arXiv subjects

Shawn D. Domagal-Goldman

Publications and source records attributed to Shawn D. Domagal-Goldman.

At least 19 recordsLinked to original sources

arXiv2025

The transmission spectrum of the potentially rocky planet L 98-59 c

We present observations of the 1.35+/-0.07 Earth-radius planet L 98-59 c, collected using Wide Field Camera 3 on the Hubble Space Telescope. L 98-59 is a nearby (10.6 pc), bright (H=7.4 mag), M3V star that harbors three small, transiting planets. As one of the closest known transiting multi-planet systems, L 98-59 offers one of the best opportunities to probe and compare the atmospheres of rocky planets that formed in the same stellar environment. We measured the transmission spectrum of L 98-59 c and the extracted spectrum showed marginal evidence (2.1σ) for wavelength-dependent transit depth variations that could indicate the presence of an atmosphere. We forward-modeled possible atmospheric compositions of the planet based on the transmission spectrum. Although L 98-59 was previously thought to be a fairly quiet star, we have seen evidence for stellar activity, and therefore we assessed a scenario where the source of the signal originates with inhomogeneities on stellar surface. We also see a correlation between transits of L 98-59 c and L 98-59 b collected 12.5 hours apart, which is suggestive (but at <2σ confidence) of a contaminating component from the star impacting the exoplanet spectrum. While intriguing, our results are inconclusive and additional data is needed to verify any atmospheric signal. Fortunately, additional data has been collected from both HST and JWST. Should this result be confirmed with additional data, L 98-59 c would be the first planet smaller than two Earth-radii with a detected atmosphere.

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arXiv2022

A transmission spectrum of the sub-Earth planet L98-59~b in 1.1-1.7 $μ$m

With the increasing number of planets discovered by TESS, the atmospheric characterization of small exoplanets is accelerating. L98-59 is a M-dwarf hosting a multi-planet system, and so far, four small planets have been confirmed. The innermost planet b is $\sim15\%$ smaller and $\sim60\%$ lighter than Earth, and should thus have a predominantly rocky composition. The Hubble Space Telescope observed five primary transits of L98-59b in $1.1-1.7\ μ$m, and here we report the data analysis and the resulting transmission spectrum of the planet. We measure the transit depths for each of the five transits and, by combination, we obtain a transmission spectrum with an overall precision of $\sim20$ ppm in for each of the 18 spectrophotometric channels. With this level of precision, the transmission spectrum does not show significant modulation, and is thus consistent with a planet without any atmosphere or a planet having an atmosphere and high-altitude clouds or haze. The scenarios involving an aerosol-free, H$_2$-dominated atmosphere with H$_2$O or CH$_4$ are inconsistent with the data. The transmission spectrum also disfavors, but does not rules out, an H$_2$O-dominated atmosphere without clouds. A spectral retrieval process suggests that an H$_2$-dominated atmosphere with HCN and clouds or haze may be the preferred solution, but this indication is non-conclusive. Future James Webb Space Telescope observations may find out the nature of the planet among the remaining viable scenarios.

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arXiv2022

The TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI). Part I: Dry Cases -- The fellowship of the GCMs

With the commissioning of powerful, new-generation telescopes such as the James Webb Space Telescope (JWST) and the ground-based Extremely Large Telescopes, the first characterization of a high molecular weight atmosphere around a temperate rocky exoplanet is imminent. Atmospheric simulations and synthetic observables of target exoplanets are essential to prepare and interpret these observations. Here we report the results of the first part of the TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI) project, which compares 3D numerical simulations performed with four state-of-the-art global climate models (ExoCAM, LMD-Generic, ROCKE-3D, Unified Model) for the potentially habitable target TRAPPIST-1e. In this first part, we present the results of dry atmospheric simulations. These simulations serve as a benchmark to test how radiative transfer, subgrid-scale mixing (dry turbulence and convection), and large-scale dynamics impact the climate of TRAPPIST-1e and consequently the transit spectroscopy signature as seen by JWST. To first order, the four models give results in good agreement. The intermodel spread in the global mean surface temperature amounts to 7K (6K) for the N2-dominated (CO2-dominated) atmosphere. The radiative fluxes are also remarkably similar (intermodel variations less than 5%), from the surface (1 bar) up to atmospheric pressures around 5 mbar. Moderate differences between the models appear in the atmospheric circulation pattern (winds) and the (stratospheric) thermal structure. These differences arise between the models from (1) large-scale dynamics, because TRAPPIST-1e lies at the tipping point between two different circulation regimes (fast and Rhines rotators) in which the models can be alternatively trapped, and (2) parameterizations used in the upper atmosphere such as numerical damping.

