Toward Robust Atmospheric Retrieval on Cloudy L Dwarfs: the Impact of Thermal and Abundance Profile Assumptions

Constraining L dwarf properties from their spectra is challenging. Near-infrared (NIR) spectra probe a limited range of pressures, while many species condense within their photospheres. Condensation creates two complexities: gas-phase species “rain out” (decreasing in abundances by many orders of ma...

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Bibliographic Details
Main Authors: Melanie J. Rowland, Caroline V. Morley, Michael R. Line
Format: Article
Language:English
Published: IOP Publishing 2023-01-01
Series:The Astrophysical Journal
Subjects:
Online Access:https://doi.org/10.3847/1538-4357/acbb07
Description
Summary:Constraining L dwarf properties from their spectra is challenging. Near-infrared (NIR) spectra probe a limited range of pressures, while many species condense within their photospheres. Condensation creates two complexities: gas-phase species “rain out” (decreasing in abundances by many orders of magnitude) and clouds form. We designed tests using synthetic data to determine the best approach for retrieving L dwarf spectra, isolating the challenges in the absence of cloud opacity. We conducted atmospheric retrievals on synthetic cloud-free L dwarf spectra derived from the Sonora Bobcat models at SpeX resolution using a variety of thermal and chemical abundance profile parameterizations. For objects hotter than L5 ( T _eff ∼ 1700 K), the limited pressure layers probed in the NIR are mostly convective; parameterized pressure–temperature ( PT ) profiles bias results and free, unsmoothed profiles should be used. Only when many layers both above and below the radiative-convective boundary are probed can parameterized profiles provide accurate results. Furthermore, a nonuniform abundance profile for FeH is needed to accurately retrieve bulk properties of early-to-mid L dwarfs. Nonuniform prescriptions for other gases in NIR retrievals may also be warranted near the L/T transition (CH _4 ) and early Y dwarfs (Na and K). We demonstrate the utility of using realistic, self-consistent models to benchmark retrievals and suggest how they can be used in the future.
ISSN:1538-4357