On Atmospheric Retrievals of Exoplanets with Inhomogeneous Terminators [arXiv:2112.09125]

Ein neues Dokument wurde auf arXiv.org gefunden: On Atmospheric Retrievals of Exoplanets with Inhomogeneous Terminators. (32 Seiten, 12 Abbildungen, 3 Tabellen)

Autoren: Luis WelbanksNikku Madhusudhan

The complexity of atmospheric retrieval models is largely data-driven and one-dimensional models have generally been considered adequate with current data quality. However, recent studies have suggested that using 1D models in retrievals can result in anomalously cool terminator temperatures and biased abundance estimates even with existing transmission spectra of hot Jupiters. Motivated by these claims and upcoming high-quality transmission spectra we systematically explore the limitations of 1D models using synthetic and current observations. We use 1D models of varying complexity, both analytic and numerical, to revisit claims of biases when interpreting transmission spectra of hot Jupiters with inhomogeneous terminator compositions. Overall, we find the reported biases to be resulting from specific model assumptions rather than intrinsic limitations of 1D atmospheric models in retrieving current observations of asymmetric terminators. Additionally, we revise atmospheric retrievals of the hot Jupiter WASP-43b (Teq=1440 K) and the ultra-hot Jupiter WASP-103b (Teq=2484 K ) for which previous studies inferred abnormally cool atmospheric temperatures. We retrieve temperatures consistent with expectations. We note, however, that in the limit of extreme terminator inhomogeneities and high data quality some atmospheric inferences may conceivably be biased, although to a lesser extent than previously claimed. To address such cases, we implement a 2D retrieval framework for transmission spectra which allows accurate constraints on average atmospheric properties and provides insights into the spectral ranges where the imprints of atmospheric inhomogeneities are strongest. Our study highlights the need for careful considerations of model assumptions and data quality before attributing biases in retrieved estimates to unaccounted atmospheric inhomogeneities.

https://arxiv.org/abs/2112.09125

Michael Johne

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