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

A new look at Spitzer primary transit observations of the exoplanet HD189733b

Blind source separation techniques are used to reanalyse two exoplanetary transit lightcurves of the exoplanet HD189733b recorded with the IR camera IRAC on board the Spitzer Space Telescope at 3.6$\mu$m during the "cold" era. These observations, together with observations at other IR wavelengths, are crucial to characterise the atmosphere of the planet HD189733b. Previous analyses of the same datasets reported discrepant results, hence the necessity of the reanalyses. The method we used here is based on the Independent Component Analysis (ICA) statistical technique, which ensures a high degree of objectivity. The use of ICA to detrend single photometric observations in a self-consistent way is novel in the literature. The advantage of our reanalyses over previous work is that we do not have to make any assumptions on the structure of the unknown instrumental systematics. Such "admission of ignorance" may result in larger error bars than reported in the literature, up to a factor $1.6$. This is a worthwhile trade-off for much higher objectivity, necessary for trustworthy claims. Our main results are (1) improved and robust values of orbital and stellar parameters, (2) new measurements of the transit depths at 3.6$\mu$m, (3) consistency between the parameters estimated from the two observations, (4) repeatability of the measurement within the photometric level of $\sim 2 \times 10^{-4}$ in the IR, (5) no evidence of stellar variability at the same photometric level within 1 year.Comment: 43 pages, 18 figure

Non-Local thermal equilibrium spectra of atmospheric molecules for exoplanets

Here we present a study of non-LTE effects on the exoplanetary spectra of a collection of molecules which are key in the investigation of exoplanet atmospheres: water, methane, carbon monoxide and titanium oxide. These molecules are chosen as examples of different spectral ranges (IR and UV), molecular types (diatomics and polyatomics) and spectral types (electronic and ro-vibrational); the importance of different vibrational bands in forming distinct non-LTE spectral features are investigated. Most notably, such key spectral signatures for distinguishing between the LTE and non-LTE cases include: for CH4 the 3.15 $\mu$m band region; for H2O the 2.0 $\mu$m and 2.7 $\mu$m band regions; for TiO, a strong variation in intensity in the bands between 0.5 and 0.75 $\mu$m; and a sole CO signature between 5 and 6 $\mu$m. The analysis is based on the ExoMol cross sections and takes advantage of the extensive vibrational assignment of these molecular line lists in the ExoMol database. We examine LTE and non-LTE cross sections under conditions consistent with those on WASP-12b and WASP-76b using the empirically motivated bi-temperature Treanor model. In addition, we make a simplistic forward model simulation of transmission spectra for H2O in the atmosphere of WASP-12b using the TauREx 3 atmospheric modelling code

Blind extraction of an exoplanetary spectrum through Independent Component Analysis

Blind-source separation techniques are used to extract the transmission spectrum of the hot-Jupiter HD189733b recorded by the Hubble/NICMOS instrument. Such a 'blind' analysis of the data is based on the concept of independent component analysis. The de-trending of Hubble/NICMOS data using the sole assumption that nongaussian systematic noise is statistically independent from the desired light-curve signals is presented. By not assuming any prior, nor auxiliary information but the data themselves, it is shown that spectroscopic errors only about 10 - 30% larger than parametric methods can be obtained for 11 spectral bins with bin sizes of ~0.09 microns. This represents a reasonable trade-off between a higher degree of objectivity for the non-parametric methods and smaller standard errors for the parametric de-trending. Results are discussed in the light of previous analyses published in the literature. The fact that three very different analysis techniques yield comparable spectra is a strong indication of the stability of these results.Comment: ApJ accepte

Non-Local thermal equilibrium spectra of atmospheric molecules for exoplanets

Here we present a study of non-LTE effects on the exoplanetary spectra of a collection of molecules which are key in the investigation of exoplanet atmospheres: water, methane, carbon monoxide and titanium oxide. These molecules are chosen as examples of different spectral ranges (IR and UV), molecular types (diatomics and polyatomics) and spectral types (electronic and ro-vibrational); the importance of different vibrational bands in forming distinct non-LTE spectral features are investigated. Most notably, such key spectral signatures for distinguishing between the LTE and non-LTE cases include: for CH4 the 3.15 $\mu$m band region; for H2O the 2.0 $\mu$m and 2.7 $\mu$m band regions; for TiO, a strong variation in intensity in the bands between 0.5 and 0.75 $\mu$m; and a sole CO signature between 5 and 6 $\mu$m. The analysis is based on the ExoMol cross sections and takes advantage of the extensive vibrational assignment of these molecular line lists in the ExoMol database. We examine LTE and non-LTE cross sections under conditions consistent with those on WASP-12b and WASP-76b using the empirically motivated bi-temperature Treanor model. In addition, we make a simplistic forward model simulation of transmission spectra for H2O in the atmosphere of WASP-12b using the TauREx 3 atmospheric modelling code.Comment: Accepted for publication in MNRA

PyLightcurve-torch: a transit modelling package for deep learning applications in PyTorch

We present a new open source python package, based on PyLightcurve and PyTorch, tailored for efficient computation and automatic differentiation of exoplanetary transits. The classes and functions implemented are fully vectorised, natively GPU-compatible and differentiable with respect to the stellar and planetary parameters. This makes PyLightcurve-torch suitable for traditional forward computation of transits, but also extends the range of possible applications with inference and optimisation algorithms requiring access to the gradients of the physical model. This endeavour is aimed at fostering the use of deep learning in exoplanets research, motivated by an ever increasing amount of stellar light curves data and various incentives for the improvement of detection and characterisation techniques.Comment: 7 pages, 3 figures; submission status updated, fig 2 caption adde

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