The ARCiS framework for exoplanet atmospheres
The ARCiS framework for exoplanet atmospheres
复制标题
系外行星大气层的 ARciS 框架
DOI：
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Y. Kawashima
中科院分区：
文献类型：
--
作者：
M. Min;C. Ormel;K. Chubb;Ch. Helling;Y. Kawashima
目的：ARCiS，一个新的程序，用于分析系外行星的传输和发射光谱。建模框架的目的是提供一个能够将观测与系外行星大气物理模型联系起来的工具。研究方法：本文选择的建模理念是使用物理和化学模型来约束某些参数，同时保持自由的部分，我们的物理理解仍然是有限的。这种方法介于完全物理建模和完全参数化之间，允许我们使用我们非常了解的过程，并将那些不太了解的过程参数化。贝叶斯检索框架的实施和应用到一组10个热的红外线的过境光谱。该代码包含化学和云的形成，并有自我一致的温度结构计算的选项。结果如下：所提出的代码是快速和灵活的，足以用于检索和目标列表模拟，例如JWST或欧空局Ariel任务。我们提出的结果，使用物理检索框架的元素丰度比的检索，并比较这使用参数化检索设置获得的结果。结论：我们的结论是，对于大多数考虑的目标，目前的数据集没有足够的约束，以可靠地确定元素丰度比。我们发现不同的物理参数之间没有显着的相关性。我们确认，在我们的样本中，行星的光学透射光谱具有很强的斜率，是我们发现云形成最活跃的行星。最后，我们得出结论，与ARCiS，我们有一个计算效率高的工具来分析系外行星观测的物理和化学模型的背景下。
Aims: ARCiS, a novel code for the analysis of exoplanet transmission and emission spectra is presented. The aim of the modelling framework is to provide a tool able to link observations to physical models of exoplanet atmospheres. Methods: The modelling philosophy chosen in this paper is to use physical and chemical models to constrain certain parameters while keeping free the parts where our physical understanding is still more limited. This approach, in between full physical modelling and full parameterisation, allows us to use the processes we understand well and parameterise those less understood. A Bayesian retrieval framework is implemented and applied to the transit spectra of a set of 10 hot Jupiters. The code contains chemistry and cloud formation and has the option for self consistent temperature structure computations. Results: The code presented is fast and flexible enough to be used for retrieval and for target list simulations for e.g. JWST or the ESA Ariel missions. We present results for the retrieval of elemental abundance ratios using the physical retrieval framework and compare this to results obtained using a parameterised retrieval setup. Conclusions: We conclude that for most of the targets considered the current dataset is not constraining enough to reliably pin down the elemental abundance ratios. We find no significant correlations between different physical parameters. We confirm that planets in our sample with a strong slope in the optical transmission spectrum are the planets where we find cloud formation to be most active. Finally, we conclude that with ARCiS we have a computationally efficient tool to analyse exoplanet observations in the context of physical and chemical models.
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