On spectroscopic phase-curve retrievals: H2 dissociation and thermal inversion in the atmosphere of the ultra-hot Jupiter WASP-103 b

This work presents a re-analysis of the spectroscopic phase-curve observations of the ultra hot Jupiter WASP-103 b obtained by the Hubble Space Telescope (HST) and the Spitzer Telescope. Traditional 1D and unified 1.5D spectral retrieval techniques are employed, allowing to map the thermal structure and the abundances of trace gases in this planet as a function of longitude. On the day-side, the atmosphere is found to have a strong thermal inversion, with indications of thermal dissociation traced by continuum H- opacity. Water vapor is found across the entire atmosphere but with depleted abundances of around 1e-5, consistent with the thermal dissociation of this molecule. Regarding metal oxide and hydrides, FeH is detected on the hot-spot and the day-side of WASP-103 b, but TiO and VO are not present in detectable quantities. Carbon-bearing species such as CO and CH4 are also found, but since their detection is reliant on the combination of HST and Spizer, the retrieved abundances should be interpreted with caution. Free and Equilibrium chemistry retrievals are overall consistent, allowing to recover robust constraints on the metallicity and C/O ratio for this planet. The analyzed phase-curve data indicates that the atmosphere of WASP-103 b is consistent with solar elemental ratios

The hubble WFC3 emission spectrum of the extremely hot jupiter KELT-9b

Recent studies of ultra-hot Jupiters suggested that their atmospheres could have thermal inversions due to the presence of optical absorbers such as titanium oxide (TiO), vanadium oxide (VO), iron hydride (FeH), and other metal hydride/oxides. However, it is expected that these molecules would thermally dissociate at extremely high temperatures, thus leading to featureless spectra in the infrared. KELT-9 b, the hottest exoplanet discovered so far, is thought to belong to this regime and host an atmosphere dominated by neutral hydrogen from dissociation and atomic/ionic species. Here, we analyzed the eclipse spectrum obtained using the Hubble Space Telescope's Wide Field Camera 3 and, by utilizing the atmospheric retrieval code TauREx3, found that the spectrum is consistent with the presence of molecular species and is poorly fitted by a simple blackbody. In particular, we find that a combination of TiO, VO, FeH, and H- provides the best fit when considering Hubble Space Telescope (HST), Spitzer, and TESS data sets together. Aware of potential biases when combining instruments, we also analyzed the HST spectrum alone and found that TiO and VO only were needed in this case. These findings paint a more complex picture of the atmospheres of ultra-hot planets than previously thought

ESA-Ariel Data Challenge NeurIPS 2022: introduction to exo-atmospheric studies and presentation of the Atmospheric Big Challenge (ABC) Database

This is an exciting era for exo-planetary exploration. The recently launched JWST, and other upcoming space missions such as Ariel, Twinkle, and ELTs are set to bring fresh insights to the convoluted processes of planetary formation and evolution and its connections to atmospheric compositions. However, with new opportunities come new challenges. The field of exoplanet atmospheres is already struggling with the incoming volume and quality of data, and machine learning (ML) techniques lands itself as a promising alternative. Developing techniques of this kind is an inter-disciplinary task, one that requires domain knowledge of the field, access to relevant tools and expert insights on the capability and limitations of current ML models. These stringent requirements have so far limited the developments of ML in the field to a few isolated initiatives. In this paper, We present the Atmospheric Big Challenge Database (ABC Database), a carefully designed, organized, and publicly available data base dedicated to the study of the inverse problem in the context of exoplanetary studies. We have generated 105 887 forward models and 26 109 complementary posterior distributions generated with Nested Sampling algorithm. Alongside with the data base, this paper provides a jargon-free introduction to non-field experts interested to dive into the intricacy of atmospheric studies. This data base forms the basis for a multitude of research directions, including, but not limited to, developing rapid inference techniques, benchmarking model performance, and mitigating data drifts. A successful application of this data base is demonstrated in the NeurIPS Ariel ML Data Challenge 2022

