arXiv subjects

Michael D. Himes

Publications and source records attributed to Michael D. Himes.

17 recordsLinked to original sources

arXiv2026

Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) IV: Analyzing CO2 Detections in the Near-IR to Determine the Long-Wavelength Cut-off for the Habitable Worlds Observatory Coronagraph

We present our analysis of how the detectability of carbon dioxide (CO2) on an Earth-like planet varies with respect to signal-to-noise ratio (SNR), wavelength, and molecular abundance. Using the Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) methodology, we can inform the optimal long-wavelength cut-off for the future Habitable Worlds Observatory (HWO) coronagraph. We test 25 evenly-spaced 20% bandpasses between 0.8-2.0{\mu}m, and simulate data spanning a range of SNRs and molecular abundance to analyze the relationship between wavelength and detectability for different planetary archetypes. We examine abundance levels from varying Earth epochs and a Venus-like archetype to investigate how detectability would change throughout the evolution of a rocky planet. Here, we present our results on the planetary conditions and technological requirements to strongly detect CO2. In addition, we analyze the degeneracy of CO2 with carbon monoxide (CO), methane (CH4), and water (H2O). We determine that any abundance of CO does not achieve strong detections and that CH4 and H2O play a pivotal role in the ability to detect CO2. We conclude that the optimal long-wavelength cut-off for the Habitable Worlds Observatory coronagraph should be 1.68{\mu}m.

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arXiv2026

Life, Machine Learning, and the Search for Habitability: Predicting Biosignature Fluxes for the Habitable Worlds Observatory

Future direct-imaging flagship missions, such as NASA's Habitable Worlds Observatory (HWO), face critical decisions in prioritizing observations due to extremely stringent time and resource constraints. In this paper, we introduce two advanced machine-learning architectures tailored for predicting biosignature species fluxes from exoplanetary reflected-light spectra: a Bayesian Convolutional Neural Network (BCNN) and our novel model architecture, the Spectral Query Adaptive Transformer (SQuAT). The BCNN robustly quantifies both epistemic and aleatoric uncertainties, offering reliable predictions under diverse observational conditions, whereas SQuAT employs query-driven attention mechanisms to enhance interpretability by explicitly associating spectral features with specific biosignature species. We demonstrate that both models achieve comparably high predictive accuracy on an augmented dataset spanning a wide range of exoplanetary conditions, while highlighting their distinct advantages in uncertainty quantification and spectral interpretability. These capabilities position our methods as promising tools for accelerating target triage, optimizing observation schedules, and maximizing scientific return for upcoming flagship missions such as HWO.

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arXiv2024

Bayesian Analysis for Remote Biosignature Identification on exoEarths (BARBIE) \RNum{3}: Introducing the KEN

We deploy a newly-generated set of geometric albedo spectral grids to examine the detectability of methane (CH4) in the reflected-light spectrum of an Earth-like exoplanet at visible and near-infrared wavelengths with a future exoplanet imaging mission. By quantifying the detectability as a function of signal-to-noise ratio (SNR) and molecular abundance, we can constrain the best methods of detection with the high-contrast space-based coronagraphy slated for the next generation telescopes such as the Habitable Worlds Observatory (HWO). We used 25 bandpasses between 0.8 and 1.5 microns. The abundances range from a modern-Earth level to an Archean-Earth level, driven by abundances found in available literature. We constrain the optimal 20%, 30%, and 40% bandpasses based on the effective SNR of the data, and investigate the impact of spectral confusion between CH4 and H2O on the detectability of each one. We find that a modern-Earth level of CH4 is not detectable, while an Archean Earth level of CH4 would be detectable at all SNRs and bandpass widths. Crucially, we find that CH4 detectability is inversely correlated with H2o abundance, with required SNR increasing as H2O abundance increases, while H2O detectability depends on CH4 abundance and selected observational wavelength, implying that science requirements for the characterization of Earth-like planet atmospheres in the VIS/NIR should consider the abundances of both species in tandem.

