Ultraviolet solar flare signatures in the framework of complex network

Abstract

Studying natural phenomena via the complex network approach makes it possible to quantify the time-evolving structures with too many elements and achieve a deeper understanding of interactions among the components of a system. In this sense, solar flare as a complex system with the chaotic behavior could be better characterized by the network parameters. Here, we employed an unsupervised network-based method to recognize the position and occurrence time of the solar flares by using the ultraviolet emission (1600 Å) recorded by the Atmospheric Imaging Assembly on board Solar Dynamics Observatory. Three different regions, the flaring active regions, the non-flaring active regions, and the quiet-Sun regions, were considered to study the variations of the network parameters in the presence and absence of flaring phases in various datasets over time intervals of several hours. The whole parts of the selected datasets were partitioned into sub-windows to construct networks based on computing the Pearson correlation between time series of the region of interest and intensities. Analyzing the network parameters such as the clustering coefficient, degree centrality, characteristic length, and PageRank verified that flare triggering has an influence on the network parameters around the flare occurrence time and close to the location of flaring. It was found that the values of the clustering coefficient and characteristic length approach those obtained for the corresponding random network in the flaring phase. These findings could be used for detecting the occurrence times and locations of the region at ultraviolet images.

Evaluate Human Perception of the Built Environment in the Metro Station Area

“…Pearson correlation coefficient is a common and robust choice, especially when the data can be deemed close to Gaussian (Hlinka et al, 2017 [76]). According to the method in reference [77,78], this paper uses Pearson correlation coefficients to measure the association of metro station area built environment indicators with sentiment intensity, polarity, and types, the Pearson correlation coefficient r is defined as…”

Complex network view for $δ$ Scuti stars

“…Then show emergent collective characteristics of the system. Natural visibility graph (NVG) and horizontal visibility graph (HVG) are among the several algorithms for building a network for a time series to study the system's characteristics (e.g., Newman, 2003;Luque et al, 2009;Newman, 2010; 2017; Daei et al, 2017;Najafi et al, 2020;Lotfi et al, 2020;Mohammadi et al, 2021;Taran et al, 2022). NVG and HVG are algorithms that focus on the interactions of system elements.…”

“…They inferred the scale invariances of light curve of Sct stars may be due to the turbulent. The complex system methods have been widely applied for distinguishing individual and collective features in many scientific fields, such as economics (Souma et al, 2003), biology (Barabasi and Oltvai, 2004), earthquakes (Baiesi and Paczuski, 2004;Pastén et al, 2018;Vogel et al, 2020), and Solar physics (Daei et al, 2017;Gheibi et al, 2017;Lotfi et al, 2020;Mohammadi et al, 2021). The complex network approach could classify features with the same characteristics (e.g., the degree distribution, average clustering coefficient, transitivity, and PageRank) and quantify the complexities of dynamic systems.…”

Evidence of Self-Organised Criticality in Time Series by the Horizontal Visibility Graph Approach

“…The problem becomes more intricate when there isn't a comprehensive description of the subject system or the phenomenon encompasses a wide range of fields. Complex systems seem to confront with both, because on the one hand there is no all-inclusive interpretation of these systems [2][3][4] , and on the other hand they include everything from brain structure to insect colonies, price fluctuations in financial markets, condensate matter, Internet, Plasma and Solar physics, and even all human societies 3,[5][6][7][8][9][10] . Given the breadth and intricacy mentioned, classifying complex systems is an outstanding issue that has attracted vast research interests [11][12][13][14] .…”