Presentation #111.05 in the session “Extrasolar Planets: Atmospheric Models”.
The last decade has witnessed a revolution in our understanding of exoplanets through the procurement of exquisite spectroscopic observations of their atmospheres. Such observations are routinely interpreted by retrieval tools, frameworks in which an atmospheric model is coupled with an optimization algorithm. Atmospheric retrievals of exoplanetary transmission spectra have been able to provide important constraints on various atmospheric properties such as chemical abundances, information about the presence and structure of clouds and hazes, and characteristic temperatures, at the day-night terminator region of the atmosphere. Most transmission spectra to date have been observed for giant exoplanets due to which retrievals typically assume Hydrogen-rich atmospheres. However, recent observations of mini-Neptunes and super-Earths, and the promise of upcoming facilities including JWST, call for a new generation of retrieval frameworks that can robustly address a wide range of atmospheric compositions and related complexities. In this talk, we will present various considerations for next-generation atmospheric retrievals of exoplanetary transmission spectra with current and upcoming facilities like JWST. We investigate the performance of multiple parametric prescriptions for the presence of inhomogeneous clouds and hazes in atmospheres of transiting exoplanets and quantify their impact on the retrieved chemical abundances. Next, we consider the agreement, or lack thereof, between these prescriptions when the assumption of a H-rich atmosphere is relaxed as it may be the case for some mini-Neptunes and super-Earths. Alongside these parametric forms, we include considerations for atmospheric forward models that incorporate Mie-scattering and refraction. Furthermore, we examine the different model degeneracies present in these models using multiple optimization algorithms which have been benchmarked against each other. Lastly, we argue about the importance of exploring existing model assumptions and incorporating these and other critical model advancements to retrieval tools.