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arXiv2022

The TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI). Part III: Simulated Observables -- The return of the spectrum

The TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI) is a community project that aims to quantify how dfferences in general circulation models (GCMs) could impact the climate prediction for TRAPPIST-1e and, subsequently its atmospheric characterization in transit. Four GCMs have participated in THAI so far: ExoCAM, LMD-Generic, ROCKE-3D and the UM. This paper, focused on the simulated observations, is the third part of a trilogy, following the analysis of two land planet scenarios (part I) and two aquaplanet scenarios (part II). Here, we show a robust agreement between the simulated spectra and the number of transits estimated to detect the land planet atmospheres. For the aquaplanet ones, using atmospheric data from any of the four GCMs would require at least 17 transits. This prediction corresponds to UM simulated data which produces the lowest and thinnest clouds. Between 35-40% more clouds are predicted by ExoCAM or LMD-G due to higher thick terminator clouds. For the first time this work provides "GCM uncertainty error bars" of 35-40% that need to be considered in future analyses of transmission spectra. We also analyzed the inter-transit variability induced by weather patterns and changes of terminator cloudiness between transits. Its magnitude differs significantly between the GCMs but its impact on the transmission spectra is within the measurement uncertainties. THAI has demonstrated the importance of model intercomparison for exoplanets and also paved the way for a larger project to develop an intercomparison meta-framework, namely the Climates Using Interactive Suites of Intercomparisons Nested for Exoplanet Studies (CUISINES).

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arXiv2022

The TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI). Part II: Moist Cases -- The Two Waterworlds

To identify promising exoplanets for atmospheric characterization and to make the best use of observational data, a thorough understanding of their atmospheres is needed. 3D general circulation models (GCMs) are one of the most comprehensive tools available for this task and will be used to interpret observations of temperate rocky exoplanets. Due to parameterization choices made in GCMs, they can produce different results, even for the same planet. Employing four widely-used exoplanetary GCMs -- ExoCAM, LMD-G, ROCKE-3D and the UM -- we continue the TRAPPIST-1 Habitable Atmosphere Intercomparison by modeling aquaplanet climates of TRAPPIST-1e with a moist atmosphere dominated by either nitrogen or carbon dioxide. Although the GCMs disagree on the details of the simulated regimes, they all predict a temperate climate with neither of the two cases pushed out of the habitable state. Nevertheless, the inter-model spread in the global mean surface temperature is non-negligible: 14 K and 24 K in the nitrogen and carbon dioxide dominated case, respectively. We find substantial inter-model differences in moist variables, with the smallest amount of clouds in LMD-Generic and the largest in ROCKE-3D. ExoCAM predicts the warmest climate for both cases and thus has the highest water vapor content and the largest amount and variability of cloud condensate. The UM tends to produce colder conditions, especially in the nitrogen-dominated case due to a strong negative cloud radiative effect on the day side of TRAPPIST-1e. Our study highlights various biases of GCMs and emphasizes the importance of not relying solely on one model to understand exoplanet climates.

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arXiv2022

The Role of Atmospheric Exchange in False-Positive Biosignature Detection

Saturn's Moon Titan receives volatiles into the top of its atmosphere-including atomic oxygen-sourced from cryovolcanoes on Enceladus. Similar types of atmosphere exchange from one body to another, such as O2 and O3 sourced from TRAPPIST-1 d, could be introduced into the upper atmosphere of TRAPPIST-1 e and might be interpreted as biosignatures. We simulate this potential false-positive for life on TRAPPIST-1 e, by applying an external influx of water and oxygen into the top of the atmosphere using a coupled 1-D photochemical-climate model (Atmos), to predict atmospheric composition. In addition, synthetic spectral observations are produced using the Planetary Spectrum Generator for the James Webb Space Telescope, Origins Space Telescope, Habitable Exoplanet Observatory and Large Ultra-violet/Optical/Infrared Surveyor to test the detectability of abiotic-generated O2 and O3 in the presence of abiotic and biotic surface fluxes of CH4. We determine that the incoming flux of material needed to trigger detection of abiotic O2/O3 by any of these observatories is more than two orders of magnitude (1E12 molecules/cm2/s) above what is physically plausible.

