A Comparative L-dwarf Sample Exploring the Interplay between Atmospheric Assumptions and Data Properties
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Department of Planetary Sciences, University of ArizonaLunar and Planetary Laboratory, University of Arizona
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2022
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Gonzales, E. C., Burningham, B., Faherty, J. K., Lewis, N. K., Visscher, C., & Marley, M. (2022). A Comparative L-dwarf Sample Exploring the Interplay between Atmospheric Assumptions and Data Properties. Astrophysical Journal, 938(1).Journal
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Copyright © 2022. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Comparisons of atmospheric retrievals can reveal powerful insights on the strengths and limitations of our data and modeling tools. In this paper, we examine a sample of five L dwarfs of similar effective temperature (T eff) or spectral type to compare their pressure-temperature (P-T) profiles. Additionally, we explore the impact of an object’s metallicity and the signal-to-noise ratio (S/N) of the observations on the parameters we can retrieve. We present the first atmospheric retrievals: 2MASS J15261405+2043414, 2MASS J05395200−0059019, 2MASS J15394189−0520428, and GD 165B increasing the small but growing number of L dwarfs retrieved. When compared to the atmospheric retrievals of SDSS J141624.08+134826.7, a low-metallicity d/sdL7 primary in a wide L+T binary, we find that similar T eff sources have similar P-T profiles with metallicity differences impacting the relative offset between their P-T profiles in the photosphere. We also find that for near-infrared spectra, when the S/N is ≳80 we are in a regime where model uncertainties dominate over data measurement uncertainties. As such, S/N does not play a role in the retrieval’s ability to distinguish between a cloud-free and cloudless model, but may impact the confidence of the retrieved parameters. Lastly, we also discuss how to break cloud model degeneracies and the impact of extraneous gases in a retrieval model. © 2022. The Author(s). Published by the American Astronomical Society.Note
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0004-637XVersion
Final published versionae974a485f413a2113503eed53cd6c53
10.3847/1538-4357/ac8f2a
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Except where otherwise noted, this item's license is described as Copyright © 2022. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence.