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

Large Interferometer For Exoplanets (LIFE): VIII. Where is the phosphine? Observing exoplanetary PH3 with a space based MIR nulling interferometer

Phosphine could be a key molecule in the understanding of exotic chemistry happening in (exo)planetary atmospheres. While it has been detected in the Solar System's giant planets, it has not been observed in exoplanets yet. In the exoplanetary context however it has been theorized as a potential biosignature molecule. The goal of our study is to identify which illustrative science cases for PH3 chemistry are observable with a space-based mid-infrared nulling interferometric observatory like the LIFE (Large Interferometer For Exoplanets) concept. We identified a representative set of scenarios for PH3 detections in exoplanetary atmospheres varying over the whole dynamic range of the LIFE mission. We used chemical kinetics and radiative transfer calculations to produce forward models of these informative, prototypical observational cases for LIFEsim, our observation simulator software for LIFE. In a detailed, yet first order approximation it takes a mission like LIFE: (i) about 1h to find phosphine in a warm giant around a G star at 10 pc, (ii) about 10 h in H2 or CO2 dominated temperate super-Earths around M star hosts at 5 pc, (iii) and even in 100h it seems very unlikely that phosphine would be detectable in a Venus-Twin with extreme PH3 concentrations at 5 pc. Phosphine in concentrations previously discussed in the literature is detectable in 2 out of the 3 cases and about an order of magnitude faster than comparable cases with JWST. We show that there is a significant number of objects accessible for these classes of observations. These results will be used to prioritize the parameter range for the next steps with more detailed retrieval simulations. They will also inform timely questions in the early design phase of a mission like LIFE and guide the community by providing easy-to-scale first estimates for a large part of detection space of such a mission.Comment: In press. Accepted for publication in Astrobiology on 02 November 2022. 26 pages, 5 figures and 8 table