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arXiv2024

Modeling Atmospheric Lines By the Exoplanet Community (MALBEC) version 1.0: A CUISINES radiative transfer intercomparison project

Radiative transfer (RT) models are critical in the interpretation of exoplanetary spectra, in simulating exoplanet climates and when designing the specifications of future flagship observatories. However, most models differ in methodologies and input data, which can lead to significantly different spectra. In this paper, we present the experimental protocol of the MALBEC (Modeling Atmospheric Lines By the Exoplanet Community) project. MALBEC is an exoplanet model intercomparison project (exoMIP) that belongs to the CUISINES (Climates Using Interactive Suites of Intercomparisons Nested for Exoplanet Studies) framework which aims to provide the exoplanet community with a large and diverse set of comparison and validation of models. The proposed protocol tests include a large set of initial participating RT models, a broad range of atmospheres (from Hot Jupiters to temperate terrestrials) and several observation geometries, which would allow us to quantify and compare the differences between different RT models used by the exoplanetary community. Two types of tests are proposed: transit spectroscopy and direct imaging modeling, with results from the proposed tests to be published in dedicated follow-up papers. To encourage the community to join this comparison effort and as an example, we present simulation results for one specific transit case (GJ-1214 b), in which we find notable differences in how the various codes handle the discretization of the atmospheres (e.g., sub-layering), the treatment of molecular opacities (e.g., correlated-k, line-by-line) and the default spectroscopic repositories generally used by each model (e.g., HITRAN, HITEMP, ExoMol).

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arXiv2023

Grid-Based Atmospheric Retrievals for Reflected-Light Spectra of Exoplanets using PSGnest

Techniques to retrieve the atmospheric properties of exoplanets via direct observation of their reflected light have often been limited in scope due to computational constraints imposed by the forward-model calculations. We have developed a new set of techniques which significantly decreases the time required to perform a retrieval while maintaining accurate results. We constructed a grid of 1.4 million pre-computed geometric albedo spectra valued at discrete sets of parameter points. Spectra from this grid are used to produce models for a fast and efficient nested sampling routine called PSGnest. Beyond the upfront time to construct a spectral grid, the amount of time to complete a full retrieval using PSGnest is on the order of seconds to minutes using a personal computer. An extensive evaluation of the error induced from interpolating intermediate spectra from the grid indicates that this bias is insignificant compared to other retrieval error sources, with an average coefficient of determination between interpolated and true spectra of 0.998. We apply these new retrieval techniques to help constrain the optimal bandpass centers for retrieving various atmospheric and bulk parameters from a LuvEx-type mission observing several planetary archetypes. We show that spectral observations made using a 20\% bandpass centered at 0.73 microns can be used alongside our new techniques to make detections of $H_2O$ and $O_2$ without the need to increase observing time beyond what is necessary for a signal-to-noise ratio of 10. The methods introduced here will enable robust studies of the capabilities of future observatories to characterize exoplanets.

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arXiv2023

Characterising a World Within the Hot Neptune Desert: Transit Observations of LTT 9779 b with HST WFC3

We present an atmospheric analysis of LTT 9779 b, a rare planet situated in the hot Neptune desert, that has been observed with HST WFC3 G102 and G141. The combined transmission spectrum, which covers 0.8 - 1.6 $\mu$m, shows a gradual increase in transit depth with wavelength. Our preferred atmospheric model shows evidence for H$_{\rm 2}$O, CO$_{\rm 2}$ and FeH with a significance of 3.1 $\sigma$, 2.4 $\sigma$ and 2.1 $\sigma$, respectively. In an attempt to constrain the rate of atmospheric escape for this planet, we search for the 1.083 $\mu$m Helium line in the G102 data but find no evidence of excess absorption that would indicate an escaping atmosphere using this tracer. We refine the orbital ephemerides of LTT 9779 b using our HST data and observations from TESS, searching for evidence of orbital decay or apsidal precession, which is not found. The phase-curve observation of LTT 9779 b with JWST NIRISS should provide deeper insights into the atmosphere of this planet and the expected atmospheric escape might be detected with further observations concentrated on other tracers such as Lyman $\alpha$.

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arXiv2023

Towards 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods

Characterizing exoplanetary atmospheres via Bayesian retrievals requires assuming some chemistry model, such as thermochemical equilibrium or parameterized abundances. The higher-resolution data offered by upcoming telescopes enables more complex chemistry models within retrieval frameworks. Yet, many chemistry codes that model more complex processes like photochemistry and vertical transport are computationally expensive, and directly incorporating them into a 1D retrieval model can result in prohibitively long execution times. Additionally, phase-curve observations with upcoming telescopes motivate 2D and 3D retrieval models, further exacerbating the lengthy runtime for retrieval frameworks with complex chemistry models. Here, we compare thermochemical equilibrium approximation methods based on their speed and accuracy with respect to a Gibbs energy-minimization code. We find that, while all methods offer orders of magnitude reductions in computational cost, neural network surrogate models perform more accurately than the other approaches considered, achieving a median absolute dex error <0.03 for the phase space considered. While our results are based on a 1D chemistry model, our study suggests that higher dimensional chemistry models could be incorporated into retrieval models via this surrogate modeling approach.

