Exoplanet Research with the Stratospheric Observatory for Infrared Astronomy (SOFIA)

When the Stratospheric Observatory for Infrared Astronomy (SOFIA) was conceived and its first science cases defined, exoplanets had not been detected. Later studies, however, showed that optical and near-infrared photometric and spectrophotometric follow-up observations during planetary transits and eclipses are feasible with SOFIA's instrumentation, in particular with the HIPO-FLITECAM and FPI+ optical and near infrared (NIR) instruments. Additionally, the airborne-based platform SOFIA has a number of unique advantages when compared to other ground- and space-based observatories in this field of research. Here we will outline these theoretical advantages, present some sample science cases and the results of two observations from SOFIA's first five observation cycles -- an observation of the Hot Jupiter HD 189733b with HIPO and an observation of the Super-Earth GJ 1214b with FLIPO and FPI+. Based on these early products available to this science case, we evaluate SOFIA's potential and future perspectives in the field of optical and infrared exoplanet spectrophotometry in the stratosphere.Comment: Invited review chapter, accepted for publication in "Handbook of Exoplanets" edited by H.J. Deeg and J.A. Belmonte, Springer Reference Work

EXONEST: The Bayesian Exoplanetary Explorer

The fields of astronomy and astrophysics are currently engaged in an unprecedented era of discovery as recent missions have revealed thousands of exoplanets orbiting other stars. While the Kepler Space Telescope mission has enabled most of these exoplanets to be detected by identifying transiting events, exoplanets often exhibit additional photometric effects that can be used to improve the characterization of exoplanets. The EXONEST Exoplanetary Explorer is a Bayesian exoplanet inference engine based on nested sampling and originally designed to analyze archived Kepler Space Telescope and CoRoT (Convection Rotation et Transits plan\'etaires) exoplanet mission data. We discuss the EXONEST software package and describe how it accommodates plug-and-play models of exoplanet-associated photometric effects for the purpose of exoplanet detection, characterization and scientific hypothesis testing. The current suite of models allows for both circular and eccentric orbits in conjunction with photometric effects, such as the primary transit and secondary eclipse, reflected light, thermal emissions, ellipsoidal variations, Doppler beaming and superrotation. We discuss our new efforts to expand the capabilities of the software to include more subtle photometric effects involving reflected and refracted light. We discuss the EXONEST inference engine design and introduce our plans to port the current MATLAB-based EXONEST software package over to the next generation Exoplanetary Explorer, which will be a Python-based open source project with the capability to employ third-party plug-and-play models of exoplanet-related photometric effects.Comment: 30 pages, 8 figures, 5 tables. Presented at the 37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2017) in Jarinu/SP Brasi

An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval

Machine learning is now used in many areas of astrophysics, from detecting exoplanets in Kepler transit signals to removing telescope systematics. Recent work demonstrated the potential of using machine learning algorithms for atmospheric retrieval by implementing a random forest to perform retrievals in seconds that are consistent with the traditional, computationally-expensive nested-sampling retrieval method. We expand upon their approach by presenting a new machine learning model, \texttt{plan-net}, based on an ensemble of Bayesian neural networks that yields more accurate inferences than the random forest for the same data set of synthetic transmission spectra. We demonstrate that an ensemble provides greater accuracy and more robust uncertainties than a single model. In addition to being the first to use Bayesian neural networks for atmospheric retrieval, we also introduce a new loss function for Bayesian neural networks that learns correlations between the model outputs. Importantly, we show that designing machine learning models to explicitly incorporate domain-specific knowledge both improves performance and provides additional insight by inferring the covariance of the retrieved atmospheric parameters. We apply \texttt{plan-net} to the Hubble Space Telescope Wide Field Camera 3 transmission spectrum for WASP-12b and retrieve an isothermal temperature and water abundance consistent with the literature. We highlight that our method is flexible and can be expanded to higher-resolution spectra and a larger number of atmospheric parameters

An astrobiological experiment to explore the habitability of tidally locked M-dwarf planets

We present a summary of a three-year academic research proposal drafted during the Sao Paulo Advanced School of Astrobiology (SPASA) to prepare for upcoming observations of tidally locked planets orbiting M-dwarf stars. The primary experimental goal of the suggested research is to expose extremophiles from analogue environments to a modified space simulation chamber reproducing the environmental parameters of a tidally locked planet in the habitable zone of a late-type star. Here we focus on a description of the astronomical analysis used to define the parameters for this climate simulation

Parameterizing pressure-temperature profiles of exoplanet atmospheres with neural networks

