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Register a new userExoReL$^Re$: A Bayesian Inverse Retrieval Framework For Exoplanetary Reflected Light Spectra
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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.
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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$10-100x Earth levels).
Direct imaging of widely separated exoplanets from space will obtain their reflected light spectra and measure atmospheric properties. Previous calculations have shown that a change in the orbital phase would cause a spectral signal, but whether this signal may be used to characterize the atmosphere has not been shown. We simulate starshade-enabled observations of the planet 47 Uma b, using the to-date most realistic simulator SISTER to estimate the uncertainties due to residual starlight, solar glint, and exozodiacal light. We then use the Bayesian retrieval algorithm ExoReL$^Re$ to determine the constraints on the atmospheric properties from observations using a Roman- or HabEx-like telescope, comparing the strategies to observe at multiple orbital phases or in multiple wavelength bands. With a $sim20%$ bandwidth in 600 - 800 nm on a Roman-like telescope, the retrieval finds a degenerate scenario with a lower gas abundance and a deeper or absent cloud than the truth. Repeating the observation at a different orbital phase or at a second $20%$ wavelength band in 800 - 1000 nm, with the same integration time and thus degraded S/N, would effectively eliminate this degenerate solution. Single observation with a HabEx-like telescope would yield high-precision constraints on the gas abundances and cloud properties, without the degenerate scenario. These results are also generally applicable to high-contrast spectroscopy with a coronagraph with a similar wavelength coverage and S/N, and can help design the wavelength bandwidth and the observation plan of exoplanet direct imaging experiments in the future.
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.
For terrestrial exoplanets with thin atmospheres or no atmospheres, the surface contributes light to the reflected light signal of the planet. Measurement of the variety of disk-integrated brightnesses of bodies in the Solar System and the variation with illumination and wavelength is essential for both planning imaging observations of directly imaged exoplanets and interpreting the eventual datasets. Here we measure the change in brightness of the Galilean satellites as a function of planetocentric longitude, illumination phase angle, and wavelength. The data span a range of wavelengths from 400-950nm and predominantly phase angles from 0-25 degrees, with some constraining observations near 60-140 degrees. Despite the similarity in size and density between the moons, surface inhomogeneities result in significant changes in the disk-integrated reflectivity with planetocentric longitude and phase angle. We find that these changes are sufficient to determine the rotational periods of the moon. We also find that at low phase angles the surface can produce reflectivity variations of 8-36% and the limited high phase angle observations suggest variations will have proportionally larger amplitudes at higher phase angles. Additionally, all the Galilean satellites are darker than predicted by an idealized Lambertian model at the phases most likely to be observed by direct-imaging missions. If Earth-size exoplanets have surfaces similar to that of the Galilean moons, we find that future direct imaging missions will need to achieve precisions of less than 0.1,ppb. Should the necessary precision be achieved, future exoplanet observations could exploit similar observation schemes to deduce surface variations, determine rotation periods, and potentially infer surface composition.
RefPlanets is a guaranteed time observation (GTO) programme that uses the Zurich IMaging POLarimeter (ZIMPOL) of SPHERE/VLT for a blind search for exoplanets in wavelengths from 600-900 nm. The goals of this study are the characterization of the unprecedented high polarimetic contrast and polarimetric precision capabilities of ZIMPOL for bright targets, the search for polarized reflected light around some of the closest bright stars to the Sun and potentially the direct detection of an evolved cold exoplanet for the first time. For our observations of Alpha Cen A and B, Sirius A, Altair, Eps Eri and Tau Ceti we used the polarimetric differential imaging (PDI) mode of ZIMPOL which removes the speckle noise down to the photon noise limit for angular separations >0.6. We describe some of the instrumental effects that dominate the noise for smaller separations and explain how to remove these additional noise effects in post-processing. We then combine PDI with angular differential imaging (ADI) as a final layer of post-processing to further improve the contrast limits of our data at these separations. For good observing conditions we achieve polarimetric contrast limits of 15.0-16.3 mag at the effective inner working angle of about 0.13, 16.3-18.3 mag at 0.5 and 18.8-20.4 mag at 1.5. The contrast limits closer in (<0.6) depend significantly on the observing conditions, while in the photon noise dominated regime (>0.6), the limits mainly depend on the brightness of the star and the total integration time. We compare our results with contrast limits from other surveys and review the exoplanet detection limits obtained with different detection methods. For all our targets we achieve unprecedented contrast limits. Despite the high polarimetric contrasts we are not able to find any additional companions or extended polarized light sources in the data that has been taken so far.
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