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Inferring atmospheric properties of exoplanets from observed spectra is key to understanding their formation, evolution, and habitability. Since traditional Bayesian approaches to atmospheric retrieval (e.g., nested sampling) are computationally expensive, a growing number of machine learning (ML) methods such as neural posterior estimation (NPE) have been proposed. We seek to make ML-based atmospheric retrieval (1) more reliable and accurate with verified results, and (2) more flexible with respect to the underlying neural networks and the choice of the assumed noise models. First, we adopt flow matching posterior estimation (FMPE) as a new ML approach to atmospheric retrieval. FMPE maintains many advantages of NPE, but provides greater architectural flexibility and scalability. Second, we use importance sampling (IS) to verify and correct ML results, and to compute an estimate of the Bayesian evidence. Third, we condition our ML models on the assumed noise level of a spectrum (i.e., error bars), thus making them adaptable to different noise models. Both our noise level-conditional FMPE and NPE models perform on par with nested sampling across a range of noise levels when tested on simulated data. FMPE trains about 3 times faster than NPE and yields higher IS efficiencies. IS successfully corrects inaccurate ML results, identifies model failures via low efficiencies, and provides accurate estimates of the Bayesian evidence. FMPE is a powerful alternative to NPE for fast, amortized, and parallelizable atmospheric retrieval. IS can verify results, thus helping to build confidence in ML-based approaches, while also facilitating model comparison via the evidence ratio. Noise level conditioning allows design studies for future instruments to be scaled up, for example, in terms of the range of signal-to-noise ratios., Comment: Accepted for publication in Astronomy & Astrophysics

The NASA Lunar Reconnaissance Orbiter (LRO) has returned petabytes of lunar high spatial resolution surface imagery over the past decade, impractical for humans to fully review manually. Here we develop an automated method using a deep generative visual model that rapidly retrieves scientifically interesting examples of LRO surface imagery representing the first planetary image anomaly detector. We give quantitative experimental evidence that our method preferentially retrieves anomalous samples such as notable geological features and known human landing and spacecraft crash sites. Our method addresses a major capability gap in planetary science and presents a novel way to unlock insights hidden in ever-increasing remote sensing data archives, with numerous applications to other science domains. We publish our code and data along with this paper., Comment: 15 pages, 10 figures - JSTARS, accepted

Atmospheric retrievals (AR) characterize exoplanets by estimating atmospheric parameters from observed light spectra, typically by framing the task as a Bayesian inference problem. However, traditional approaches such as nested sampling are computationally expensive, thus sparking an interest in solutions based on machine learning (ML). In this ongoing work, we first explore flow matching posterior estimation (FMPE) as a new ML-based method for AR and find that, in our case, it is more accurate than neural posterior estimation (NPE), but less accurate than nested sampling. We then combine both FMPE and NPE with importance sampling, in which case both methods outperform nested sampling in terms of accuracy and simulation efficiency. Going forward, our analysis suggests that simulation-based inference with likelihood-based importance sampling provides a framework for accurate and efficient AR that may become a valuable tool not only for the analysis of observational data from existing telescopes, but also for the development of new missions and instruments., Comment: Accepted at the "AI to Accelerate Science and Engineering (AI2ASE)" workshop at AAAI 2024

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 & Astrophysics

Cloud computing offers an opportunity to run compute-resource intensive climate models at scale by parallelising model runs such that datasets useful to the exoplanet community can be produced efficiently. To better understand the statistical distributions and properties of potentially habitable planetary atmospheres we implemented a parallelised climate modelling tool to scan a range of hypothetical atmospheres.Starting with a modern day Earth atmosphere, we iteratively and incrementally simulated a range of atmospheres to infer the landscape of the multi-parameter space, such as the abundances of biological mediated gases (\ce{O2}, \ce{CO2}, \ce{H2O}, \ce{CH4}, \ce{H2}, and \ce{N2}) that would yield `steady state' planetary atmospheres on Earth-like planets around solar-type stars. Our current datasets comprises of \numatmospheres simulated models of exoplanet atmospheres and is available publicly on the NASA Exoplanet Archive. Our scalable approach of analysing atmospheres could also help interpret future observations of planetary atmospheres by providing estimates of atmospheric gas fluxes and temperatures as a function of altitude. Such data could enable high-throughput first-order assessment of the potential habitability of exoplanetary surfaces and sepcan be a learning dataset for machine learning applications in the atmospheric and exoplanet science domain., Comment: 9 pages, 6 figures

