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Observations, Thermochemical Calculations, and Modeling of Exoplanetary Atmospheres
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This dissertation as a whole aims to provide means to better understand hot-Jupiter planets through observing, performing thermochemical calculations, and modeling their atmospheres. We used Spitzer multi-wavelength secondary-eclipse observations and targets with high signal-to-noise ratios, as their deep eclipses allow us to detect signatures of spectral features and assess planetary atmospheric structure and composition with greater certainty. Chapter 1 gives a short introduction. Chapter 2 presents the Spitzer secondary-eclipse analysis and atmospheric characterization of WASP-14b. WASP-14b is a highly irradiated, transiting hot Jupiter. By applying a Bayesian approach in the atmospheric analysis, we found an absence of thermal inversion contrary to theoretical predictions. Chapter 3 describes the infrared observations of WASP-43b Spitzer secondary eclipses, data analysis, and atmospheric characterization. WASP-43b is one of the closest-orbiting hot Jupiters, orbiting one of the coolest stars with a hot Jupiter. The atmospheric analysis ruled out a strong thermal inversion in its dayside atmosphere. Chapter 4 presents an open-source Thermochemical Equilibrium Abundances (TEA) code and its application to several hot Jupiters. TEA calculates the abundances of gaseous species using the Gibbs free-energy minimization method within an iterative Lagrangian optimization scheme. The code is written in Python and available to the community via http://github.com/dzesmin/TEA. Chapter 5 presents my contributions to an open-source Bayesian Atmospheric Radiative Transfer (BART) code, and its application to WASP-43b. BART characterizes planetary atmospheres based on the observed spectroscopic information. It initializes a planetary atmospheric model, performs radiative-transfer calculations to produce models of planetary spectra, and using a statistical module compares models with observations.
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Estimating dayside effective temperatures of hot Jupiters and associated uncertainties through Gaussian process regression
Gaussian process regression gives better-calibrated uncertainties for hot Jupiter dayside temperatures than error-weighted averaging or linear interpolation, and produces a twelve-planet catalogue with credible error bars.
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