Atmospheric retrievals (AR) of exoplanets typically rely on a combination of a Bayesian inference technique and a forward simulator to estimate atmospheric properties from an observed spectrum. A key component in simulating spectra is the pressure-temperature (PT) profile, which describes the thermal structure of the atmosphere. Current AR pipelines commonly use ad hoc fitting functions here that limit the retrieved PT profiles to simple approximations, but still use a relatively large number of parameters. In this work, we introduce a conceptually new, data-driven parameterization scheme for physically consistent PT profiles that does not require explicit assumptions about the functional form of the PT profiles and uses fewer parameters than existing methods. Our approach consists of a latent variable model (based on a neural network) that learns a distribution over functions (PT profiles). Each profile is represented by a low-dimensional vector that can be used to condition a decoder network that maps $P$ to $T$. When training and evaluating our method on two publicly available datasets of self-consistent PT profiles, we find that our method achieves, on average, better fit quality than existing baseline methods, despite using fewer parameters. In an AR based on existing literature, our model (using two parameters) produces a tighter, more accurate posterior for the PT profile than the five-parameter polynomial baseline, while also speeding up the retrieval by more than a factor of three. By providing parametric access to physically consistent PT profiles, and by reducing the number of parameters required to describe a PT profile (thereby reducing computational cost or freeing resources for additional parameters of interest), our method can help improve AR and thus our understanding of exoplanet atmospheres and their habitability.Comment: Accepted for publication in Astronomy & Astrophysic

Rapid classification of TESS planet candidates with convolutional neural networks

Accurately and rapidly classifying exoplanet candidates from transit surveys is a goal of growing importance as the data rates from space-based survey missions increases. This is especially true for NASA's TESS mission which generates thousands of new candidates each month. Here we created the first deep learning model capable of classifying TESS planet candidates. We adapted the neural network model of Ansdell et al. (2018) to TESS data. We then trained and tested this updated model on 4 sectors of high-fidelity, pixel-level simulations data created using the Lilith simulator and processed using the full TESS SPOC pipeline. We find our model performs very well on our simulated data, with 97% average precision and 92% accuracy on planets in the 2-class model. This accuracy is also boosted by another ~4% if planets found at the wrong periods are included. We also performed 3- and 4-class classification of planets, blended & target eclipsing binaries, and non-astrophysical false positives, which have slightly lower average precision and planet accuracies, but are useful for follow-up decisions. When applied to real TESS data, 61% of TCEs coincident with currently published TOIs are recovered as planets, 4% more are suggested to be EBs, and we propose a further 200 TCEs as planet candidates

Simultaneous multicolour optical and near-IR transit photometry of GJ 1214b with SOFIA

Context. The benchmark exoplanet GJ 1214b is one of the best studied transiting planets in the transition zone between rocky Earth-sized planets and gas or ice giants. This class of super-Earth or mini-Neptune planets is unknown in our solar system, yet is one of the most frequently detected classes of exoplanets. Understanding the transition from rocky to gaseous planets is a crucial step in the exploration of extrasolar planetary systems, in particular with regard to the potential habitability of this class of planets. Aims: GJ 1214b has already been studied in detail from various platforms at many different wavelengths. Our airborne observations with the Stratospheric Observatory for Infrared Astronomy (SOFIA) add information in the Paschen-? cont. 1.9 ľm infrared wavelength band, which is not accessible by any other current ground- or space-based instrument due to telluric absorption or limited spectral coverage. Methods: We used FLIPO, the combination of the High-speed Imaging Photometer for Occultations (HIPO) and the First Light Infrared TEst CAMera (FLITECAM) and the Focal Plane Imager (FPI+) on SOFIA to comprehensively analyse the transmission signal of the possible water-world GJ 1214b through photometric observations during transit in three optical and one infrared channels. Results: We present four simultaneous light curves and corresponding transit depths in three optical and one infrared channel, which we compare to previous observations and current synthetic atmospheric models of GJ 1214b. The final precision in transit depth is between 1.5 and 2.5 times the theoretical photon noise limit, not sensitive enough to constrain the theoretical models any better than previous observations. This is the first exoplanet observation with SOFIA that uses its full set of instruments available to exoplanet spectrophotometry. Therefore we use these results to evaluate SOFIA's potential in this field and suggest future improvements. Tables of the lightcurve data are only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/608/A120</A

Thermochemical and Photochemical Kinetics in Cooler Hydrogen-dominated Extrasolar Planets: A Methane-poor GJ436b?

We introduce a thermochemical kinetics and photochemical model. We use high-temperature bidirectional reaction rates for important H, C, O, and N reactions (most importantly for CH_4 to CO interconversion), allowing us to attain thermochemical equilibrium, deep in an atmosphere, purely kinetically. This allows the chemical modeling of an entire atmosphere, from deep-atmosphere thermochemical equilibrium to the photochemically dominated regime. We use our model to explore the atmospheric chemistry of cooler (T_(eff) < 10^3 K) extrasolar giant planets. In particular, we choose to model the nearby hot-Neptune GJ436b, the only planet in this temperature regime for which spectroscopic measurements and estimates of chemical abundances now exist. Recent Spitzer measurements with retrieval have shown that methane is driven strongly out of equilibrium and is deeply depleted on the day side of GJ436b, whereas quenched carbon monoxide is abundant. This is surprising because GJ436b is cooler than many of the heavily irradiated hot Jovians and thermally favorable for CH_4, and thus requires an efficient mechanism for destroying it. We include realistic estimates of ultraviolet flux from the parent dM star GJ436, to bound the direct photolysis and photosensitized depletion of CH_4. While our models indicate fairly rich disequilibrium conditions are likely in cooler exoplanets over a range of planetary metallicities, we are unable to generate the conditions for substantial CH_4 destruction. One possibility is an anomalous source of abundant H atoms between 0.01 and 1 bars (which attack CH_4), but we cannot as yet identify an efficient means to produce these hot atoms