Abstract
The Ariel mission, due to launch in 2029, will obtain spectroscopic information for 1000 exoplanets, providing an unprecedented opportunity for comparative exoplanetology. Retrieval codes - parameteric atmospheric models coupled with an inversion algorithm - represent the tool of choice for interpreting Ariel data. Ensuring that reliable and consistent results can be produced by these tools is a critical preparatory step for the mission. Here, we present the results of a retrieval challenge. We use five different exoplanet retrieval codes to analyse the same synthetic datasets, and test a) the ability of each to recover the correct input solution and b) the consistency of the results. We find that generally there is very good agreement between the five codes, and in the majority of cases the correct solutions are recovered. This demonstrates the reproducibility of retrievals for transit spectra of exoplanets, even when codes are not previously benchmarked against each other.
Highlights
In recent years, our knowledge of exoplanet atmospheres has been increasing rapidly
We present a retrieval challenge conducted by the Spectral Retrievals Working Group for the Ariel Science Team
Pyrat Bay considers opacities from the main sources expected for exoplanets at these wavelengths: molecular line transitions from HITRAN or ExoMol [48,49,50], collision-induced absorption from Borysow or HITRAN [51,52,53,54,55], resonant Na and K opacity models [56], Rayleigh scattering for H, He, and H 2 [57, 58], and several cloud models, from a simple gray cloud deck to complex Mie-scattering [59] models in thermal stability [60] or microphysical parameterization (Blecic et al, in prep.)
Summary
Our knowledge of exoplanet atmospheres has been increasing rapidly. Recent highlights have included the detection of hazes/clouds in most exoplanets [1,2,3,4], the presence of ionised metals in the atmosphere of an ultrahot Jupiter [5], and the discovery of water vapour in the atmosphere of a small, temperate planet [6, 7]. Retrieval models incorporate a simplified, parameterised radiative transfer model, usually one-dimensional, and an algorithm to explore the parameter space and recover the model solution that provides the best fit to the data. These models involve minimal physical assumptions, instead allowing the atmospheric parameters to vary freely; as a consequence, this technique is a data-driven approach to spectral analysis.
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