The next generation of space-based observatories will characterize the atmospheres of low-mass, temperate exoplanets with the direct-imaging technique. This will be a major step forward in our understanding of exoplanet diversity and the prevalence of potentially habitable conditions beyond the Earth. We compute a list of currently known exoplanets detectable with the mid-infrared Large Interferometer For Exoplanets (LIFE) in thermal emission. We also compute the list of known exoplanets accessible to a notional design of the Habitable Worlds Observatory (HWO), observing in reflected starlight. With a pre-existing method, we processed the NASA Exoplanet Archive and computed orbital realizations for each known exoplanet. We derived their mass, radius, equilibrium temperature, and planet-star angular separation. We used the LIFEsim simulator to compute the integration time ($t_{int}$) required to detect each planet with LIFE. A planet is considered detectable if a broadband signal-to-noise ratio $S/N$=7 is achieved over the spectral range $4-18.5\mu$m in $t_{int}\leq$100 hours. We tested whether the planet is accessible to HWO in reflected starlight based on its notional inner and outer working angles, and minimum planet-to-star contrast. LIFE's reference configuration (four 2-m telescopes with 5% throughput and a nulling baseline between 10-100 m) can detect 212 known planets within 20 pc. Of these, 55 are also accessible to HWO in reflected starlight, offering a unique opportunity for synergies in atmospheric characterization. LIFE can also detect 32 known transiting exoplanets. Furthermore, 38 LIFE-detectable planets orbit in the habitable zone, of which 13 with $M_p<5M_\oplus$ and 8 with $5M_\oplus

Active comets have been detected in several exoplanetary systems, although so far only indirectly, when the dust or gas in the extended coma has transited in front of the stellar disk. The large optical surface and relatively high temperature of an active cometary coma also makes it suitable to study with direct imaging, but the angular separation is generally too small to be reachable with present-day facilities. However, future imaging facilities with the ability to detect terrestrial planets in the habitable zones of nearby systems will also be sensitive to exocomets in such systems. Here we examine several aspects of exocomet imaging, particularly in the context of the Large Interferometer for Exoplanets (LIFE), which is a proposed space mission for infrared imaging and spectroscopy through nulling interferometry. We study what capabilities LIFE would have for acquiring imaging and spectroscopy of exocomets, based on simulations of the LIFE performance as well as statistical properties of exocomets that have recently been deduced from transit surveys. We find that for systems with extreme cometary activities such as beta Pictoris, sufficiently bright comets may be so abundant that they overcrowd the LIFE inner field of view. More nearby and moderately active systems such as epsilon Eridani or Fomalhaut may turn out to be optimal targets. If the exocomets have strong silicate emission features, such as in comet Hale-Bopp, it may become possible to study the mineralogy of individual exocometary bodies. We also discuss the possibility of exocomets as false positives for planets, with recent deep imaging of alpha Centauri as one hypothetical example. Such contaminants could be common, primarily among young debris disk stars, but should be rare among the main sequence population. We discuss strategies to mitigate the risk of any such false positives., Comment: 17 pages, 11 figures, accepted for publication in A&A

