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Register a new userThe PHOENIX Exoplanet Retrieval Algorithm and Using H$^{-}$ Opacity as a Probe in Ultra-hot Jupiters
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Atmospheric retrievals are now a standard tool to analyze observations of exoplanet atmospheres. This data-driven approach quantitatively compares atmospheric models to observations in order to estimate atmospheric properties and their uncertainties. In this paper, we introduce a new retrieval package, the PHOENIX Exoplanet Retrieval Analysis (PETRA). PETRA places the PHOENIX atmosphere model in a retrieval framework, allowing us to combine the strengths of a well-tested and widely-used atmosphere model with the advantages of retrieval algorithms. We validate PETRA by retrieving on simulated data for which the true atmospheric state is known. We also show that PETRA can successfully reproduce results from previously published retrievals of WASP-43b and HD 209458b. For the WASP-43b results, we show the effect that different line lists and line profile treatments have on the retrieved atmospheric properties. Lastly, we describe a novel technique for retrieving the temperature structure and $e^{-}$ density in ultra-hot Jupiters using H$^{-}$ opacity, allowing us to probe atmospheres devoid of most molecular features with JWST.
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In this paper we present three new extrasolar planets from the Qatar Exoplanet Survey (QES). Qatar-8b is a hot Saturn, with Mpl = 0.37 Mjup and Rpl = 1.3 Rjup, orbiting a solar-like star every Porb = 3.7 days. Qatar-9b is a hot Jupiter with a mass of Mpl = 1.2 Mjup and a radius of Rpl = 1 Rjup, in a Porb = 1.5 days orbit around a low mass, Mstar = 0.7 Msun, mid-K main-sequence star. Finally, Qatar-10b is a hot, Teq ~ 2000 K, sub-Jupiter mass planet, Mpl = 0.7 Mjup, with a radius of Rpl = 1.54 Rjup and an orbital period of Porb = 1.6 days, placing it on the edge of the sub-Jupiter desert.
We present near infrared high-precision photometry for eight transiting hot Jupiters observed during their predicted secondary eclipses. Our observations were carried out using the staring mode of the WIRCam instrument on the Canada-France-Hawaii Telescope (CFHT). We present the observing strategies and data reduction methods which delivered time series photometry with statistical photometric precisionas low as 0.11%. We performed a Bayesian analysis to model the eclipse parameters and systematics simultaneously. The measured planet-to-star flux ratios allowed us to constrain the thermal emission from the day side of these hot Jupiters, as we derived the planet brightness temperatures. Our results combined with previously observed eclipses reveal an excess in the brightness temperatures relative to the blackbody prediction for the equilibrium temperatures of the planets for a wide range of heat redistribution factors. We find a trend that this excess appears to be larger for planets with lower equilibrium temperatures. This may imply some additional sources of radiation, such as reflected light from the host star and/or thermal emission from residual internal heat from the formation of the planet.
One of the principal bottlenecks to atmosphere characterisation in the era of all-sky surveys is the availability of fast, autonomous and robust atmospheric retrieval methods. We present a new approach using unsupervised machine learning to generate informed priors for retrieval of exoplanetary atmosphere parameters from transmission spectra. We use principal component analysis (PCA) to efficiently compress the information content of a library of transmission spectra forward models generated using the PLATON package. We then apply a $k$-means clustering algorithm in PCA space to segregate the library into discrete classes. We show that our classifier is almost always able to instantaneously place a previously unseen spectrum into the correct class, for low-to-moderate spectral resolutions, $R$, in the range $R~=~30-300$ and noise levels up to $10$~per~cent of the peak-to-trough spectrum amplitude. The distribution of physical parameters for all members of the class therefore provides an informed prior for standard retrieval methods such as nested sampling. We benchmark our informed-prior approach against a standard uniform-prior nested sampler, finding that our approach is up to a factor two faster, with negligible reduction in accuracy. We demonstrate the application of this method to existing and near-future observatories, and show that it is suitable for real-world application. Our general approach is not specific to transmission spectroscopy and should be more widely applicable to cases that involve repetitive fitting of trusted high-dimensional models to large data catalogues, including beyond exoplanetary science.
Extremely irradiated, close-in planets to early-type stars might be prone to strong atmospheric escape. We review the literature showing that X-ray-to-optical measurements indicate that for intermediate-mass stars (IMS) cooler than $approx$8250 K, the X-ray and EUV (XUV) fluxes are on average significantly higher than those of solar-like stars, while for hotter IMS, because of the lack of surface convection, it is the opposite. We construct spectral energy distributions for prototypical IMS, comparing them to solar. The XUV fluxes relevant for upper planet atmospheric heating are highest for the cooler IMS and lowest for the hotter IMS, while the UV fluxes increase with increasing stellar temperature. We quantify the influence of this characteristic of the stellar fluxes on the mass loss of close-in planets by simulating the atmospheres of planets orbiting EUV-bright (WASP-33) and EUV-faint (KELT-9) A-type stars. For KELT-9b, we find that atmospheric expansion caused by heating due to absorption of the stellar UV and optical light drives mass-loss rates of $approx$10$^{11}$ g s$^{-1}$, while heating caused by absorption of the stellar XUV radiation leads to mass-loss rates of $approx$10$^{10}$ g s$^{-1}$, thus underestimating mass loss. For WASP-33b, the high XUV stellar fluxes lead to mass-loss rates of $approx$10$^{11}$ g s$^{-1}$. Even higher mass-loss rates are possible for less massive planets orbiting EUV-bright IMS. We argue that it is the weak XUV stellar emission, combined with a relatively high planetary mass, which limit planetary mass-loss rates, to allow the prolonged existence of KELT-9-like systems.
Aims: ARCiS, a novel code for the analysis of exoplanet transmission and emission spectra is presented. The aim of the modelling framework is to provide a tool able to link observations to physical models of exoplanet atmospheres. Methods: The modelling philosophy chosen in this paper is to use physical and chemical models to constrain certain parameters while keeping free the parts where our physical understanding is still more limited. This approach, in between full physical modelling and full parameterisation, allows us to use the processes we understand well and parameterise those less understood. A Bayesian retrieval framework is implemented and applied to the transit spectra of a set of 10 hot Jupiters. The code contains chemistry and cloud formation and has the option for self consistent temperature structure computations. Results: The code presented is fast and flexible enough to be used for retrieval and for target list simulations for e.g. JWST or the ESA Ariel missions. We present results for the retrieval of elemental abundance ratios using the physical retrieval framework and compare this to results obtained using a parameterised retrieval setup. Conclusions: We conclude that for most of the targets considered the current dataset is not constraining enough to reliably pin down the elemental abundance ratios. We find no significant correlations between different physical parameters. We confirm that planets in our sample with a strong slope in the optical transmission spectrum are the planets where we find cloud formation to be most active. Finally, we conclude that with ARCiS we have a computationally efficient tool to analyse exoplanet observations in the context of physical and chemical models.
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