Observational Signatures of Waves and Flows in the Solar Corona
Observational Signatures of Waves and Flows in the Solar Corona
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DOI：
10.1007/s11207-014-0610-y
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发表时间：
2015-02
期刊：
影响因子：
2.8
通讯作者：
I. Moortel;P. Antolin;T. Doorsselaere
中科院分区：
文献类型：
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作者：
I. Moortel;P. Antolin;T. Doorsselaere
多年来，在扩展的日冕环状结构中观察到了传播扰动，但对慢(传播)磁声波和/或准周期上升流的解释仍然没有解决。我们使用正演模型来构建与简单的慢磁声波或周期性流动模型相关的观测信号。计算了171条Feix和193条Fexiisspectral谱线的观测特征。尽管流动模型和波浪模型之间有许多不同之处，但我们没有发现任何清晰、稳健的观测特征可以单独使用(即，不依赖于模型之间的比较)。对于波浪模型，平均线宽作为(浅)视线角度的函数变化较快，而对于流动模型，平均线宽幅度与多普勒速度幅度之比较大。所发现的最稳健的观测特征是，多普勒速度的平均值与幅度的比率总是高于流动模型的比率。流动的这一比率大大高于波浪的比率，研究中使用的流动模型在171°Feix和193°Fexiisspectral谱线上完全相同。然而，这些潜在的观测信号需要谨慎对待，因为它们可能依赖于模型。
Propagating perturbations have been observed in extended coronal loop structures for a number of years, but the interpretation in terms of slow (propagating) magneto-acoustic waves and/or as quasi-periodic upflows remains unresolved. We used forward-modelling to construct observational signatures associated with a simple slow magneto-acoustic wave or periodic flow model. Observational signatures were computed for the 171 Å Feixand the 193 Å Fexiispectral lines. Although there are many differences between the flow and wave models, we did not find any clear, robust observational characteristics that can be used in isolation (i.e.that do not rely on a comparison between the models). For the waves model, a relatively rapid change of the average line widths as a function of (shallow) line-of-sight angles was found, whereas the ratio of the line width amplitudes to the Doppler velocity amplitudes is relatively high for the flow model. The most robust observational signature found is that the ratio of the mean to the amplitudes of the Doppler velocity is always higher than one for the flow model. This ratio is substantially higher for flows than for waves, and for the flows model used in the study is exactly the same in the 171 Å Feixand the 193 Å Fexiispectral lines. However, these potential observational signatures need to be treated cautiously because they are likely to be model-dependent.