paper

A Systematic Retrieval Analysis of Secondary Eclipse Spectra I: A Comparison of Atmospheric Retrieval Techniques

arXiv:1304.5561 · doi:10.1088/0004-637X/775/2/137

Abstract

Spectra of exoplanet atmospheres provide us the opportunity to improve our understanding of these objects just as remote sensing in our own solar system has increased our understanding of the solar system bodies. The challenge is to quantitatively determine the range of temperatures and species abundances allowed by the data. This challenge is often difficult given the low information content of most exoplanet spectra which commonly leads to degeneracies in the interpretation. A variety of temperature and abundance retrieval approaches have been applied to exoplanet spectra, but no previous investigations have sought to compare these approaches. In this investigation we compare three different retrieval methods: Optimal Estimation, Differential Evolution Markov Chain Monte Carlo, and Bootstrap Monte Carlo. We call our suite of retrieval algorithms the Caltech Inverse Modeling and Retrieval Algorithms (CHIMERA). We discuss what we can expect in terms of uncertainties in abundances and temperatures given current observations as well as potential future observations and what conclusions can be drawn given those uncertainties. In general we find that the three approaches agree for high quality spectra expected to come from potential future spaceborne missions, but disagree for low quality spectra representative of current observations. We also show that the Gaussian posterior probability distribution assumption made in the Optimal Estimation approach is valid for high quality spectral data. We also discuss the implications of our models for the inferred C to O ratios of exoplanetary atmospheres, which of course are important for understanding formation environments. More specifically we show that in the observational limit of a few photometric points, the retrieved C/O is biased towards values near solar and near one simply due to the assumption of uninformative priors.

27 pages, 13 figures

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