色球漩涡I. H $α$观测值的自动检测及其统计性质
Chromospheric swirls I. Automated detection in H$α$ observations and their statistical properties
摘要：
色球漩涡被认为在太阳上层大气的动力学和加热中起着重要作用。在色球观测中，对它们进行自动检测和跟踪，并确定它们的性质是非常重要的。我们采用了最近开发的自动色球漩涡检测方法的时间序列观测的一个安静的区域的太阳色球获得的H $α$ -0.2波长的H $α$谱线的CRISP仪器在瑞典1米太阳望远镜。该算法利用了高对比度色球观测中涡旋事件的形态特征，并导致在时间序列的每个帧中检测这些结构及其随时间的跟踪。我们进行了统计分析，以确定其各种属性，包括生存分析，推导出平均寿命。在任何给定时间，在FOV内检测到平均146个$\pm$ 9漩涡。发现平均表面密度等于$\sim$ 0.08漩涡$ $ Mm $^{-2}$，发生率为$\sim$ 10 $^{-2}$漩涡$ $ Mm $^{-2}$ min $^{-1}$。这些数值远高于以前报道的色球观测值。检测到的漩涡的半径范围在0.5和2.5微米之间，平均值等于1.3 $\pm$ 0.3微米，这略高于以前的报告。寿命范围在1.5 min和33.7 min之间，算术平均值为$\sim$ 8.5 min。然而，使用Kaplan-Meier估计值结合参数模型对寿命进行生存分析，得出平均寿命为10.3 $\pm$ 0.6 min。自动化方法比目视检查更能揭示其丰度，而更高的节奏，更高分辨率的观测将很可能导致在更小的尺度上探测到更多的这种特征，其寿命也更短。
英文摘要：
Chromospheric swirls are considered to play a significant role in the dynamics and heating of the upper solar atmosphere. It is important to automatically detect and track them in chromospheric observations and determine their properties. We applied a recently developed automated chromospheric swirl detection method to time-series observations of a quiet region of the solar chromosphere obtained in the H$α$-0.2 Å wavelength of the H$α$ spectral line by the CRISP instrument at the Swedish 1-m Solar Telescope. The algorithm exploits the morphological characteristics of swirling events in high contrast chromospheric observations and results in the detection of these structures in each frame of the time series and their tracking over time. We conducted a statistical analysis to determine their various properties, including a survival analysis for deriving the mean lifetime. A mean number of 146 $\pm$ 9 swirls was detected within the FOV at any given time. The mean surface density is found equal to $\sim$0.08 swirls$ $Mm$^{-2}$ and the occurrence rate is $\sim$10$^{-2}$ swirls$ $Mm$^{-2}$ min$^{-1}$. These values are much higher than those previously reported from chromospheric observations. The radii of the detected swirls range between 0.5 and 2.5 Mm, with a mean value equal to 1.3 $\pm$ 0.3 Mm, which is slightly higher than previous reports. The lifetimes range between 1.5 min and 33.7 min with an arithmetic mean value of $\sim$8.5 min. A survival analysis of the lifetimes, however, using the Kaplan-Meier estimator in combination with a parametric model results in a mean lifetime of 10.3 $\pm$ 0.6 min. An automated method sheds more light on their abundance than visual inspection, while higher cadence, higher resolution observations will most probably result in the detection of a higher number of such features on smaller scales and with shorter lifetimes.
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