Atmospheric retrieval studies are essential to determine the science requirements for future generation missions, such as the Large Interferometer for Exoplanets (LIFE). The use of heterogeneous absorption cross-sections might be the cause of systematic effects in retrievals, which could bias a correct characterization of the atmosphere. In this contribution we quantified the impact of differences in line list provenance, broadening coefficients, and line wing cut-offs in the retrieval of an Earth twin exoplanet orbiting a Sun-like star at 10 pc from the observer, as it would be observed with LIFE. We ran four different retrievals on the same input spectrum, by varying the opacity tables that the Bayesian retrieval framework was allowed to use. We found that the systematics introduced by the opacity tables could bias the correct estimation of the atmospheric pressure at the surface level, as well as an accurate retrieval of the abundance of some species in the atmosphere (such as CO$_2$ and N$_2$O). We argue that differences in the line wing cut-off might be the major source of errors. We highlight the need for more laboratory and modeling efforts, as well as inter-model comparisons of the main radiative transfer models and Bayesian retrieval frameworks. This is especially relevant in the context of LIFE and future generation missions, to identify issues and critical points for the community to jointly work together to prepare for the analysis of the upcoming observations., Comment: 24 pages, 12 figures. Proceedings SPIE Volume 12180, Space Telescopes and Instrumentation 2022: Optical, Infrared, and Millimeter Wave; 121803L (2022)

Technosignatures refer to observational manifestations of technology that could be detected through astronomical means. Most previous searches for technosignatures have focused on searches for radio signals, but many current and future observing facilities could also constrain the prevalence of some non-radio technosignatures. This search could thus benefit from broader participation by the astronomical community, as contributions to technosignature science can also take the form of negative results that provide statistically meaningful quantitative upper limits on the presence of a signal. This paper provides a synthesis of the recommendations of the 2020 TechnoClimes workshop, which was an online event intended to develop a research agenda to prioritize and guide future theoretical and observational studies technosignatures. The paper provides a high-level overview of the use of current and future missions to detect exoplanetary technosignatures at ultraviolet, optical, or infrared wavelengths, which specifically focuses on the detectability of atmospheric technosignatures, artificial surface modifications, optical beacons, space engineering and megastructures, and interstellar flight. This overview does not derive any new quantitative detection limits but is intended to provide additional science justification for the use of current and planned observing facilities as well as to inspire astronomers conducting such observations to consider the relevance of their ongoing observations to technosignature science. This synthesis also identifies possible technology gaps with the ability of current and planned missions to search for technosignatures, which suggests the need to consider technosignature science cases in the design of future mission concepts., Comment: Accepted for publication in Acta Astronautica

An important future goal in exoplanetology is to detect and characterize potentially habitable planets. Using nulling interferometry, LIFE will allow us to constrain the radius and effective temperature of (terrestrial) exoplanets, as well as provide unique information about their atmospheric structure and composition. We explore the potential of LIFE in characterizing emission spectra of Earth at various stages of its evolution. We perform Bayesian retrievals on simulated spectra of 8 different scenarios, which correspond to cloud-free and cloudy spectra of four different epochs of the evolution of the Earth. Assuming a distance of 10 pc and a Sun-like host star, we simulate observations obtained with LIFE using its simulator LIFEsim, considering all major astrophysical noise sources. With the nominal spectral resolution (R=50) and signal-to-noise ratio (assumed to be S/N=10 at 11.2 $\mu$m), we can identify the main spectral features of all the analyzed scenarios (most notably CO$_2$, H$_2$O, O$_3$, CH$_4$). This allows us to distinguish between inhabited and lifeless scenarios. Results suggest that particularly O$_3$ and CH$_4$ yield an improved abundance estimate by doubling the S/N from 10 to 20. We conclude that the baseline requirements for R and S/N are sufficient for LIFE to detect O$_3$ and CH$_4$ in the atmosphere of an Earth-like planet with an abundance of O$_2$ of around 2% in volume mixing ratio. This information is relevant in terms of the LIFE mission planning. We also conclude that cloud-free retrievals of cloudy planets can be used to characterize the atmospheric composition of terrestrial habitable planets, but not the thermal structure of the atmosphere. From the inter-model comparison performed, we deduce that differences in the opacity tables (caused by e.g. a different line wing treatment) may be an important source of systematic errors., Comment: 18 pages (main text, incl. 11 figures) + appendix; submitted to A&A; comments are very welcome! Fifth paper of LIFE telescope series. First: arXiv:2101.07500v4, Second: arXiv:2203.00471, Third: arXiv:2112.02054, Sixth: arXiv:2201.04891

