Threlfall James, De Moortel Ineke, Conlon Thomas
School of Mathematics and Statistics, Mathematical Institute, University of St Andrews, St Andrews, KY169SS UK.
Sol Phys. 2017;292(11):165. doi: 10.1007/s11207-017-1191-3. Epub 2017 Oct 31.
Remote sensing of coronal and heliospheric periodicities can provide vital insight into the local conditions and dynamics of the solar atmosphere. We seek to trace long (one hour or longer) periodic oscillatory signatures (previously identified above the limb in the corona by, , Telloni in , 138, 2013) from their origin at the solar surface out into the heliosphere. To do this, we combined on-disk measurements taken by the (AIA) onboard the (SDO) and concurrent extreme ultra-violet (EUV) and coronagraph data from one of the (STEREO) spacecraft to study the evolution of two active regions in the vicinity of an equatorial coronal hole over several days in early 2011. Fourier and wavelet analysis of signals were performed. Applying white-noise-based confidence levels to the power spectra associated with detrended intensity time series yields detections of oscillatory signatures with periods from 6 - 13 hours in both AIA and STEREO data. As was found by Telloni (2013), these signatures are aligned with local magnetic structures. However, typical spectral power densities all vary substantially as a function of period, indicating spectra dominated by red (rather than white) noise. Contrary to the white-noise-based results, applying global confidence levels based on a generic background-noise model (allowing a combination of white noise, red noise, and transients following Auchère in , 110, 2016) without detrending the time series uncovers only sporadic, spatially uncorrelated evidence of periodic signatures in either instrument. Automating this method to individual pixels in the STEREO/COR coronagraph field of view is non-trivial. Efforts to identify and implement a more robust automatic background noise model fitting procedure are needed.
对日冕和日球层周期性的遥感可以为太阳大气的局部条件和动力学提供至关重要的见解。我们试图追踪长周期（一小时或更长）振荡特征（此前由Telloni等人于2013年在日冕的边缘上方识别出）从其在太阳表面的起源一直延伸到日球层。为此，我们将太阳动力学观测台（SDO）上搭载的大气成像仪（AIA）的盘面测量数据，与太阳地球关系观测台（STEREO）其中一艘航天器的同步极紫外（EUV）和日冕仪数据相结合，以研究2011年初赤道日冕洞附近两个活跃区域在数天内的演化情况。我们对信号进行了傅里叶和小波分析。将基于白噪声的置信水平应用于与去趋势强度时间序列相关的功率谱，在AIA和STEREO数据中均检测到了周期为6至13小时的振荡特征。正如Telloni等人（2013年）所发现的，这些特征与局部磁结构对齐。然而，典型的光谱功率密度都随周期有很大变化，表明光谱以红噪声（而非白噪声）为主。与基于白噪声的结果相反，在不对时间序列进行去趋势处理的情况下，应用基于通用背景噪声模型（允许白噪声、红噪声和瞬态的组合，参考Auchère等人于2016年的研究）的全局置信水平，在任何一台仪器中都只发现了零星的、空间上不相关的周期性特征证据。将此方法自动化应用于STEREO/COR日冕仪视场中的各个像素并非易事。需要努力识别并实施一种更稳健的自动背景噪声模型拟合程序。
