Repeatability of Spitzer/IRAC exoplanetary eclipses with Independent Component Analysis

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 $\mu$m, have been taken with Spitzer. In some cases, in the past years, repeated observations and multiple reanalyses of the same datasets 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 IRAC data, obtaining coherent results when applied to repeated transit observations previously debated in the literature. Here we introduce a variant to pixel-ICA through the use of wavelet transform, wavelet pixel-ICA, which extends its applicability to low-S/N cases. We describe the method and discuss the results obtained over twelve eclipses of the exoplanet XO3b observed during the "Warm Spitzer" era in the 4.5 $\mu$m band. The final results will be reported also in Ingalls et al. (in prep.), together with results obtained with other detrending methods, and over ten synthetic eclipses that were analyzed for the "IRAC Data Challenge 2015". Our results are consistent within 1 $\sigma$ with the ones reported in Wong et al. 2014. 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

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

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

Integrating Light Curve and Atmospheric Modeling of Transiting Exoplanets

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

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 recognize 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

Data analysis Pipeline for EChO end-to-end simulations

Atmospheric spectroscopy of extrasolar planets is an intricate business. Atmospheric signatures typically require a photometric precision of $1 \times 10^{-4}$ in flux over several hours. Such precision demands high instrument stability as well as an understanding of stellar variability and an optimal data reduction and removal of systematic noise. In the context of the EChO mission concept, we here discuss the data reduction and analysis pipeline developed for the EChO end-to-end simulator EChOSim. We present and discuss the step by step procedures required in order to obtain the final exoplanetary spectrum from the EChOSim`raw data' using a simulated observation of the secondary eclipse of the hot-Neptune 55 Cnc e

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

Exploring biases of atmospheric retrievals in simulated jwst transmission spectra of hot jupiters

With a scheduled launch in 2018 October, the James Webb Space Telescope (JWST) is expected to revolutionize the field of atmospheric characterization of exoplanets. The broad wavelength coverage and high sensitivity of its instruments will allow us to extract far more information from exoplanet spectra than what has been possible with current observations. In this paper, we investigate whether current retrieval methods will still be valid in the era of JWST, exploring common approximations used when retrieving transmission spectra of hot Jupiters. To assess biases, we use 1D photochemical models to simulate typical hot Jupiter cloud-free atmospheres and generate synthetic observations for a range of carbon-to-oxygen ratios. Then, we retrieve these spectra using TauREx, a Bayesian retrieval tool, using two methodologies: one assuming an isothermal atmosphere, and one assuming a parameterized temperature profile. Both methods assume constant-with-altitude abundances. We found that the isothermal approximation biases the retrieved parameters considerably, overestimating the abundances by about one order of magnitude. The retrieved abundances using the parameterized profile are usually within 1σ of the true state, and we found the retrieved uncertainties to be generally larger compared to the isothermal approximation. Interestingly, we found that by using the parameterized temperature profile we could place tight constraints on the temperature structure. This opens the possibility of characterizing the temperature profile of the terminator region of hot Jupiters. Lastly, we found that assuming a constant-with-altitude mixing ratio profile is a good approximation for most of the atmospheres under study

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

Exploring biases of atmospheric retrievals in simulated jwst transmission spectra of hot jupiters

With a scheduled launch in 2018 October, the James Webb Space Telescope (JWST) is expected to revolutionize the field of atmospheric characterization of exoplanets. The broad wavelength coverage and high sensitivity of its instruments will allow us to extract far more information from exoplanet spectra than what has been possible with current observations. In this paper, we investigate whether current retrieval methods will still be valid in the era of JWST, exploring common approximations used when retrieving transmission spectra of hot Jupiters. To assess biases, we use 1D photochemical models to simulate typical hot Jupiter cloud-free atmospheres and generate synthetic observations for a range of carbon-to-oxygen ratios. Then, we retrieve these spectra using TauREx, a Bayesian retrieval tool, using two methodologies: one assuming an isothermal atmosphere, and one assuming a parameterized temperature profile. Both methods assume constant-with-altitude abundances. We found that the isothermal approximation biases the retrieved parameters considerably, overestimating the abundances by about one order of magnitude. The retrieved abundances using the parameterized profile are usually within 1σ of the true state, and we found the retrieved uncertainties to be generally larger compared to the isothermal approximation. Interestingly, we found that by using the parameterized temperature profile we could place tight constraints on the temperature structure. This opens the possibility of characterizing the temperature profile of the terminator region of hot Jupiters. Lastly, we found that assuming a constant-with-altitude mixing ratio profile is a good approximation for most of the atmospheres under study