TauREx3 PhaseCurve: A 1.5D Model for Phase-curve Description

In recent years, retrieval analysis of exoplanet atmospheres have been very successful, providing deep insights on the composition and the temperature structure of these worlds via transit and eclipse methods. Analysis of spectral phase-curve observations, which in theory provide even more information, are still limited to a few planets. In the next decade, new facilities such as NASA–James Webb Space Telescope and ESA-Ariel will revolutionize the field of exoplanet atmospheres and we expect that a significant time will be spent on spectral phase-curve observations. Most current models are still limited in their analysis of phase-curve data as they do not consider the planet atmosphere as a whole or they require large computational resources. In this paper we present a semi-analytical model that will allow computing exoplanet emission spectra at different phase angles. Our model provides a way to simulate a large number of observations while being only about four times slower than the traditional forward model for plane–parallel primary eclipse. This model, which is based on the newly developed TauREx 3 framework, will be further developed to allow for phase-curve atmospheric retrievals

Impact of Planetary Mass Uncertainties on Exoplanet Atmospheric Retrievals

In current models used to interpret exoplanet atmospheric observations, the planetary mass is treated as a prior and is measured/estimated independently with external methods, such as radial velocity or transit timing variation techniques. This approach is necessary as available spectroscopic data do not have sufficient wavelength coverage and/or signal-to-noise to infer the planetary mass. We examine here whether the planetary mass can be directly retrieved from transit spectra as observed by future space observatories, which will provide higher quality spectra. More in general, we quantify the impact of mass uncertainties on spectral retrieval analyses for a host of atmospheric scenarios. Our approach is both analytical and numerical: we first use simple approximations to extract analytically the influence of each atmospheric/planetary parameter to the wavelength-dependent transit depth. We then adopt a fully Bayesian retrieval model to quantify the propagation of the mass uncertainty onto other atmospheric parameters. We found that for clear-sky, gaseous atmospheres the posterior distributions are the same when the mass is known or retrieved. The retrieved mass is very accurate, with a precision of more than 10%, provided the wavelength coverage and signal-to-noise ratio are adequate. When opaque clouds are included in the simulations, the uncertainties in the retrieved mass increase, especially for high altitude clouds. However, atmospheric parameters such as the temperature and trace-gas abundances are unaffected by the knowledge of the mass. Secondary atmospheres, expected to be present in many super-Earths, are more challenging due to the higher degree of freedom for the atmospheric main component, which is unknown. For broad wavelength range and adequate signal-to-noise observations, the mass can still be retrieved accurately and precisely if clouds are not present, and so are all the other atmospheric/planetary parameters. When clouds are added, we find that the mass uncertainties may impact substantially the retrieval of the mean molecular weight: an independent characterization of the mass would therefore be helpful to capture/confirm the main atmospheric constituent

TauREx 3: A Fast, Dynamic, and Extendable Framework for Retrievals

TauREx 3 is the next generation of the TauREx exoplanet atmospheric retrieval framework for Windows, Mac and Linux. It is a complete rewrite with a full Python stack that makes it simple to use, high performance and dynamic/flexible. The new main taurex program is extremely modular, allowing the user to augment taurex functionalities with their own code and easily perform retrievals on their own parameters. This is achieved by dynamic determination of fitting parameters where TauREx 3 can detect new parameters for retrieval from the user code though a simple interface. TauREx 3 can act as a library with a simple 'import taurex' providing a rich set of classes and functions related to atmospheric modelling. A 10x speed-up in forward model computations is achieved compared to the previous version with a six-fold reduction in retrieval times whilst maintaining robust results. TauREx 3 intends to act as a standalone, all in one package for retrievals whilst the TauREx 3 python library can be used by the user to easily build or augment their own data pipelines

