Presentation #102.416 in the session Poster Session.
Transit and eclipse spectroscopy offer insights into the chemical and radiative properties of extrasolar planet atmospheres, but inferring these properties from their spectra requires data-model comparisons. Bayesian retrieval methods are commonly used to derive atmosphere characteristics from models. Free retrievals make minimal assumptions allowing for flexible data-model fits and potentially illuminating unknown physics, but this also creates the possibility of inferring unphysical atmosphere characteristics. In this work, we combine powerful Bayesian parameter estimation tools with novel grids of self-consistent, one-dimensional, radiative-convective equilibrium models. These models are more restrictive than free retrievals, giving them more predictive power, while maintaining the computational speed that increasingly complex models lack. Using this framework, we determine metallicities, carbon-to-oxygen ratios, and heat redistributions for a population of hot Jupiters from their dayside thermal emission spectra. Our sample is derived from a uniform data-reduction analysis of Hubble Space Telescope WFC3 and Spitzer 3.6/4.5 micron observations sourced from a variety of programs. Results are presented in the context of current trend predictions in literature — metallicity and C/O closely relate to planetary formation theories, and heat redistribution has implications for climate. Finally, we present this grid modeling method as a tool for broad trend predictions that inform observations with JWST and ground-based high-resolution instruments.