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Register a new userAurora: A Generalised Retrieval Framework for Exoplanetary Transmission Spectra
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Atmospheric retrievals of exoplanetary transmission spectra provide important constraints on various properties such as chemical abundances, cloud/haze properties, and characteristic temperatures, at the day-night atmospheric terminator. To date, most spectra have been observed for giant exoplanets due to which retrievals typically assume H-rich atmospheres. However, recent observations of mini-Neptunes/super-Earths, and the promise of upcoming facilities including JWST, call for a new generation of retrievals that can address a wide range of atmospheric compositions and related complexities. Here we report Aurora, a next-generation atmospheric retrieval framework that builds upon state-of-the-art architectures and incorporates the following key advancements: a) a generalised compositional retrieval allowing for H-rich and H-poor atmospheres, b) a generalised prescription for inhomogeneous clouds/hazes, c) multiple Bayesian inference algorithms for high-dimensional retrievals, d) modular considerations for refraction, forward scattering, and Mie-scattering, and e) noise modeling functionalities. We demonstrate Aurora on current and/or synthetic observations of hot Jupiter HD209458b, mini-Neptune K218b, and rocky exoplanet TRAPPIST1d. Using current HD209458b spectra, we demonstrate the robustness of our framework and cloud/haze prescription against assumptions of H-rich/H-poor atmospheres, improving on previous treatments. Using real and synthetic spectra of K218b, we demonstrate the agnostic approach to confidently constrain its bulk atmospheric composition and obtain precise abundance estimates. For TRAPPIST1d, 10 JWST NIRSpec transits can enable identification of the main atmospheric component for cloud-free CO$_2$-rich and N$_2$-rich atmospheres, and abundance constraints on trace gases including initial indications of O$_3$ if present at enhanced levels ($sim$10-100x Earth levels).
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The high-contrast imaging technique is meant to provide insight into those planets orbiting several astronomical units from their host star. Space missions such as WFIRST, HabEx, and LUVOIR will measure reflected light spectra of cold gaseous and rocky planets. To interpret these observations we introduce ExoReL$^Re$ (Exoplanetary Reflected Light Retrieval), a novel Bayesian retrieval framework to retrieve cloud properties and atmospheric structures from exoplanetary reflected light spectra. As a unique feature, it assumes a vertically non-uniform volume mixing ratio profile of water and ammonia, and use it to construct cloud densities. In this way, clouds and molecular mixture ratios are consistent. We apply ExoReL$^Re$ on three test cases: two exoplanets ($upsilon$ And e and 47 Uma b) and Jupiter. We show that we are able to retrieve the concentration of methane in the atmosphere, and estimate the position of clouds when the S/N of the spectrum is higher than 15, in line with previous works. Moreover, we described the ability of our model of giving a chemical identity to clouds, and we discussed whether or not we can observe this difference in the planetary reflection spectrum. Finally, we demonstrate how it could be possible to retrieve molecular concentrations (water and ammonia in this work) below the clouds by linking the non-uniform volume mixing ratio profile to the cloud presence. This will help to constrain the concentration of water and ammonia unseen in direct measurements.
Transmission spectrum surveys have suggested the ubiquity of high-altitude clouds in exoplanetary atmospheres. Theoretical studies have investigated the formation processes of the high-altitude clouds; however, cloud particles have been commonly approximated as compact spheres, which is not always true for solid mineral particles that likely constitute exoplanetary clouds. Here, we investigate how the porosity of cloud particles evolve in exoplanetary atmospheres and influence the cloud vertical profiles. We first construct a porosity evolution model that takes into account the fractal aggregation and the compression of cloud particle aggregates. Using a cloud microphysical model coupled with the porosity model, we demonstrate that the particle internal density can significantly decrease during the cloud formation. As a result, fluffy-aggregate clouds ascend to altitude much higher than that for compact-sphere clouds assumed so far. We also examine how the fluffy-aggregate clouds affect transmission spectra. We find that the clouds largely obscure the molecular features and produce a spectral slope originated by the scattering properties of aggregates. Finally, we compare the synthetic spectra with the observations of GJ1214 b and find that its flat spectrum could be explained if the atmospheric metallicity is sufficiently high ($ge100times$ solar) and the monomer size is sufficiently small ($r_{rm mon}<1~{rm {mu}m}$). The high-metallicity atmosphere may offer the clues to explore the gas accretion processes onto past GJ1214b.
Machine learning is now used in many areas of astrophysics, from detecting exoplanets in Kepler transit signals to removing telescope systematics. Recent work demonstrated the potential of using machine learning algorithms for atmospheric retrieval by implementing a random forest to perform retrievals in seconds that are consistent with the traditional, computationally-expensive nested-sampling retrieval method. We expand upon their approach by presenting a new machine learning model, texttt{plan-net}, based on an ensemble of Bayesian neural networks that yields more accurate inferences than the random forest for the same data set of synthetic transmission spectra. We demonstrate that an ensemble provides greater accuracy and more robust uncertainties than a single model. In addition to being the first to use Bayesian neural networks for atmospheric retrieval, we also introduce a new loss function for Bayesian neural networks that learns correlations between the model outputs. Importantly, we show that designing machine learning models to explicitly incorporate domain-specific knowledge both improves performance and provides additional insight by inferring the covariance of the retrieved atmospheric parameters. We apply texttt{plan-net} to the Hubble Space Telescope Wide Field Camera 3 transmission spectrum for WASP-12b and retrieve an isothermal temperature and water abundance consistent with the literature. We highlight that our method is flexible and can be expanded to higher-resolution spectra and a larger number of atmospheric parameters.
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.
Transmission spectra of exoplanetary atmospheres have been used to infer the presence of clouds/hazes. Such inferences are typically based on spectral slopes in the optical deviant from gaseous Rayleigh scattering or low-amplitude spectral features in the infrared. We investigate three observable metrics that could allow constraints on cloud properties from transmission spectra, namely, the optical slope, the uniformity of this slope, and condensate features in the infrared. We derive these metrics using model transmission spectra considering Mie extinction from a wide range of condensate species, particle sizes, and scale heights. Firstly, we investigate possible degeneracies among the cloud properties for an observed slope. We find, for example, that spectra with very steep optical slopes suggest sulphide clouds (e.g. MnS, ZnS, Na$_2$S) in the atmospheres. Secondly, (non)uniformities in optical slopes provide additional constraints on cloud properties, e.g., MnS, ZnS, TiO$_2$, and Fe$_2$O$_3$ have significantly non-uniform slopes. Thirdly, infrared spectra provide an additional powerful probe into cloud properties, with SiO$_2$, Fe$_2$O$_3$, Mg$_2$SiO$_4$, and MgSiO$_3$ bearing strong infrared features observable with the James Webb Space Telescope. We investigate observed spectra of eight hot Jupiters and discuss their implications. In particular, no single or composite condensate species considered here conforms to the steep and non-uniform optical slope observed for HD 189733b. Our work highlights the importance of the three above metrics to investigate cloud properties in exoplanetary atmospheres using high-precision transmission spectra and detailed cloud models. We make our Mie scattering data for condensates publicly available to the community.
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