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arXiv2022

Accurate Machine Learning Atmospheric Retrieval via a Neural Network Surrogate Model for Radiative Transfer

Atmospheric retrieval determines the properties of an atmosphere based on its measured spectrum. The low signal-to-noise ratio of exoplanet observations require a Bayesian approach to determine posterior probability distributions of each model parameter, given observed spectra. This inference is computationally expensive, as it requires many executions of a costly radiative transfer (RT) simulation for each set of sampled model parameters. Machine learning (ML) has recently been shown to provide a significant reduction in runtime for retrievals, mainly by training inverse ML models that predict parameter distributions, given observed spectra, albeit with reduced posterior accuracy. Here we present a novel approach to retrieval by training a forward ML surrogate model that predicts spectra given model parameters, providing a fast approximate RT simulation that can be used in a conventional Bayesian retrieval framework without significant loss of accuracy. We demonstrate our method on the emission spectrum of HD 189733 b and find good agreement with a traditional retrieval from the Bayesian Atmospheric Radiative Transfer (BART) code (Bhattacharyya coefficients of 0.9843--0.9972, with a mean of 0.9925, between 1D marginalized posteriors). This accuracy comes while still offering significant speed enhancements over traditional RT, albeit not as much as ML methods with lower posterior accuracy. Our method is ~9x faster per parallel chain than BART when run on an AMD EPYC 7402P central processing unit (CPU). Neural-network computation using an NVIDIA Titan Xp graphics processing unit is 90--180x faster per chain than BART on that CPU.

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arXiv2021

Final Report for SAG 22: A Target Star Archive for Exoplanet Science

Present and upcoming NASA missions will be intensively observing a selected, partially overlapping set of stars for exoplanet studies. Key physical and chemical information about these stars and their systems is needed for planning observations and interpreting the results. A target star archive of such data would benefit a wide cross-section of the exoplanet community by enhancing the chances of mission success and improving the efficiency of mission observatories. It would also provide a common, accessible resource for scientific analysis based on standardized assumptions, while revealing gaps or deficiencies in existing knowledge of stellar properties necessary for exoplanetary system characterization.

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arXiv2021

L 98-59: a Benchmark System of Small Planets for Future Atmospheric Characterization

L 98-59 is an M3V dwarf star that hosts three small (R < 1.6 Earth radii) planets. The host star is bright (K = 7.1) and nearby (10.6 pc), making the system a prime target for follow-up characterization with the Hubble Space Telescope (HST) and the upcoming James Webb Space Telescope (JWST). Herein, we use simulated transmission spectroscopy to evaluate the detectability of spectral features with HST and JWST assuming diverse atmospheric scenarios (e.g., atmospheres dominated by H2, H2O, CO2, or O2). We find that H2O and CH4 present in a low mean-molecular weight atmosphere could be detected with HST in 1 transit for the two outermost planets, while H2O in a clear steam atmosphere could be detected in 6 transits or fewer with HST for all three planets. We predict that observations using JWST/NIRISS would be capable of detecting a clear steam atmosphere in 1 transit for each planet, and H2O absorption in a hazy steam atmosphere in 2 transits or less. In a clear, desiccated atmosphere, O2 absorption may be detectable for all three planets with NIRISS. If the L 98-59 planets possess a clear, Venus-like atmosphere, NIRSpec could detect CO2 within 26 transits for each planet, but the presence of H2SO4 clouds would significantly suppress CO2 absorption. The L 98-59 system is an excellent laboratory for comparative planetary studies of transiting multiplanet systems, and observations of the system via HST and JWST would present a unique opportunity to test the accuracy of the models presented in this study.

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arXiv2021

TRAPPIST Habitable Atmosphere Intercomparison (THAI) workshop report

The era of atmospheric characterization of terrestrial exoplanets is just around the corner. Modeling prior to observations is crucial in order to predict the observational challenges and to prepare for the data interpretation. This paper presents the report of the TRAPPIST Habitable Atmosphere Intercomparison (THAI) workshop (14-16 September 2020). A review of the climate models and parameterizations of the atmospheric processes on terrestrial exoplanets, model advancements and limitations, as well as direction for future model development was discussed. We hope that this report will be used as a roadmap for future numerical simulations of exoplanet atmospheres and maintaining strong connections to the astronomical community.

