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arXiv 2008.05539astro-ph.SR
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振荡现象对Stokes反演结果的影响

On the effect of oscillatory phenomena on Stokes inversion results

P. H. Keys, O. Steiner, G. Vigeesh

摘要:

斯托克斯反演码在反演太阳大气温度和磁场强度等参数方面具有重要意义。然而,这种算法返回可靠值的成功可能受到磁波导内振荡现象的存在的阻碍。返回准确的参数是至关重要的磁流体力学研究和太阳物理学的一般。在这里,我们采用一个模拟具有已知的驱动程序和大气参数的磁通管内传播MHD波。我们反转的斯托克斯配置文件的6301 $\unicode{0xc5}$和6302 $\unicode{0xc5}$线对出现从模拟使用著名的斯托克斯反演响应函数(SIR)代码,看看大气参数是否可以返回典型的空间分辨率在地面观测站。反演返回的合成光谱与原始输入光谱相当,即使在大气中波传播的光谱中引入了不对称性。反演的输出模型在反演线的典型地层高度内的温度、视线磁场和视线速度方面与模拟密切匹配。远离这些高度区域可以看到与模拟的偏差。反演结果是不太准确的过程中的波的通道内的线形成区域。原始波周期可以从反演的大气输出中恢复,经验模式分解在这项任务中的表现优于小波方法。

英文摘要:

Stokes inversion codes are crucial in returning properties of the solar atmosphere, such as temperature and magnetic field strength. However, the success of such algorithms to return reliable values can be hindered by the presence of oscillatory phenomena within magnetic wave guides. Returning accurate parameters is crucial to both magnetohydrodynamics studies and solar physics in general. Here, we employ a simulation featuring propagating MHD waves within a flux tube with a known driver and atmospheric parameters. We invert the Stokes profiles for the 6301 $\unicode{0xc5}$ and 6302 $\unicode{0xc5}$ line pair emergent from the simulations using the well-known Stokes Inversions from Response functions (SIR) code to see if the atmospheric parameters can be returned for typical spatial resolutions at ground-based observatories. The inversions return synthetic spectra comparable to the original input spectra, even with asymmetries introduced in the spectra from wave propagation in the atmosphere. The output models from the inversions match closely to the simulations in temperature, line-of-sight magnetic field and line-of-sight velocity within typical formation heights of the inverted lines. Deviations from the simulations are seen away from these height regions. The inversion results are less accurate during passage of the waves within the line formation region. The original wave period could be recovered from the atmosphere output by the inversions, with empirical mode decomposition performing better than the wavelet approach in this task.

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