In this work we show that modern data-driven machine learning techniques can be successfully applied on lunar surface remote sensing data to learn, in an unsupervised way, sufficiently good representations of the data distribution to enable lunar technosignature and anomaly detection. In particular we train an unsupervised distribution learning neural network model to find the Apollo 15 landing module in a testing dataset, with no dataset specific model or hyperparameter tuning. Sufficiently good unsupervised data density estimation has the promise of enabling myriad useful downstream tasks, including locating lunar resources for future space flight and colonization, finding new impact craters or lunar surface reshaping, and algorithmically deciding the importance of unlabeled samples to send back from power- and bandwidth-constrained missions. We show in this work that such unsupervised learning can be successfully done in the lunar remote sensing and space science contexts., Comment: Second Workshop on Machine Learning and the Physical Sciences, NeurIPS 2019. Five pages, three figures

Exoplanet science is one of the most thriving fields of modern astrophysics. A major goal is the atmospheric characterization of dozens of small, terrestrial exoplanets in order to search for signatures in their atmospheres that indicate biological activity, assess their ability to provide conditions for life as we know it, and investigate their expected atmospheric diversity. None of the currently adopted projects or missions, from ground or in space, can address these goals. In this White Paper we argue that a large space-based mission designed to detect and investigate thermal emission spectra of terrestrial exoplanets in the MIR wavelength range provides unique scientific potential to address these goals and surpasses the capabilities of other approaches. While NASA might be focusing on large missions that aim to detect terrestrial planets in reflected light, ESA has the opportunity to take leadership and spearhead the development of a large MIR exoplanet mission within the scope of the "Voyage 2050" long-term plan establishing Europe at the forefront of exoplanet science for decades to come. Given the ambitious science goals of such a mission, additional international partners might be interested in participating and contributing to a roadmap that, in the long run, leads to a successful implementation. A new, dedicated development program funded by ESA to help reduce development and implementation cost and further push some of the required key technologies would be a first important step in this direction. Ultimately, a large MIR exoplanet imaging mission will be needed to help answer one of mankind's most fundamental questions: "How unique is our Earth?", Comment: Accepted for publication in special issue of Experimental Astronomy together with other White Papers submitted to ESA in 2019 in the context of the "Voyage 2050" long-term planning of the Science Programme

This paper outlines some of the possible advancements for the technosignatures searches using the new methods currently rapidly developing in computer science, such as machine learning and deep learning. It also showcases a couple of case studies of large research programs where such methods have been already successfully implemented with notable results. We consider that the availability of data from all sky, all the time observations paired with the latest developments in computational capabilities and algorithms currently used in artificial intelligence, including automation, will spur an unprecedented development of the technosignatures search efforts., Comment: Science white paper submitted in response to the the U.S. National Academies of Science, Engineering, and Medicine's call for community input to the Astro2020 Decadal Survey; 7 pages, 1 figure

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 Brasil

We present near infrared high-precision photometry for eight transiting hot Jupiters observed during their predicted secondary eclipses. Our observations were carried out using the staring mode of the WIRCam instrument on the Canada-France-Hawaii Telescope (CFHT). We present the observing strategies and data reduction methods which delivered time series photometry with statistical photometric precisionas low as 0.11%. We performed a Bayesian analysis to model the eclipse parameters and systematics simultaneously. The measured planet-to-star flux ratios allowed us to constrain the thermal emission from the day side of these hot Jupiters, as we derived the planet brightness temperatures. Our results combined with previously observed eclipses reveal an excess in the brightness temperatures relative to the blackbody prediction for the equilibrium temperatures of the planets for a wide range of heat redistribution factors. We find a trend that this excess appears to be larger for planets with lower equilibrium temperatures. This may imply some additional sources of radiation, such as reflected light from the host star and/or thermal emission from residual internal heat from the formation of the planet., Comment: 17 pages, 19 figures, accepted for publication in the Monthly Notices of the Royal Astronomical Society

