The Grace of Teaching

Rev. Himes teaches theology at Boston College. This Convocation Address was delivered on October 21, 1999, when he accepted a Doctor of Humane Letters degree, honoris causa, from Sacred Heart University

Atmospheric Retrieval: Bayesian Methods, Machine Learning, and Application to Exoplanets

Atmospheric retrieval is the inverse modeling method where atmospheric properties are constrained based on measured spectra. Due to the low signal-to-noise ratios of exoplanet observations, exoplanetary retrieval codes pair a radiative transfer (RT) simulator with a Bayesian statistical framework in order to characterize the distribution of atmospheric parameters that could explain the observations (the posterior distribution). This requires on the order of 106 RT model evaluations, which requires hours to days of compute time depending on model complexity. In this work, I investigate atmospheric retrieval methods and apply them to observations of hot Jupiters. Chapter 2 presents a set of RT and retrieval tests to validate the Bayesian Atmospheric Radiative Transfer (BART) retrieval code and applies BART to the emission spectrum of HD 189733 b. Chapter 3 investigates the dayside atmosphere of WASP-12b and resolves a tension in the literature over its composition. Chapter 4 introduces a machine learning direct retrieval framework which spawns virtual machines, generates spectra, trains neural networks, and performs atmospheric retrievals using trained neural networks. Chapter 5 builds on this and presents a machine learning indirect retrieval method, where the retrieval is performed using a neural network surrogate model for RT within a Bayesian framework, and compares it with BART. Chapter 6 utilizes the neural network surrogate modeling approach for thermochemical equilibrium chemistry models and compares it with other equilibrium estimation methods. Appendices address retrieval errors induced by choice of wavenumber gridding for opacity-sampling RT schemes, neural network model selection, the effects of data set size on neural network training, and the accuracy of Bayesian frameworks used for atmospheric retrieval

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.Comment: 22 pages, 14 figures, submitted to PSJ 2022/11/22, revised 2023/3/7, accepted 2023/3/23. Updated to add Zenodo link to Reproducible Research Compendiu

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

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.Comment: 32 pages, 17 figures. Accepted for publication in The Astronomical Journa