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arXiv2020

Sensitive Probing of Exoplanetary Oxygen via Mid Infrared Collisional Absorption

The collision-induced fundamental vibration-rotation band at 6.4 um is the most significant absorption feature from O2 in the infrared (Timofeyev and Tonkov, 1978; Rinslandet al., 1982, 1989), yet it has not been previously incorporated into exoplanet spectral analyses for several reasons. Either CIAs were not included or incomplete/obsolete CIA databases were used. Also, the current version of HITRAN does not include CIAs at 6.4 um with other collision partners (O2-X). We include O2-X CIA features in our transmission spectroscopy simulations by parameterizing the 6.4 um O2-N2 CIA based on Rinsland et al.(1989) and the O2-CO2 CIA based on Baranov et al. (2004). Here we report that the O2-X CIA may be the most detectable O2 feature for transit observations. For a potentialTRAPPIST-1e analogue system within 5 pc of the Sun, it could be the only O2 detectable signature with JWST (using MIRI LRS) for a modern Earth-like cloudy atmosphere with biological quantities of O2. Also, we show that the 6.4 um O2-X CIA would be prominent for O2-rich desiccated atmospheres (Luger and Barnes, 2015) and could be detectable with JWST in just a few transits. For systems beyond 5 pc, this feature could therefore be a powerful discriminator of uninhabited planets with non-biological "false positive" O2 in their atmospheres - as they would only be detectable at those higher O2 pressures.

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arXiv2019

Impact of Clouds and Hazes on the Simulated JWST Transmission Spectra of Habitable Zone Planets in the TRAPPIST-1 System

The TRAPPIST-1 system, consisting of an ultra-cool host star having seven known Earth-size planets will be a prime target for atmospheric characterization with JWST. However, the detectability of atmospheric molecular species may be severely impacted by the presence of clouds and/or hazes. In this work, we perform 3-D General Circulation Model (GCM) simulations with the LMD Generic model supplemented by 1-D photochemistry simulations at the terminator with the Atmos model to simulate several possible atmospheres for TRAPPIST-1e, 1f and 1g: 1) modern Earth, 2) Archean Earth, and 3) CO2-rich atmospheres. JWST synthetic transit spectra were computed using the GSFC Planetary Spectrum Generator (PSG). We find that TRAPPIST-1e, 1f and 1g atmospheres, with clouds and/or hazes, could be detected using JWST's NIRSpec prism from the CO2 absorption line at 4.3 um in less than 15 transits at 3 sigma or less than 35 transits at 5 sigma. However, our analysis suggests that other gases would require hundreds (or thousands) of transits to be detectable. We also find that H2O, mostly confined in the lower atmosphere, is very challenging to detect for these planets or similar systems if the planets' atmospheres are not in a moist greenhouse state. This result demonstrates that the use of GCMs, self-consistently taking into account the effect of clouds and sub-saturation, is crucial to evaluate the detectability of atmospheric molecules of interest as well as for interpreting future detections in a more global (and thus robust and relevant) approach.

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arXiv2019

VPLanet: The Virtual Planet Simulator

We describe a software package called VPLanet that simulates fundamental aspects of planetary system evolution over Gyr timescales, with a focus on investigating habitable worlds. In this initial release, eleven physics modules are included that model internal, atmospheric, rotational, orbital, stellar, and galactic processes. Many of these modules can be coupled simultaneously to simulate the evolution of terrestrial planets, gaseous planets, and stars. The code is validated by reproducing a selection of observations and past results. VPLanet is written in C and designed so that the user can choose the physics modules to apply to an individual object at runtime without recompiling, i.e., a single executable can simulate the diverse phenomena that are relevant to a wide range of planetary and stellar systems. This feature is enabled by matrices and vectors of function pointers that are dynamically allocated and populated based on user input. The speed and modularity of VPLanet enables large parameter sweeps and the versatility to add/remove physical phenomena to assess their importance. VPLanet is publicly available from a repository that contains extensive documentation, numerous examples, Python scripts for plotting and data management, and infrastructure for community input and future development.

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arXiv2019

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.

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arXiv2019

The remote detectability of Earth's biosphere through time and the importance of UV capability for characterizing habitable exoplanets

Thousands of planets beyond our solar system have been discovered to date, dozens of which are rocky in composition and are orbiting within the circumstellar habitable zone of their host star. The next frontier in life detection beyond our solar system will be detailed characterization of the atmospheres of potentially habitable worlds, resulting in a pressing need to develop a comprehensive understanding of the factors controlling the emergence and maintenance of atmospheric biosignatures. Understanding Earth system evolution is central to this pursuit, and a refined understanding of Earth's evolution can provide substantive insight into observational and interpretive frameworks in exoplanet science. Using this framework, we argue here that UV observations can help to effectively mitigate 'false positive' scenarios for oxygen-based biosignatures, while 'false negative' scenarios potentially represent a significant problem for biosignature surveys lacking UV capability. Moving forward, we suggest that well-resolved UV observations will be critical for near-term volume-limited surveys of habitable planets orbiting nearby Sun-like stars, and will provide the potential for biosignature detection across the most diverse spectrum of reducing, weakly oxygenated, and oxic habitable terrestrial planets.

