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

Abstract

Exoplanet atmosphere spectroscopy enables us to improve our understanding of exoplanets 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 molecular abundances allowed by the data which is often difficult given the low information content of most exoplanet spectra which commonly leading to degeneracies in the interpretation. A variety of spectral retrieval approaches have been applied to exoplanet spectra, but no previous investigations have sought to compare these approaches. We compare three different retrieval methods: optimal estimation, differential evolution Markov chain Monte Carlo, and bootstrap Monte Carlo on a synthetic water dominated hot-Jupiter. We discuss expectations of uncertainties in abundances and temperatures given current and potential future observations. In general we find that the three approaches agree for high spectral resolution, high signal-to-noise data expected to come from potential future spaceborne missions, but disagree for low resolution, low signal-to-noise spectra representative of current observations. We also compare the results from a parameterized temperature profile versus a full classical Level-by-Level approach and discriminate in which situations each of these approaches is applicable. Furthermore, we discuss the implications of our models for the inferred C to O ratios of exoplanetary atmospheres. 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.

A global analysis ofSpitzerand new HARPS data confirms the loneliness and metal-richness of GJ 436 b

“…23 we compare our Spitzer depths to two models from Moses et al (2013) that were created to better fit the previous Spitzer depths derived by S10. The model labelled "bestfit retrieval" used the Bayesian inversion methods of Line et al (2013Line et al ( , 2014 to find a best-fit spectrum, with free parameters that include the P − T profile and chemical mixing ratios. The "300×-solar" model uses a Line et al P − T profile, but mixing ratios from a detailed non-equilibrium chemistry calculation for an atmosphere enriched in metals by a factor of 300 over solar abundances.…”

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“…The atmospheric model consist of a 1D set of concentric shell layers, in hydrostatic equilibrium, spanning from 10 −8 to 100 bar. For the temperature profile we consider either the three-channel Eddington approximation parameterization (TCEA; Line et al 2013b) or an isothermal profile.…”

“…The retrieval parameterization includes free scale factors for the mixing ratios of H 2 O, NH 3 , CH 4 , and HCN (verticalconstant values) and the mean molecular mass of the atmosphere; either the isothermal temperature or the κ, γ, and β parameters of the TCEA model (see Line et al 2013b); the gray-cloud cross section or the Mie-scattering cloud profile shape, condensate mole fraction, particle-size distribution, and gas number fraction just below the cloud deck. ; and the planetary radius at 0.1 bar.…”

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“…Lee et al (2012) use an iterative optimal estimation procedure. Line et al (2013b) investigate several different approaches to retrieval (Optimal Estimation, Bootstrap Monte Carlo, and Differential Evolution Markov Chain Monte Carlo). Benneke & Seager (2012) use a somewhat different approach where the temperature profile is not retrieved, but is determined by a self consistent model requiring radiative and hydrostatic equilibrium and allowing for convection.…”