Characterisation of exoplanetary atmospheres: from target selection to feature recognition using deep learning

On the day of writing this thesis, we know about 4000 exoplanets beyond the Solar System. We have a wide variety of known exoplanets, from very hot giant planets to cold Earths. Planetary detections, nevertheless, are not enough to thoroughly investigate the history and chemistry of the exoplanets. For this reason, atmospheric characterisation is becoming more critical than ever in exoplanetary science. In the next decade, many space missions such as JWST, Twinkle, ground-based instruments (ELT, TMT), and ARIEL, will study spectroscopically exoplanetary atmospheres and will help us examining more-in-depth planetary formation and dynamics. Today, the Hubble/WFC3 camera represents the state-of-art for transit spectroscopy. State of the art inverse models to interpret the observed exoplanetary spectra are based on Bayesian analysis, able to sample a sizeable parameter space and to converge on a possible real set of parameters that can explain the structure of exoplanetary spectra. In this thesis, I present the results obtained by applying the UCL Bayesian inverse model, TauREx, to the largest catalogue observed to date. I will demonstrate how it is possible to find water vapour in 16 out of 30 planets chosen from the WFC3 planetary dataset. Often the input spectra are too noisy to obtain a result statistically significant. For this reason, I will introduce the ADI (Atmospheric Detection Index) index, able to quantify the ``goodness'' and the significance of molecular detection. The use of complex atmospheric models on Bayesian analysis tools can require a prohibitive amount of time. For this reason, it is crucial to improve the analysis efficiency of complex atmospheres and accelerate their computations. To speed up the computation of atmospheric spectroscopic retrievals, I developed ExoGAN (Exoplanetary Generative Adversarial Network), a new-generation deep learning algorithm able to learn how to generate atmospheric spectra and retrieve the best set of parameters that can explain the observed spectrum. It consists of a deep convolutional generative adversarial network able to recognise molecular features, abundances and physical atmospheric parameters. Finally, after describing more ``traditional'' atmospheric retrieval tools, their optimisation using deep learning algorithm and their application to real data-sets (i.e. the HST/WFC3 camera), I introduce a possible target list of planet candidates for a space mission dedicated to transit spectroscopy: the ARIEL space mission. Target selection is a crucial task to optimally select the planets with different basic parameters to sample uniformly the whole orbital and physical parameters space. The generation of an optimal target list is highly dependent on the type of instrument, and it will critically influence the science return of the mission

ExoGAN: Retrieving Exoplanetary Atmospheres Using Deep Convolutional Generative Adversarial Networks

Atmospheric retrievals on exoplanets usually involve computationally intensive Bayesian sampling methods. Large parameter spaces and increasingly complex atmospheric models create a computational bottleneck forcing a trade-off between statistical sampling accuracy and model complexity. It is especially true for upcoming JWST and ARIEL observations. We introduce ExoGAN, the Exoplanet Generative Adversarial Network, a new deep learning algorithm able to recognise molecular features, atmospheric trace-gas abundances and planetary parameters using unsupervised learning. Once trained, ExoGAN is widely applicable to a large number of instruments and planetary types. The ExoGAN retrievals constitute a significant speed improvement over traditional retrievals and can be used either as a final atmospheric analysis or provide prior constraints to subsequent retrieval.Comment: 19 pages, 17 figures, 7 table

Toward a multidimensional analysis of transmission spectroscopy. Part III: Modelling 2D effects in retrievals with TauREx

