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arXiv 2209.05087astro-ph.SRastro-ph.IM
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GONG资料中太阳暗条振荡的自动检测技术

Automatic detection technique for solar filament oscillations in GONG data

Manuel Luna, Joan-René Merou Mestre, Frédéric Auchère

摘要:

太阳暗条振荡已经被发现了几十年。现在,由于新望远镜的新功能,这些周期性运动被定期观察到。暗条中的振荡显示了暗条结构的关键方面。系统地研究太阳活动周期中的暗条振荡,可以揭示暗条振荡的演化规律。这项工作是一个概念的证明,旨在自动检测和参数化使用H $α$数据从龚网络的望远镜这样的振荡。该技术研究了H $α$数据立方体中每个像素的周期性波动。使用FFT,我们计算功率谱密度(PSD)。我们定义了一个标准来考虑它是否是一个真正的振荡或是否是一个虚假的波动。这包括考虑PSD中的峰值必须大于背景噪声的几倍,置信水平为95%。背景噪声很好地拟合为红色和白色噪声的组合。我们应用的方法已经在文献中报道的几个观察,以确定其可靠性。我们还应用该方法的测试情况下,这是一个数据集,其中的灯丝的振荡是不知道的先验。该方法表明,在细丝中存在PSD高于阈值的区域。所得到的周期与用其他方法得到的值基本一致。在测试情况下,该方法检测几个灯丝中的振荡。我们的结论是,建议的光谱技术是一个强大的工具,自动检测振荡的双折射使用H $α$数据。

英文摘要:

Solar filament oscillations have been known for decades. Now thanks to the new capabilities of the new telescopes, these periodic motions are routinely observed. Oscillations in filaments show key aspects of their structure. A systematic study of filament oscillations over the solar cycle can shed light on the evolution of the prominences. This work is a proof of concept that aims to automatically detect and parameterise such oscillations using H$α$ data from the GONG network of telescopes. The proposed technique studies the periodic fluctuations of every pixel of the H$α$ data cubes. Using the FFT we compute the power spectral density (PSD). We define a criterion to consider whether it is a real oscillation or whether it is a spurious fluctuation. This consists in considering that the peak in the PSD must be greater than several times the background noise with a confidence level of 95\%. The background noise is well fitted to a combination of red and white noise. We applied the method to several observations already reported in the literature to determine its reliability. We also applied the method to a test case, which is a data set in which the oscillations of the filaments were not known a priori. The method shows that there are areas in the filaments with PSD above the threshold value. The periodicities obtained are in general agreement with the values obtained by other methods. In the test case, the method detects oscillations in several filaments. We conclude that the proposed spectral technique is a powerful tool to automatically detect oscillations in prominences using H$α$ data.

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