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arXiv2019

Stellar Activity Effects on Moist Habitable Terrestrial Atmospheres Around M dwarfs

Transit spectroscopy of terrestrial planets around nearby M dwarfs is a primary goal of space missions in coming decades. 3-D climate modeling has shown that slow-synchronous rotating terrestrial planets may develop thick clouds at the substellar point, increasing the albedo. For M dwarfs with Teff > 3000 K, such planets at the inner habitable zone (IHZ) have been shown to retain moist greenhouse conditions, with enhanced stratospheric water vapor (fH2O > 1E-3) and low Earth-like surface temperatures. However, M dwarfs also possess strong UV activity, which may effectively photolyze stratospheric H2O. Prior modeling efforts have not included the impact of high stellar UV activity on the H2O. Here, we employ a 1-D photochemical model with varied stellar UV, to assess whether H2O destruction driven by high stellar UV would affect its detectability in transmission spectroscopy. Temperature and water vapor profiles are taken from published 3-D climate model simulations for an IHZ Earth-sized planet around a 3300 K M dwarf with an N2-H2O atmosphere; they serve as self-consistent input profiles for the 1-D model. We explore additional chemical complexity within the 1-D model by introducing other species into the atmosphere. We find that as long as the atmosphere is well-mixed up to 1 mbar, UV activity appears to not impact detectability of H2O in the transmission spectrum. The strongest H2O features occur in the JWST MIRI instrument wavelength range and are comparable to the estimated systematic noise floor of ~50 ppm.

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arXiv2018

Bayesian Deep Learning for Exoplanet Atmospheric Retrieval

Over the past decade, the study of extrasolar planets has evolved rapidly from plain detection and identification to comprehensive categorization and characterization of exoplanet systems and their atmospheres. Atmospheric retrieval, the inverse modeling technique used to determine an exoplanetary atmosphere's temperature structure and composition from an observed spectrum, is both time-consuming and compute-intensive, requiring complex algorithms that compare thousands to millions of atmospheric models to the observational data to find the most probable values and associated uncertainties for each model parameter. For rocky, terrestrial planets, the retrieved atmospheric composition can give insight into the surface fluxes of gaseous species necessary to maintain the stability of that atmosphere, which may in turn provide insight into the geological and/or biological processes active on the planet. These atmospheres contain many molecules, some of them biosignatures, spectral fingerprints indicative of biological activity, which will become observable with the next generation of telescopes. Runtimes of traditional retrieval models scale with the number of model parameters, so as more molecular species are considered, runtimes can become prohibitively long. Recent advances in machine learning (ML) and computer vision offer new ways to reduce the time to perform a retrieval by orders of magnitude, given a sufficient data set to train with. Here we present an ML-based retrieval framework called Intelligent exoplaNet Atmospheric RetrievAl (INARA) that consists of a Bayesian deep learning model for retrieval and a data set of 3,000,000 synthetic rocky exoplanetary spectra generated using the NASA Planetary Spectrum Generator. Our work represents the first ML retrieval model for rocky, terrestrial exoplanets and the first synthetic data set of terrestrial spectra generated at this scale.

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arXiv2018

Exoplanet Science Priorities from the Perspective of Internal and Surface Processes for Silicate and Ice Dominated Worlds

The geophysics of extrasolar planets is a scientific topic often regarded as standing largely beyond the reach of near-term observations. This reality in no way diminishes the central role of geophysical phenomena in shaping planetary outcomes, from formation, to thermal and chemical evolution, to numerous issues of surface and near-surface habitability. We emphasize that for a balanced understanding of extrasolar planets, it is important to look beyond the natural biases of current observing tools, and actively seek unique pathways to understand exoplanet interiors as best as possible during the long interim prior to a time when internal components are more directly accessible. Such pathways include but are not limited to: (a) enhanced theoretical and numerical modeling, (b) laboratory research on critical material properties, (c) measurement of geophysical properties by indirect inference from imprints left on atmospheric and orbital properties, and (d) the purpose-driven use of Solar System object exploration expressly for its value in comparative planetology toward exoplanet-analogs. Breaking down barriers that envision local Solar System exploration, including the study of Earth's own deep interior, as separate from and in financial competition with extrasolar planet research, may greatly improve the rate of needed scientific progress for exoplanet geophysics. As the number of known rocky and icy exoplanets grows in the years ahead, we expect demand for expertise in 'exogeoscience' will expand at a commensurately intense pace. We highlight key topics, including: how water oceans below ice shells may dominate the total habitability of our galaxy by volume, how free-floating nomad planets may often attain habitable subsurface oceans supported by radionuclide decay, and how deep interiors may critically interact with atmospheric mass loss via dynamo-driven magnetic fields.

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