New-generation spectrographs dedicated to the study of exoplanetary atmospheres require a high accuracy in the atmospheric models to better interpret the input spectra. Thanks to space missions, the observed spectra will cover a large wavelength range from visible to mid-infrared with an higher precision compared to the old-generation instrumentation, revealing complex features coming from different regions of the atmosphere. For hot and ultra hot Jupiters (HJs and UHJs), the main source of complexity in the spectra comes from thermal and chemical differences between the day and the night sides. In this context, one-dimensional plane parallel retrieval models of atmospheres may not be suitable to extract the complexity of such spectra. In addition, Bayesian frameworks are computationally intensive and prevent us from using complete three-dimensional self-consistent models to retrieve exoplanetary atmospheres. We propose the TauREx 2D retrieval code, which uses two-dimensional atmospheric models as a good compromise between computational cost and model accuracy to better infer exoplanetary atmospheric characteristics for the hottest planets. TauREx 2D uses a 2D parametrization across the limb which computes the transmission spectrum from an exoplanetary atmosphere assuming azimuthal symmetry. It also includes a thermal dissociation model of various species. We demonstrate that, given an input observation, TauREx 2D mitigates the biases between the retrieved atmospheric parameters and the real atmospheric parameters. We also show that having a prior knowledge on the link between local temperature and composition is instrumental in inferring the temperature structure of the atmosphere. Finally, we apply such a model on a synthetic spectrum computed from a GCM simulation of WASP-121b and show how parameter biases can be removed when using two-dimensional forward models across the limb.Comment: 16 pages, 16 figures. Accepted for publication in Astronomy & Astrophysic

Exoplanet spectroscopy and photometry with the Twinkle space telescope

The Twinkle space telescope has been designed for the characterisation of exoplanets and Solar System objects. Operating in a low Earth, Sun-synchronous orbit, Twinkle is equipped with a 45 cm telescope and visible (0.4 – 1 μm) and infrared (1.3 – 4.5 μm) spectrometers which can be operated simultaneously. Twinkle is a general observatory which will provide on-demand observations of a wide variety of targets within wavelength ranges that are currently not accessible using other space telescopes or accessible only to oversubscribed observatories in the short-term future. Here we explore the ability of Twinkle’s spectrometers to characterise the currently-known exoplanets. We study the spectral resolution achievable by combining multiple observations for various planetary and stellar types. We also simulate spectral retrievals for some well-known planets (HD 209458 b, GJ 3470 b and 55 Cnc e). From the exoplanets known today, we find that with a single transit or eclipse, Twinkle could probe 89 planets at low spectral resolution (R 20) in channel 1 (1.3 – 4.5 μm). With 10 observations, the atmospheres of 144 planets could be characterised with R 20. By stacking 10 transits, there are 1185 potential targets for study at R < 20 as well as 388 planets at higher resolutions. The majority of targets are found to be large gaseous planets although by stacking multiple observations smaller planets around bright stars (e.g. 55 Cnc e) could be observed with Twinkle. Photometry and low resolution spectroscopy with Twinkle will be useful to refine planetary, stellar and orbital parameters, monitor stellar activity through time and search for transit time and duration variations (TTVs and TDVs). Refinement of these parameters could be used to in the planning of observations with larger space-based observatories such as JWST and ARIEL. For planets orbiting very bright stars, Twinkle observations at higher spectral resolution will enable us to probe the chemical and thermal properties of an atmosphere. Simultaneous coverage across a wide wavelength range will reduce the degeneracies seen with Hubble and provide access to detections of a wide range molecules. There is the potential to revisit them many times over the mission lifetime to detect variations in cloud cover

