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

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

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

FRECKLL: Full and Reduced Exoplanet Chemical Kinetics distiLLed

We introduce a new chemical kinetic code FRECKLL (Full and Reduced Exoplanet Chemical Kinetics distiLLed) to evolve large chemical networks efficiently. FRECKLL employs `distillation' in computing the reaction rates, which minimizes the error bounds to the minimum allowed by double precision values ($\epsilon \leq 10^{-15}$). FRECKLL requires less than 5 minutes to evolve the full Venot2020 network in a 130 layers atmosphere and 30 seconds to evolve the Venot2020 reduced scheme. Packaged with FRECKLL is a TauREx 3.1 plugin for usage in forward modelling and retrievals. We present TauREx retrievals performed on a simulated HD189733 JWST spectra using the full and reduced Venot2020 chemical networks and demonstrate the viability of total disequilibrium chemistry retrievals and the ability for JWST to detect disequilibrium processes.Comment: 13 pages, 8 figure

To Sample or Not to Sample: Retrieving Exoplanetary Spectra with Variational Inference and Normalizing Flows

Current endeavours in exoplanet characterization rely on atmospheric retrieval to quantify crucial physical properties of remote exoplanets from observations. However, the scalability and efficiency of said technique are under strain with increasing spectroscopic resolution and forward model complexity. The situation has become more acute with the recent launch of the James Webb Space Telescope and other upcoming missions. Recent advances in machine learning provide optimization-based variational inference as an alternative approach to perform approximate Bayesian posterior inference. In this investigation we developed a normalizing-flow-based neural network, combined with our newly developed differentiable forward model, Diff-τ, to perform Bayesian inference in the context of atmospheric retrievals. Using examples from real and simulated spectroscopic data, we demonstrate the advantages of our proposed framework: (1) training our neural network does not require a large precomputed training set and can be trained with only a single observation; (2) it produces high-fidelity posterior distributions in excellent agreement with sampling-based retrievals; (3) it requires up to 75% fewer forward model calls to converge to the same result; and (4) this approach allows formal Bayesian model selection. We discuss the computational efficiencies of Diff-τ in relation to TauREx3's nominal forward model and provide a “lessons learned” account of developing radiative transfer models in differentiable languages. Our proposed framework contributes toward the latest development of neural network–powered atmospheric retrieval. Its flexibility and significant reduction in forward model calls required for convergence holds the potential to be an important addition to the retrieval tool box for large and complex data sets along with sampling-based approaches

Spitzer thermal phase curve of WASP-121 b

Aims. We analyse unpublished Spitzer observations of the thermal phase-curve of WASP-121 b, a benchmark ultra-hot Jupiter. Methods. We adopted the wavelet pixel-independent component analysis technique to remove challenging instrumental systematic effects in these datasets and we fit them simultaneously with parametric light-curve models. We also performed phase-curve retrievals to better understand the horizontal and vertical thermal structure of the planetary atmosphere. Results. We measured planetary brightness temperatures of $\sim$2700\,K (dayside) and $\sim$700--1100\,K (nightside), along with modest peak offsets of 5.9$^{\circ} \pm$1.6 (3.6\,$\mu$m) and 5.0$^{\circ}$$_{-3.1}^{+3.4}$ (4.5\,$\mu$m) after mid-eclipse. These results suggest inefficient heat redistribution in the atmosphere of WASP-121 b. The inferred atmospheric Bond albedo and circulation efficiency align well with observed trends for hot giant exoplanets. Interestingly, the measured peak offsets correspond to a westward hot spot, which has rarely been observed. We also report consistent transit depths at 3.6 and 4.5\,$\mu$m, along with updated geometric and orbital parameters. Finally, we compared our Spitzer results with previous measurements, including recent JWST observations. Conclusions. We extracted new information on the thermal properties and dynamics of an exoplanet atmosphere from an especially problematic dataset. This study probes the reliability of exoplanet phase-curve parameters obtained from Spitzer observations when state-of-the-art pipelines are adopted to remove the instrumental systematic effects. It demonstrates that Spitzer phase-curve observations provide a useful baseline for comparison with JWST observations, and shows the increase in parameters precision achieved with the newer telescope.Comment: 14 pages, 10 figure

