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The majority of existing parametric cloud and haze treatments in atmospheric retrievals of transmission spectra serve the goal of marginalizing out their impact so as not to bias inferences on the atmospheric composition or vertical temperature structure. This goal has been deemed acceptable considering current low resolution spectro-photometric observations. Furthermore, the possible limitations of our prescriptions have been shielded by our existing model assumptions. After all, the reliability of our atmospheric inferences is only as good as the model assumptions themselves. In this talk, I will present our latest efforts to generalize atmospheric retrievals of transmission spectra. Part of these efforts involves relaxing the assumption of a H2-rich atmosphere to retrieve the atmospheric composition of any type of exoplanet atmosphere, while considering the presence of inhomogeneous clouds and hazes. In this process, we discovered that previous prescriptions for inhomogeneous clouds and hazes have important limitations in their exploration of parameter space. This significant blind spot may result in biases in retrieved abundances. As a solution, we propose a new generalized parametric treatment that addresses this blind spot. Our new treatment is robust against assumptions of whether the atmosphere is H2 rich or not, improving on previous treatments. The use of these more advanced modelling approaches is essential to reliably extracting atmospheric properties from the data of today and tomorrow.

Cloud Nine Con | Luis Welbanks | Blind spots in treatment of clouds in atmospheric retrievals