An Automated Algorithm for Identifying and Tracking Transverse Waves in Solar Images
An Automated Algorithm for Identifying and Tracking Transverse Waves in Solar Images
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DOI：
10.3847/1538-4357/aa9e4a
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发表时间：
2018-01
期刊：
影响因子：
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通讯作者：
M. Weberg;R. Morton;J. McLaughlin
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文献类型：
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作者：
M. Weberg;R. Morton;J. McLaughlin
最近的仪器表明，太阳大气支持无所不在的横波，这可能在为日冕提供能量方面发挥关键作用。为了了解这些横波的一般特性，需要进行大规模研究。为了帮助实现这一目标，我们提出了一种自动算法，用于识别和跟踪太阳图像中的特征，并提取任何观察到的横向振荡的波特性。我们使用一组合成数据来测试和校准我们的算法，其中包括噪声和旋转效应。结果表明，位移幅度的精度为 1%–2%，波浪周期和速度幅度的精度为 4%–10%。我们还将该算法应用于太阳动力学观测站大气成像组件的数据，并发现与之前的研究有很好的一致性。值得注意的是，我们发现 35%–41% 的观察到的羽流表现出多个波特征，这表明波的叠加或在单个结构内的不同时间观察到的多个独立波包。本文描述的自动化方法显着提高了直接测量太阳大气中横波的速度和质量。该算法开启了以前不切实际的广泛统计研究。
Recent instrumentation has demonstrated that the solar atmosphere supports omnipresent transverse waves, which could play a key role in energizing the solar corona. Large-scale studies are required in order to build up an understanding of the general properties of these transverse waves. To help facilitate this, we present an automated algorithm for identifying and tracking features in solar images and extracting the wave properties of any observed transverse oscillations. We test and calibrate our algorithm using a set of synthetic data, which includes noise and rotational effects. The results indicate an accuracy of 1%–2% for displacement amplitudes and 4%–10% for wave periods and velocity amplitudes. We also apply the algorithm to data from the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory and find good agreement with previous studies. Of note, we find that 35%–41% of the observed plumes exhibit multiple wave signatures, which indicates either the superposition of waves or multiple independent wave packets observed at different times within a single structure. The automated methods described in this paper represent a significant improvement on the speed and quality of direct measurements of transverse waves within the solar atmosphere. This algorithm unlocks a wide range of statistical studies that were previously impractical.