De-Biasing Structure Function Estimates From Sparse Time Series of the Solar Wind: A Data-Driven Approach
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稀疏太阳风时间序列结构函数估计的数据驱动去偏
- 研究问题
- 太阳风观测中的缺口如何扭曲结构函数形状及惯性区间斜率，能否通过数据学习的修正减少插值引起的偏差？
- 方法与思路
- 作者在太阳探测器磁场数据中模拟缺口，量化结构函数误差，学习经验修正因子，并在其他太阳风环境及旅行者数据中应用。
- 材料陈述的贡献
- 摘要称线性插值导致系统性低估，经验修正在跨环境数据中具有迁移表现，缺失比例超过百分之二十五时平均误差更小。
- 阅读关注点
- 应核对缺口生成机制、修正因子的训练和验证划分、不同缺失比例的误差表现，以及跨太阳风环境迁移的适用条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未说明修正公式和缺失机制，不能推断其对任意稀疏序列均有效。
原文摘要
Structure functions, which represent the moments of the increments of a stochastic process, are essential complementary statistics to power spectra for analysing the self-similar behaviour of a time series. However, many real-world environmental datasets, such as those collected by spacecraft monitoring the solar wind, contain gaps, which inevitably corrupt the statistics. The nature of this corruption for structure functions remains poorly understood - indeed, often overlooked. Here we simulate gaps in a large set of magnetic field intervals from Parker Solar Probe in order to characterize the behaviour of the structure function of a sparse time series of solar wind turbulence. We quantify the resultant error with regards to the overall shape of the structure function, and its slope in the inertial range. Noting the consistent underestimation of the true curve when using linear interpolation, we demonstrate the ability of an empirical correction factor to de-bias these estimates. This correction, "learnt" from the data from a single spacecraft, is shown to generalize well to data from a solar wind regime elsewhere in the heliosphere, producing smaller errors, on average, for missing fractions >25%. Given this success, we apply the correction to gap-affected Voyager intervals from the inner heliosheath and local interstellar medium, obtaining spectral indices similar to those from previous studies. This work provides a tool for future studies of fragmented solar wind time series, such as those from Voyager, MAVEN, and OMNI, as well as sparsely-sampled astrophysical and geophysical processes more generally.