论文标题

比较l-warf样本,探讨了大气假设和数据属性之间的相互作用

A Comparative L-dwarf Sample Exploring the Interplay Between Atmospheric Assumptions and Data Properties

论文作者

Gonzales, Eileen C., Burningham, Ben, Faherty, Jacqueline K., Lewis, Nikole K., Visscher, Channon, Marley, Mark

论文摘要

大气检索的比较可以揭示有关我们数据和建模工具的优势和局限性的有力见解。在本文中,我们检查了5个相似的有效温度(TEFF)或光谱型L矮人的样本,以比较其压力温度(P-T)剖面。此外,我们探讨了对象金属性的影响以及观察值的信号到噪声(SNR)对我们可以检索的参数的影响。我们介绍了第一个大气检索:2MASS J15261405 $+$+$ 2043414,2MASS J05395200 $ - $ 0059019,2MASS J15394189 $ - $ 0520428,和GD 165B增加了少量但增加了L-Dwarfs的较小数量。与SDS的大气检索相比,SDSS J141624.08+134826.7,在宽L+T二进制中的低金属d/sdl7 primary相比,我们发现相似的TEFF源具有相似的P-T曲线,具有金属差异,影响了Phothphere Pphere Pphere Pphere的P-T型之间的金属差异。我们还发现,对于近红外光谱,当SNR为$ \ gtrsim80 $时,我们处于模型不确定性在数据测量不确定性上占主导地位的政权。因此,SNR在检索区分无云和无云模型的能力中不发挥作用,而是可能影响检索到的参数的信心。最后,我们还讨论了如何打破云模型退化以及在检索模型中外部气体的影响。

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 5 similar effective temperature (Teff) or spectral type L dwarfs to compare their pressure-temperature (P-T) profiles. Additionally, we explore the impact of an object's metallicity and the observations' signal-to-noise (SNR) 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 atmospheric retrievals of SDSS J141624.08+134826.7, a low-metallicity d/sdL7 primary in a wide L+T binary, we find similar Teff 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 SNR is $\gtrsim80$ we are in a regime where model uncertainties dominate over data measurement uncertainties. As such, SNR 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.

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