[en] The characterization of ever smaller and fainter extrasolar planets requires an intricate understanding of one's data and the analysis techniques used. Correcting the raw data at the 10^–4 level of accuracy in flux is one of the central challenges. This can be difficult for instruments that do not feature a calibration plan for such high precision measurements. Here, it is not always obvious how to de-correlate the data using auxiliary information of the instrument and it becomes paramount to know how well one can disentangle instrument systematics from one's data, given nothing but the data themselves. We propose a non-parametric machine learning algorithm, based on the concept of independent component analysis, to de-convolve the systematic noise and all non-Gaussian signals from the desired astrophysical signal. Such a 'blind' signal de-mixing is commonly known as the 'Cocktail Party problem' in signal processing. Given multiple simultaneous observations of the same exoplanetary eclipse, as in the case of spectrophotometry, we show that we can often disentangle systematic noise from the original light-curve signal without the use of any complementary information of the instrument. In this paper, we explore these signal extraction techniques using simulated data and two data sets observed with the Hubble Space Telescope NICMOS instrument. Another important application is the de-correlation of the exoplanetary signal from time-correlated stellar variability. Using data obtained by the Kepler mission we show that the desired signal can be de-convolved from the stellar noise using a single time series spanning several eclipse events. Such non-parametric techniques can provide important confirmations of the existent parametric corrections reported in the literature, and their associated results. Additionally they can substantially improve the precision exoplanetary light-curve analysis in the future.

[en] Independent component analysis (ICA) has recently been shown to be a promising new path in data analysis and de-trending of exoplanetary time series signals. Such approaches do not require or assume any prior or auxiliary knowledge about the data or instrument in order to de-convolve the astrophysical light curve signal from instrument or stellar systematic noise. These methods are often known as 'blind-source separation' (BSS) algorithms. Unfortunately, all BSS methods suffer from an amplitude and sign ambiguity of their de-convolved components, which severely limits these methods in low signal-to-noise (S/N) observations where their scalings cannot be determined otherwise. Here we present a novel approach to calibrate ICA using sparse wavelet calibrators. The Amplitude Calibrated Independent Component Analysis (ACICA) allows for the direct retrieval of the independent components' scalings and the robust de-trending of low S/N data. Such an approach gives us an unique and unprecedented insight in the underlying morphology of a data set, which makes this method a powerful tool for exoplanetary data de-trending and signal diagnostics.

[en] Here, we introduce the RobERt (Robotic Exoplanet Recognition) algorithm for the classification of exoplanetary emission spectra. Spectral retrieval of exoplanetary atmospheres frequently requires the preselection of molecular/atomic opacities to be defined by the user. In the era of open-source, automated, and self-sufficient retrieval algorithms, manual input should be avoided. User dependent input could, in worst-case scenarios, lead to incomplete models and biases in the retrieval. The RobERt algorithm is based on deep-belief neural (DBN) networks trained to accurately recognize molecular signatures for a wide range of planets, atmospheric thermal profiles, and compositions. Reconstructions of the learned features, also referred to as the “dreams” of the network, indicate good convergence and an accurate representation of molecular features in the DBN. Using these deep neural networks, we work toward retrieval algorithms that themselves understand the nature of the observed spectra, are able to learn from current and past data, and make sensible qualitative preselections of atmospheric opacities to be used for the quantitative stage of the retrieval process

[en] The research of effective and reliable detrending methods for Spitzer data is of paramount importance for the characterization of exoplanetary atmospheres. To date, the totality of exoplanetary observations in the mid- and far-infrared, at wavelengths >3 μm, have been taken with Spitzer. In some cases, in past years, repeated observations and multiple reanalyses of the same data sets led to discrepant results, raising questions about the accuracy and reproducibility of such measurements. Morello et al. (2014, 2015) proposed a blind-source separation method based on the Independent Component Analysis of pixel time series (pixel-ICA) to analyze InfraRed Array Camera (IRAC) data, obtaining coherent results when applied to repeated transit observations previously debated in the literature. Here we introduce a variant to the pixel-ICA through the use of wavelet transform, wavelet pixel-ICA, which extends its applicability to low-signal-to-noise-ratio cases. We describe the method and discuss the results obtained over 12 eclipses of the exoplanet XO3b observed during the “Warm Spitzer” era in the 4.5 μm band. The final results are reported, in part, also in Ingalls et al. (2016), together with results obtained with other detrending methods, and over 10 synthetic eclipses that were analyzed for the “IRAC Data Challenge 2015.” Our results are consistent within 1σ with the ones reported in Wong et al. (2014) and with most of the results reported in Ingalls et al. (2016), which appeared on arXiv while this paper was under review. Based on many statistical tests discussed in Ingalls et al. (2016), the wavelet pixel-ICA method performs as well as or better than other state-of-art methods recently developed by other teams to analyze Spitzer/IRAC data, and, in particular, it appears to be the most repeatable and the most reliable, while reaching the photon noise limit, at least for the particular data set analyzed. Another strength of the ICA approach is its highest objectivity, as it does not use prior information about the instrument systematics, making it a promising method to analyze data from other observatories. The self-consistency of individual measurements of eclipse depth and phase curve slope over a span of more than three years proves the stability of Warm Spitzer/IRAC photometry within the error bars, at the level of 1 part in 10"4 in stellar flux

[en] 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.

[en] Spectral retrieval techniques are currently our best tool to interpret the observed exoplanet atmospheric data. Said techniques retrieve the optimal atmospheric components and parameters by identifying the best fit to an observed transmission/emission spectrum. Over the past decade, our understanding of remote worlds in our galaxy has flourished thanks to the use of increasingly sophisticated spectral retrieval techniques and the collective effort of the community working on exoplanet atmospheric models. A new generation of instruments in space and from the ground is expected to deliver higher quality data in the next decade; it is therefore paramount to upgrade current models and improve their reliability, their completeness, and the numerical speed with which they can be run. In this paper, we address the issue of reliability of the results provided by retrieval models in the presence of systematics of unknown origin. More specifically, we demonstrate that if we fit directly individual light curves at different wavelengths (L-retrieval), instead of fitting transit or eclipse depths, as it is currently done (S-retrieval), the said methodology is more sensitive against astrophysical and instrumental noise. This new approach is tested, in particular, when discrepant simulated observations from Hubble Space Telescope/Wide Field Camera 3 and Spitzer/IRAC are combined. We find that while S-retrievals converge to an incorrect solution without any warning, L-retrievals are able to flag potential discrepancies between the data sets.

[en] 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.

[en] Spectroscopy of exoplanetary atmospheres has become a well established method for the characterization of extrasolar planets. We here present a novel inverse retrieval code for exoplanetary atmospheres. $\mathcal{T}$-REx(Tau Retrieval for Exoplanets) is a line-by-line radiative transfer fully Bayesian retrieval framework. $\mathcal{T}$-REx includes the following features:(1) the optimized use of molecular line lists from the ExoMol project; (2) an unbiased atmospheric composition prior selection, through custom built pattern recognition software; (3) the use of two independent algorithms to fully sample the Bayesian likelihood space: nested sampling as well as a more classical Markov Chain Monte Carlo approach; (4) iterative Bayesian parameter and model selection using the full Bayesian Evidence as well as the Savage–Dickey Ratio for nested models; and (5) the ability to fully map very large parameter spaces through optimal code parallelization and scalability to cluster computing. In this publication we outline the $\mathcal{T}$-REx framework and demonstrate, using a theoretical hot-Jupiter transmission spectrum, the parameter retrieval and model selection. We investigate the impact of signal-to-noise ratio and spectral resolution on the retrievability of individual model parameters, both in terms of error bars on the temperature and molecular mixing ratios as well as its effect on the model’s global Bayesian evidence.

[en] $\mathcal{T}$-REx (Tau Retrieval of Exoplanets) is a novel, fully Bayesian atmospheric retrieval code custom built for extrasolar atmospheres. In Waldmann et al., the transmission spectroscopic case was introduced, and here we present the emission spectroscopy spectral retrieval for the $\mathcal{T}$-REx framework. Compared to transmission spectroscopy, the emission case is often significantly more degenerate due to the need to retrieve the full atmospheric temperature–pressure (TP) profile. This is particularly true in the case of current measurements of exoplanetary atmospheres, which are either of low signal-to-noise, low spectral resolution, or both. We present a new way of combining two existing approaches to the modeling of the said TP profile: (1) the parametric profile, where the atmospheric TP structure is analytically approximated by a few model parameters, (2) the layer-by-layer approach, where individual atmospheric layers are modeled. Both of these approaches have distinct advantages and disadvantages in terms of convergence properties and potential model biases. The $\mathcal{T}$-REx hybrid model presented here is a new two-stage TP profile retrieval, which combines the robustness of the analytic solution with the accuracy of the layer-by-layer approach. The retrieval process is demonstrated using simulations of the hot-Jupiter WASP-76b and the hot-super-Earth 55 Cnc e as well as the secondary eclipse measurements of HD 189733b.

[en] In the past decade, the analysis of exoplanet atmospheric spectra has revealed the presence of water vapor in almost all the planets observed, with the exception of a fraction of overcast planets. Indeed, water vapor presents a large absorption signature in the wavelength coverage of the Hubble Space Telescope’s (HST) Wide Field Camera 3 (WFC3), which is the main space-based observatory for atmospheric studies of exoplanets, making its detection very robust. However, while carbon-bearing species such as methane, carbon monoxide, and carbon dioxide are also predicted from current chemical models, their direct detection and abundance characterization has remained a challenge. Here we analyze the transmission spectrum of the puffy, clear hot-Jupiter KELT-11 b from the HST WFC3 camera. We find that the spectrum is consistent with the presence of water vapor and an additional absorption at longer wavelengths than 1.5 μm, which could well be explained by a mix of carbon bearing molecules. CO₂, when included is systematically detected. One of the main difficulties to constrain the abundance of those molecules is their weak signatures across the HST WFC3 wavelength coverage, particularly when compared to those of water. Through a comprehensive retrieval analysis, we attempt to explain the main degeneracies present in this data set and explore some of the recurrent challenges that are occurring in retrieval studies (e.g., the impact of model selection, the use of free versus self-consistent chemistry, and the combination of instrument observations). Our results make this planet an exceptional example of a chemical laboratory to test current physical and chemical models of the atmospheres of hot Jupiters.