubble/WFC3 Transmission Spectra

Abstract

We present the open-source pyratbay framework for exoplanet atmospheric modeling, spectral synthesis, and Bayesian retrieval. The modular design of the code allows the users to generate atmospheric 1D parametric models of the temperature, abundances (in thermochemical equilibrium or constant-with-altitude), and altitude profiles in hydrostatic equilibrium; sample ExoMol and HITRAN line-by-line cross sections with custom resolving power and line-wing cutoff values; compute emission or transmission spectra considering cross sections from molecular line transitions, collision-induced absorption, Rayleigh scattering, gray clouds, and alkali resonance lines; and perform Markov chain Monte Carlo atmospheric retrievals for a given transit or eclipse dataset. We benchmarked the pyratbay framework by reproducing line-by-line cross-section sampling of ExoMol cross sections, producing transmission and emission spectra consistent with petitRADTRANS models, accurately retrieving the atmospheric properties of simulated transmission and emission observations generated with TauREx models, and closely reproducing Aura retrieval analyses of the space-based transmission spectrum of HD 209458b. Finally, we present a retrieval analysis of a population of transiting exoplanets, focusing on those observed in transmission with the HST WFC3/G141 grism. We found that this instrument alone can confidently identify when a dataset shows H2O-absorption features; however, it cannot distinguish whether a muted H2O feature is caused by clouds, high atmospheric metallicity, or low H2O abundance. Our results are consistent with previous retrieval analyses. The pyratbay code is available at PyPI (pip install pyratbay) and conda. The code is heavily documented (https://pyratbay.readthedocs.io) and tested to provide maximum accessibility to the community and long-term development stability.

arXiv commentAccepted for publication at MNRAS

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