A chemical survey of exoplanets with ARIEL

Thousands of exoplanets have now been discovered with a huge range of masses, sizes and orbits: from rocky Earth-like planets to large gas giants grazing the surface of their host star. However, the essential nature of these exoplanets remains largely mysterious: there is no known, discernible pattern linking the presence, size, or orbital parameters of a planet to the nature of its parent star. We have little idea whether the chemistry of a planet is linked to its formation environment, or whether the type of host star drives the physics and chemistry of the planet’s birth, and evolution. ARIEL was conceived to observe a large number (~1000) of transiting planets for statistical understanding, including gas giants, Neptunes, super-Earths and Earth-size planets around a range of host star types using transit spectroscopy in the 1.25–7.8 μm spectral range and multiple narrow-band photometry in the optical. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres which should show minimal condensation and sequestration of high-Z materials compared to their colder Solar System siblings. Said warm and hot atmospheres are expected to be more representative of the planetary bulk composition. Observations of these warm/hot exoplanets, and in particular of their elemental composition (especially C, O, N, S, Si), will allow the understanding of the early stages of planetary and atmospheric formation during the nebular phase and the following few million years. ARIEL will thus provide a representative picture of the chemical nature of the exoplanets and relate this directly to the type and chemical environment of the host star. ARIEL is designed as a dedicated survey mission for combined-light spectroscopy, capable of observing a large and well-defined planet sample within its 4-year mission lifetime. Transit, eclipse and phase-curve spectroscopy methods, whereby the signal from the star and planet are differentiated using knowledge of the planetary ephemerides, allow us to measure atmospheric signals from the planet at levels of 10–100 part per million (ppm) relative to the star and, given the bright nature of targets, also allows more sophisticated techniques, such as eclipse mapping, to give a deeper insight into the nature of the atmosphere. These types of observations require a stable payload and satellite platform with broad, instantaneous wavelength coverage to detect many molecular species, probe the thermal structure, identify clouds and monitor the stellar activity. The wavelength range proposed covers all the expected major atmospheric gases from e.g. H2O, CO2, CH4 NH3, HCN, H2S through to the more exotic metallic compounds, such as TiO, VO, and condensed species. Simulations of ARIEL performance in conducting exoplanet surveys have been performed – using conservative estimates of mission performance and a full model of all significant noise sources in the measurement – using a list of potential ARIEL targets that incorporates the latest available exoplanet statistics. The conclusion at the end of the Phase A study, is that ARIEL – in line with the stated mission objectives – will be able to observe about 1000 exoplanets depending on the details of the adopted survey strategy, thus confirming the feasibility of the main science objectives.Peer reviewedFinal Published versio

Enabling planetary science across light-years. Ariel Definition Study Report

Ariel, the Atmospheric Remote-sensing Infrared Exoplanet Large-survey, was adopted as the fourth medium-class mission in ESA's Cosmic Vision programme to be launched in 2029. During its 4-year mission, Ariel will study what exoplanets are made of, how they formed and how they evolve, by surveying a diverse sample of about 1000 extrasolar planets, simultaneously in visible and infrared wavelengths. It is the first mission dedicated to measuring the chemical composition and thermal structures of hundreds of transiting exoplanets, enabling planetary science far beyond the boundaries of the Solar System. The payload consists of an off-axis Cassegrain telescope (primary mirror 1100 mm x 730 mm ellipse) and two separate instruments (FGS and AIRS) covering simultaneously 0.5-7.8 micron spectral range. The satellite is best placed into an L2 orbit to maximise the thermal stability and the field of regard. The payload module is passively cooled via a series of V-Groove radiators; the detectors for the AIRS are the only items that require active cooling via an active Ne JT cooler. The Ariel payload is developed by a consortium of more than 50 institutes from 16 ESA countries, which include the UK, France, Italy, Belgium, Poland, Spain, Austria, Denmark, Ireland, Portugal, Czech Republic, Hungary, the Netherlands, Sweden, Norway, Estonia, and a NASA contribution

Toward a multidimensional analysis of transmission spectroscopy: I. Computation of transmission spectra using a 1D, 2D, or 3D atmosphere structure