Constraining the atmospheric elements in hot Jupiters with Ariel

One of the main objectives of the European Space Agency’s Ariel telescope (launch 2029) is to understand the formation and evolution processes of a large sample of planets in our Galaxy. Important indicators of such processes in giant planets are the elemental compositions of their atmospheres. Here we investigate the capability of Ariel to constrain four key atmospheric markers: metallicity, C/O, S/O, and N/O, for three well-known, representative hot-Jupiter atmospheres observed with transit spectroscopy, i.e. HD 209458b, HD 189733b, and WASP-121b. We have performed retrieval simulations for these targets to verify how the planetary formation markers listed above would be recovered by Ariel when observed as part of the Ariel Tier 3 survey. We have considered eight simplified different atmospheric scenarios with a cloud-free isothermal atmosphere. Additionally, extra cases were tested to illustrate the effect of C/O and metallicity in recovering the N/O. From our retrieval results, we conclude that Ariel is able to recover the majority of planetary formation markers. The contributions from CO and CO2 are dominant for the C/O in the solar scenario. In a C-rich case, C2H2, HCN, and CH4 may provide additional spectral signatures that can be captured by Ariel. In our simulations, H2S is the main tracer for the S/O in hot-Jupiter atmospheres. In the super-solar metallicity cases and the cases with C/O > 1, the increased abundance of HCN is easily detectable and the main contributor to N/O, while other N-bearing species contribute little to the N/O in the investigated atmospheres

Detecting molecules in Ariel low resolution transmission spectra

The Ariel Space Mission aims to observe a diverse sample of exoplanet atmospheres across a wide wavelength range of 0.5 to 7.8 microns. The observations are organized into four Tiers, with Tier 1 being a reconnaissance survey. This Tier is designed to achieve a sufficient signal-to-noise ratio (S/N) at low spectral resolution in order to identify featureless spectra or detect key molecular species without necessarily constraining their abundances with high confidence. We introduce a P-statistic that uses the abundance posteriors from a spectral retrieval to infer the probability of a molecule’s presence in a given planet’s atmosphere in Tier 1. We find that this method predicts probabilities that correlate well with the input abundances, indicating considerable predictive power when retrieval models have comparable or higher complexity compared to the data. However, we also demonstrate that the P-statistic loses representativity when the retrieval model has lower complexity, expressed as the inclusion of fewer than the expected molecules. The reliability and predictive power of the P-statistic are assessed on a simulated population of exoplanets with H2-He dominated atmospheres, and forecasting biases are studied and found not to adversely affect the classification of the survey

Detectability of Rocky-Vapour Atmospheres on Super-Earths with Ariel

Ariel will mark the dawn of a new era as the first large-scale survey characterising exoplanetary atmospheres with science objectives to address fundamental questions about planetary composition, evolution and formation. In this study, we explore the detectability of atmospheres vaporised from magma oceans on dry, rocky Super-Earths orbiting very close to their host stars. The detection of such atmospheres would provide a definitive piece of evidence for rocky planets but are challenging measurements with currently available instruments due to their small spectral signatures. However, some of the hottest planets are believed to have atmospheres composed of vaporised rock, such as Na and SiO, with spectral signatures bright enough to be detected through eclipse observations with planned space-based telescopes. In this study, we find that rocky super-Earths with a irradiation temperature of 3000 K and a distance from Earth of up to 20 pc, as well as planets hotter than 3500 K and closer than 50 pc, have SiO features which are potentially detectable in eclipse spectra observed with Ariel.Comment: 12 pages, 8 figures, accepted for publication in Experimental Astronomy, Ariel Special Issu