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Implementation of Aerosol Mie Scattering in POSEIDON with Application to the hot Jupiter HD 189733 b's Transmission, Emission, and Reflected Light Spectrum (2410.19253v1)

Published 25 Oct 2024 in astro-ph.EP and astro-ph.IM

Abstract: Aerosols are a ubiquitous feature of planetary atmospheres and leave clear spectral imprints in exoplanet spectra. Pre-JWST, exoplanet retrieval frameworks mostly adopted simple parametric approximations. With JWST, we now have access to mid-infrared wavelengths where aerosols have detectable composition-specific resonance features. Here, we implement new features into the open-source atmospheric retrieval code POSEIDON to account for the complex scattering, reflection, and absorption properties of Mie scattering aerosols. We provide an open-source database of these Mie scattering cross sections and optical properties. We also extend the radiative transfer and retrieval functionality in POSEIDON to include multiple scattering reflection and emission spectroscopy. We demonstrate these new retrieval capabilities on archival Hubble and Spitzer transmission and secondary eclipse spectra of the hot Jupiter HD 189733 b. We find that a high-altitude, low-density, thin slab composed of sub-micron particles is necessary to fit HD 189733 b's transmission spectrum, with multiple aerosol species providing a good fit. We additionally retrieve a sub-solar H$_2$O abundance, a sub-solar K abundance, and do not detect CO$_2$. Our joint thermal and reflection retrievals of HD 189733 b's secondary eclipse spectrum, however, finds no evidence of dayside aerosols, a sub-solar dayside H$_2$O abundance, enhanced CO$_2$, and slighty sub-solar alkali abundances. We additionally explore how retrieval model choices, such as cloud parameterization, aerosol species and properties, and thermal structure parameterization affect retrieved atmospheric properties. Upcoming JWST data for hot Jupiters like HD 189733 b will be well suited to enable deeper exploration of aerosol properties, allowing the formulation of a self-consistent, multi-dimensional picture of cloud formation processes.

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