International audienceConsidering the relatively high precision that will be reached by future observatories, it has recently become clear that one dimensional (1D) atmospheric models, in which the atmospheric temperature and composition of a planet are considered to vary only in the vertical, will be unable to represent exoplanetary transmission spectra with a sufficient accuracy. This is particularly true for warm to (ultra-) hot exoplanets because the atmosphere is unable to redistribute all the energy deposited on the dayside, creating a strong thermal and often compositional dichotomy on the planet. This situation is exacerbated by transmission spectroscopy, which probes the terminator region. This is the most heterogeneous region of the atmosphere. However, if being able to compute transmission spectra from 3D atmospheric structures (from a global climate model, e.g.) is necessary to predict realistic observables, it is too computationally expensive to be used in a data inversion framework. For this reason, there is a need for a medium-complexity 2D approach that captures the most salient features of the 3D model in a sufficiently fast implementation. With this in mind, we present a new open-source documented version of Pytmosph3R that handles the computation of transmission spectra for atmospheres with up to three spatial dimensions and can account for time variability. Taking the example of an ultrahot Jupiter, we illustrate how the changing orientation of the planet during the transit can allow us to probe the horizontal variations in the atmosphere. We further implement our algorithm in TauREx to allow the community to perform 2D retrievals. We describe our extensive cross-validation benchmarks and discuss the accuracy and numerical performance of each model

Strong biases in retrieved atmospheric composition caused by day–night chemical heterogeneities

Most planets currently amenable to transit spectroscopy are close enough to their host stars to exhibit a relatively strong day to night temperature gradient. For hot planets this leads to a chemical composition dichotomy between the two hemispheres. In the extreme case of ultra-hot Jupiters, some species, such as molecular hydrogen and water, are strongly dissociated on the day side while others, such as carbon monoxide, are not. However, most current retrieval algorithms rely on 1D forward models that are unable to reproduce this effect. We thus investigate how the 3D structure of the atmosphere biases the abundances retrieved using commonly used algorithms. We study the case of Wasp-121b as a prototypical ultra-hot Jupiter. We use the simulations of this planet performed with the Substellar and Planetary Atmospheric Radiation and Circulation global climate model and generate transmission spectra that fully account for the 3D structure of the atmosphere with Pytmosph3R. These spectra are then analyzed using the TauREx retrieval code. We find that the ultra-hot Jupiter transmission spectra exhibit muted H2O features that originate on the night side where the temperature, hence the scale-height, is smaller than on the day side. However, the spectral features of molecules present on the day side are boosted by both its high temperature and low mean molecular weight. As a result, the retrieved parameters are strongly biased compared to the ground truth. In particular the [CO]/[H2O] is overestimated by one to three orders of magnitude. This must be kept in mind when using the retrieval analysis to infer the C/O of a planet’s atmosphere. We also discuss whether indicators can allow us to infer the 3D structure of an observed atmosphere. Finally, we show that Wide Field Camera 3 from Hubble Space Telescope transmission data of Wasp-121b are compatible with the day–night thermal and compositional dichotomy predicted by models

The ARIEL Mission Reference Sample

31 pages, 32 figures, submitted to Experimental Astronomy, ARIEL special issueInternational audienceThe main purpose of this paper is to identify an optimal list of targets observable by ARIEL (Atmospheric Remote-sensing Exoplanet Largesurvey), one of the three European Space Agency M4 mission candidates competing for a launch in 2026. Here we describe the assumptions made to estimate an optimal sample of exoplanets - including both the already known exoplanets and the "expected" ones yet to be discovered - observable by ARIEL and define a realistic mission scenario. The current ARIEL design enables the observation of ~1000 planets during its four year mission lifetime. This nominal list of planets is expected to evolve over the years depending on the new exoplanet discoveries

The ARIEL mission reference sample

31 pages, 32 figures, submitted to Experimental Astronomy, ARIEL special issueInternational audienceThe main purpose of this paper is to identify an optimal list of targets observable by ARIEL (Atmospheric Remote-sensing Exoplanet Largesurvey), one of the three European Space Agency M4 mission candidates competing for a launch in 2026. Here we describe the assumptions made to estimate an optimal sample of exoplanets - including both the already known exoplanets and the "expected" ones yet to be discovered - observable by ARIEL and define a realistic mission scenario. The current ARIEL design enables the observation of ~1000 planets during its four year mission lifetime. This nominal list of planets is expected to evolve over the years depending on the new exoplanet discoveries