论文标题

部分可观测时空混沌系统的无模型预测

Can 1D radiative equilibrium models of faculae be used for calculating contamination of transmission spectra?

论文作者

Witzke, Veronika, Shapiro, Alexander I., Kostogryz, Nadiia M., Cameron, Robert, Rackham, Benjamin V., Seager, Sara, Solanki, Sami K., Unruh, Yvonne C.

论文摘要

储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。

The reliable characterization of planetary atmospheres with transmission spectroscopy requires realistic modeling of stellar magnetic features, since features that are attributable to an exoplanet atmosphere could instead stem from the host star's magnetic activity. Current retrieval algorithms for analysing transmission spectra rely on intensity contrasts of magnetic features from 1D radiative-convective models. However, magnetic features, especially faculae, are not fully captured by such simplified models. Here we investigate how well such 1D models can reproduce 3D facular contrasts, taking a G2V star as an example. We employ the well established radiative magnetohydrodynamic code MURaM to obtain three-dimensional simulations of the magneto-convection and photosphere harboring a local small-scale-dynamo. Simulations without additional vertical magnetic fields are taken to describe the quiet solar regions, while simulations with initially 100 G, 200 G and 300 G vertical magnetic fields are used to represent different magnetic activity levels. Subsequently, the spectra emergent from the MURaM cubes are calculated with the MPS-ATLAS radiative transfer code. We find that the wavelength dependence of facular contrast from 1D radiative-convective models cannot reproduce facular contrasts obtained from 3D modeling. This has far reaching consequences for exoplanet characterization using transmission spectroscopy, where accurate knowledge of the host star is essential for unbiased inferences of the planetary atmospheric properties.

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