标题： 探索太阳风结构和流的复杂性度量 显示英文标题

标题： Exploring Complexity Measures for Analysis of Solar Wind Structures and Streams

摘要： 在本文中，我们使用统计复杂性和信息论度量来研究太阳风时间序列中的结构。 我们通过熵-复杂性和信息平面进行探索，其中熵的度量是通过排列熵或水平可见图（HVG）的度分布形成的。 然后将熵与Jensen复杂性（Jensen-Shannon复杂性平面）和Fisher信息度量（Fisher-Shannon信息平面）进行比较，这些度量均基于排列和HVG方法形成。 此外，我们通过研究HVG度分布的特性来表征太阳风时间序列。 分析了四种类型的太阳风区间，即高速流、低速流、磁云和鞘区，它们都有不同的起源和行星际特征。 我们的结果表明，总体而言，不同的度量给出相似的结果，但Fisher-Shannon度量由于提供了更局部的复杂性度量，在熵-复杂性平面上导致了更大的值的分散。 所有方法中，磁云区间都表现突出，特别是在分析磁场强度时。 除磁云外，太阳风类型的差异通常在较大的时间滞后下更为明显，这表明小尺度波动具有普遍性。 太阳风时间序列内的波动通常被发现是随机的，这与之前的研究一致。 在太阳风时间序列分析中使用信息论工具有助于识别结构，并提供关于其起源和形成机制的见解。 显示英文摘要

摘要： In this paper we use statistical complexity and information theory metrics to study structure within solar wind time series. We explore this using entropy-complexity and information planes, where the measure for entropy is formed using either permutation entropy or the degree distribution of a horizontal visibility graph (HVG). The entropy is then compared to the Jensen complexity (Jensen-Shannon complexity plane) and Fisher information measure (Fisher-Shannon information plane), formed both from permutations and the HVG approach. Additionally we characterise the solar wind time series by studying the properties of the HVG degree distribution. Four types of solar wind intervals have been analysed, namely fast streams, slow streams, magnetic clouds and sheath regions, all of which have distinct origins and interplanetary characteristics. Our results show that, overall, different metrics give similar results but Fisher-Shannon, which gives a more local measure of complexity, leads to a larger spread of values in the entropy-complexity plane. Magnetic cloud intervals stood out in all approaches, in particular when analysing the magnetic field magnitude. Differences between solar wind types (except for magnetic clouds) were typically more distinct for larger time lags, suggesting universality in fluctuations for small scales. The fluctuations within the solar wind time series were generally found to be stochastic, in agreement with previous studies. The use of information theory tools in the analysis of solar wind time series can help to identify structures and provide insight into their origin and formation.