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 Works

Secondary eclipses and phase curves reveal information about the reflectivity and heat distribution in exoplanet atmospheres. The phase curve is composed of a combination of reflected, and thermally emitted light from the planet, and for circular orbits the phase curve peaks during the secondary eclipse or at an orbital phase of 0.5. Physical mechanisms have been discovered which shift the phase curve maximum of tidally locked close in planets to the right, or left, of the secondary eclipse. These mechanisms include cloud formations, and atmospheric superrotation, both of which serve to shift the thermally bright hot-spot, or highly reflective bright spot, of the atmosphere away from the sub-stellar point. Here we present a methodology for optimizing observing strategies for both secondary eclipses and phase curves with the goal of maximizing the information gained about the planetary atmosphere while minimizing the (assumed) continuous observation time. We show that we can increase the duty cycle of observations aimed at the measurements of phase curve characteristics (Amplitude, Phase offset) by up to $50\%$ for future platforms such as CHEOPS and JWST. We apply this methodology to the test cases of the Spitzer phase curve of 55-Cancri-e, which displays an eastward shift in its phase curve maximum, as well as model-generated observations of an ultra-short period planet observed with CHEOPS., Comment: Accepted at The Astronomical Journal

The James Webb Space Telescope will revolutionize transiting exoplanet atmospheric science due to its capability for continuous, long-duration observations and its larger collecting area, spectral coverage, and spectral resolution compared to existing space-based facilities. However, it is unclear precisely how well JWST will perform and which of its myriad instruments and observing modes will be best suited for transiting exoplanet studies. In this article, we describe a prefatory JWST Early Release Science (ERS) program that focuses on testing specific observing modes to quickly give the community the data and experience it needs to plan more efficient and successful future transiting exoplanet characterization programs. We propose a multi-pronged approach wherein one aspect of the program focuses on observing transits of a single target with all of the recommended observing modes to identify and understand potential systematics, compare transmission spectra at overlapping and neighboring wavelength regions, confirm throughputs, and determine overall performances. In our search for transiting exoplanets that are well suited to achieving these goals, we identify 12 objects (dubbed "community targets") that meet our defined criteria. Currently, the most favorable target is WASP-62b because of its large predicted signal size, relatively bright host star, and location in JWST's continuous viewing zone. Since most of the community targets do not have well-characterized atmospheres, we recommend initiating preparatory observing programs to determine the presence of obscuring clouds/hazes within their atmospheres. Measurable spectroscopic features are needed to establish the optimal resolution and wavelength regions for exoplanet characterization. Other initiatives from our proposed ERS program include testing the instrument brightness limits and performing phase-curve observations.(Abridged), Comment: This is a white paper that originated from an open discussion at the Enabling Transiting Exoplanet Science with JWST workshop held November 16 - 18, 2015 at STScI (http://www.stsci.edu/jwst/science/exoplanets). Accepted for publication in PASP

Planets emit thermal radiation and reflect incident light that they recieve from their host stars. As a planet orbits it's host star the photometric variations associated with these two effects produce very similar phase curves. If observed through only a single bandpass this leads to a degeneracy between certain planetary parameters that hinder the precise characterization of such planets. However, observing the same planet through two different bandpasses gives one much more information about the planet. Here, we develop a Bayesian methodology for combining photometry from both \emph{Kepler} and the Transiting Exoplanet Survey Satellite (TESS). In addition, we demonstrate via simulations that one can disentangle the reflected and thermally emitted light from the atmosphere of a hot-Jupiter as well as more precisely constrain both the geometric albedo and dayside temperature of the planet. This methodology can further be employed using various combinations of photometry from the James Webb Space Telescope (JWST), the Characterizing ExOplanet Satellite (CHEOPS), or the PLATO mission., Comment: Submitted to PASP