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arXiv2022

On the Dayside Atmosphere of WASP-12b

The atmospheric structure of WASP-12b has been hotly contested for years, with disagreements on the presence of a thermal inversion as well as the carbon-to-oxygen ratio, C/O, due to retrieved abundances of H2O, CO2, and other included species such as HCN and C2H2. Previously, these difficult-to-diagnose discrepancies have been attributed to model differences; assumptions in these models were thought to drive retrievals toward different answers. Here, we show that some of these differences are independent of model assumptions and are instead due to subtle differences in the inputs, such as the eclipse depths and line-list databases. We replicate previously published retrievals and find that the retrieved results are data driven and are mostly unaffected by the addition of species such as HCN and C2H2. We also propose a new physically motivated model that takes into consideration the formation of H- via the thermal dissociation of H2O and H2 at the temperatures reached in the dayside atmosphere of WASP-12b, but the data's current resolution does not support its inclusion in the atmospheric model. This study raises the concern that other exoplanet retrievals may be similarly sensitive to slight changes in the input data.

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arXiv2021

An Open-Source Bayesian Atmospheric Radiative Transfer (BART) Code: I. Design, Tests, and Application to Exoplanet HD 189733 b

We present the open-source Bayesian Atmospheric Radiative Transfer (BART) retrieval package, which produces estimates and uncertainties for an atmosphere's thermal profile and chemical abundances from observations. Several BART components are also stand-alone packages, including the parallel Multi-Core Markov chain Monte Carlo (MC3), which implements several Bayesian samplers; a line-by-line radiative-transfer model, transit; a code that calculates Thermochemical Equilibrium Abundances, TEA; and a test suite for verifying radiative-transfer and retrieval codes, BARTTest. The codes are in Python and C. BART and TEA are under a Reproducible Research (RR) license, which requires reviewed-paper authors to publish a compendium of all inputs, codes, and outputs supporting the paper's scientific claims. BART and TEA produce the compendium's content. Otherwise, these codes are under permissive open-source terms, as are MC3 and BARTTest, for any purpose. This paper presents an overview of the code, BARTTest, and an application to eclipse data for exoplanet HD 189733 b. Appendices address RR methodology for accelerating science, a reporting checklist for retrieval papers, the spectral resolution required for synthetic tests, and a derivation of the effective sample size required to estimate any Bayesian posterior distribution to a given precision, which determines how many iterations to run. Paper II, by Cubillos et al., presents the underlying radiative-transfer scheme and an application to transit data for exoplanet HAT-P-11b. Paper III, by Blecic et al., discusses the initialization and post-processing routines, with an application to eclipse data for exoplanet WASP-43b. We invite the community to use and improve BART and its components at http://GitHub.com/ExOSPORTS/BART/.

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arXiv2021

An Open-source Bayesian Atmospheric Radiative Transfer (BART) Code: II. The Transit Radiative-transfer Module and Retrieval of HAT-P-11b

This and companion papers by Harrington et al. and Blecic et al. present the Bayesian Atmospheric Radiative Transfer (BART) code, an open-source, open-development package to characterize extrasolar-planet atmospheres. BART combines a thermochemical equilibrium abundances (TEA), a radiative-transfer (Transit), and a Bayesian statistical (MC3) module to constrain atmospheric temperatures and molecular abundances for given spectroscopic observations. Here, we describe the Transit radiative-transfer package, an efficient line-by-line radiative-transfer C code for one-dimensional atmospheres, developed by P. Rojo and further modified by the UCF exoplanet group. This code produces transmission and hemisphere-integrated emission spectra. Transit handles line-by-line opacities from HITRAN, Partridge \& Schwenke ({\water}), Schwenke (TiO), and Plez (VO); and collision-induced absorption from Borysow, HITRAN, and ExoMol. Transit emission-spectra models agree with models from C. Morley (priv. comm.) within a few percent. We applied BART to the {\Spitzer} and {\Hubble} transit observations of the Neptune-sized planet HAT-P-11b. Our results generally agree with those from previous studies, constraining the {\water} abundance and finding an atmosphere enhanced in heavy elements. Different conclusions start to emerge when we make different assumptions from other studies. The BART source code and documentation are available at https://github.com/exosports/BART.