Impact of planetary mass uncertainties on exoplanet atmospheric retrievals

In current models used to interpret exoplanet atmospheric observations, the planet mass is treated as a prior and is estimated independently with external methods, such as RV or TTV techniques. This approach is necessary as available spectroscopic data do not have sufficient wavelength coverage and/or SNR to infer the planetary mass. We examine here the impact of mass uncertainties on spectral retrieval analyses for a host of atmospheric scenarios. Our approach is both analytical and numerical: we first use simple approximations to extract analytically the influence of each parameter to the wavelength-dependent transit depth. We then adopt a fully Bayesian retrieval model to quantify the propagation of the mass uncertainty onto other atmospheric parameters. We found that for clear-sky, gaseous atmospheres the posterior distributions are the same when the mass is known or retrieved. The retrieved mass is very accurate, with a precision of more than 10%, provided the wavelength coverage and S/N are adequate. When opaque clouds are included in the simulations, the uncertainties in the retrieved mass increase, especially for high altitude clouds. However atmospheric parameters such as the temperature and trace-gas abundances are unaffected by the knowledge of the mass. Secondary atmospheres are more challenging due to the higher degree of freedom for the atmospheric main component, which is unknown. For broad wavelength range and adequate SNR, the mass can still be retrieved accurately and precisely if clouds are not present, and so are all the other atmospheric/planetary parameters. When clouds are added, we find that the mass uncertainties may impact substantially the retrieval of the mean molecular weight: an independent characterisation of the mass would therefore be helpful to capture/confirm the main atmospheric constituent.Comment: 19 pages, 12 figures, Accepted in Ap

An Exploration of Model Degeneracies with a Unified Phase Curve Retrieval Analysis: The Light and Dark Sides of WASP-43 b

The analysis of exoplanetary atmospheres often relies upon the observation of transit or eclipse events. While very powerful, these snapshots provide mainly one-dimensional information on the planet structure and do not easily allow precise latitude–longitude characterizations. The phase curve technique, which consists of measuring the planet emission throughout its entire orbit, can break this limitation and provide useful two-dimensional thermal and chemical constraints on the atmosphere. As of today, however, computing performances have limited our ability to perform unified retrieval studies on the full set of observed spectra from phase curve observations at the same time. Here, we present a new phase curve model that enables fast, unified retrieval capabilities. We apply our technique to the combined phase curve data from the Hubble and Spitzer space telescopes of the hot Jupiter WASP-43 b. We tested different scenarios and discussed the dependence of our solution on different assumptions in the model. Our more comprehensive approach suggests that multiple interpretations of this data set are possible, but our more complex model is consistent with the presence of thermal inversions and a metal-rich atmosphere, contrasting with previous data analyses, although this likely depends on the Spitzer data reduction. The detailed constraints extracted here demonstrate the importance of developing and understanding advanced phase curve techniques, which we believe will unlock access to a richer picture of exoplanet atmospheres

Toward a More Complex Description of Chemical Profiles in Exoplanet Retrievals: A Two-layer Parameterization

State of the art spectral retrieval models of exoplanet atmospheres assume constant chemical profiles with altitude. This assumption is justified by the information content of current data sets which do not allow, in most cases, for the molecular abundances as a function of pressure to be constrained. In the context of the next generation of telescopes, a more accurate description of chemical profiles may become crucial to interpret observations and gain new insights into atmospheric physics. We explore here the possibility of retrieving pressure-dependent chemical profiles from transit spectra, without injecting any priors from theoretical chemical models in our retrievals. The "two-layer" parameterization presented here allows for the independent extraction of molecular abundances above and below a certain atmospheric pressure. By simulating various cases, we demonstrate that this evolution from constant chemical abundances is justified by the information content of spectra provided by future space instruments. Comparisons with traditional retrieval models show that assumptions made on chemical profiles may significantly impact retrieved parameters, such as the atmospheric temperature, and justify the attention we give here to this issue. We find that the two-layer retrieval accurately captures discontinuities in the vertical chemical profiles, which could be caused by disequilibrium processes—such as photochemistry—or the presence of clouds/hazes. The two-layer retrieval could also help to constrain the composition of clouds and hazes by exploring the correlation between the chemical changes in the gaseous phase and the pressure at which the condensed phase occurs. The two-layer retrieval presented here therefore represents an important step forward in our ability to constrain theoretical chemical models and cloud/haze composition from the analysis of future observations