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arXiv2021

An open-source Bayesian atmospheric radiative transfer (BART) code: III. Initialization, atmospheric profile generator, post-processing routines, and application to exoplanet WASP-43b

This and companion papers by Harrington et al. 2021, submitted and Cubillos et al. 2021, submitted describe an open-source retrieval framework, Bayesian Atmospheric Radiative Transfer (BART), available to the community under the reproducible-research license via https://github.com/exosports/BART . BART is a radiative-transfer code (transit, https://github.com/exosports/transit , Rojo 2009, 2009ASPC..420..321R), initialized by the Thermochemical Equilibrium Abundances (TEA, https://github.com/dzesmin/TEA , Blecic et al. 2016, arXiv:1505.06392) code, and driven through the parameter phase space by a differential-evolution Markov-chain Monte Carlo (MC3, https://github.com/pcubillos/mc3 , Cubillos et al. 2017, arXiv:1610.01336) sampler. In this paper we give a brief description of the framework, and its modules that can be used separately for other scientific purposes; outline the retrieval analysis flow; present the initialization routines, describing in detail the atmospheric profile generator and the temperature and species parameterizations; and specify the post-processing routines and outputs, concentrating on the spectrum band integrator, the best-fit model selection, and the contribution functions. We also present an atmospheric analysis of WASP-43b secondary eclipse data obtained from space- and ground-based observations. We compare our results with the results from the literature, and investigate how the inclusion of additional opacity sources influence the best-fit model.

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arXiv2020

Identification and Mitigation of a Vibrational Telescope Systematic with Application to Spitzer

We observed Proxima Centauri with the Spitzer Space Telescope InfraRed Array Camera (IRAC) five times in 2016 and 2017 to search for transits of Proxima Centauri b. Following standard analysis procedures, we found three asymmetric, transit-like events that are now understood to be vibrational systematics. This systematic is correlated with the width of the point-response function (PRF), which we measure with rotated and non-rotated Gaussian fits with respect to the detecor array. We show that the systematic can be removed with a novel application of an adaptive elliptical-aperture photometry technique, and compare the performance of this technique with fixed and variable circular-aperture photometry, using both BiLinearly Interpolated Subpixel Sensitivity (BLISS) maps and non-binned Pixel-Level Decorrelation (PLD). With BLISS maps, elliptical photometry results in a lower standard deviation of normalized residuals, and reduced or similar correlated noise when compared to circular apertures. PLD prefers variable, circular apertures, but generally results in more correlated noise than BLISS. This vibrational effect is likely present in other telescopes and Spitzer observations, where correction could improve results. Our elliptical apertures can be applied to any photometry observations, and may be even more effective when applied to more circular PRFs than Spitzer's.

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arXiv2020

Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade

Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets. Despite the increasing volume of planetary observations, our field has seen few applications of ML in comparison to other sciences. To support these methods, we propose ten recommendations for bolstering a data-rich future in planetary science.

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arXiv2020

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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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

Proxima Centauri b is not a transiting exoplanet

We report Spitzer Space Telescope observations during predicted transits of the exoplanet Proxima Centauri b. As the nearest terrestrial habitable-zone planet we will ever discover, any potential transit of Proxima b would place strong constraints on its radius, bulk density, and atmosphere. Subsequent transmission spectroscopy and secondary-eclipse measurements could then probe the atmospheric chemistry, physical processes, and orbit, including a search for biosignatures. However, our photometric results rule out planetary transits at the 200~ppm level at 4.5$~{\mu}m$, yielding a 3$\sigma$ upper radius limit of 0.4~$R_\rm{\oplus}$ (Earth radii). Previous claims of possible transits from optical ground- and space-based photometry were likely correlated noise in the data from Proxima Centauri's frequent flaring. Follow-up observations should focus on planetary radio emission, phase curves, and direct imaging. Our study indicates dramatically reduced stellar activity at near-to-mid infrared wavelengths, compared to the optical. Proxima b is an ideal target for space-based infrared telescopes, if their instruments can be configured to handle Proxima's brightness.

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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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