Complex Network for Solar Protons and Correlations With Flares

Abstract

The high-energy particles coming from the Sun with energies in the range of a few KeV to more than several GeV are called solar energetic particles (SEPs). Solar eruptions such as coronal mass ejections (CMEs) and solar flares are the important sources of SEPs (Gopalswamy et al., 2015;Krucker & Lin, 2000). Magnetic reconnection and waves are the crucial mechanisms for accelerating particles and the production of SEPs. Among SEPs, solar proton events (SPEs) are one of the most significant components of interplanetary streams and play an important role in space weather studies (Reames, 1999). SPEs can be spectacular as they are in auroras or be dangerous as they form solar storms. A solar storm is a flux of SEPs which is carried by the solar wind and can be detrimental to astronauts' health or damage the electrical devices in space or even on the Earth (Feynman & Gabriel, 2000;Jiggens et al., 2014). Simply, a geomagnetic storm can be defined as a major disturbance in the Earth's magnetic field that may be the result of sudden and drastic changes in the solar wind (Taran et al., 2019, and references therein), leading to changes in the current, plasma, and magnetic fields of the magnetosphere. It is accepted that the geomagnetic storms on the Earth may affect radio communication and cause hardware damage on satellites and the global positioning system (GPS). Obviously, not all CMEs can cause a geomagnetic storm; conditions on the strength and direction of the interplanetary magnetic field need to create an intense storm (Gonzalez et al., 1994). Also, large speed and width increase the probability for a CME to create a geomagnetic storm (Gopalswamy et al., 2010). However, strong geomagnetic storms are most likely associated with large CMEs or CMEs associated with X-class flares (Srivastava & Venkatakrishnan, 2002). Recently, machine learning methods have been developed for forecasting the geomagnetic indices (Camporeale, 2019) and the occurrence time of large solar flares (Alipour et al., 2019) which are open challenges for the space weather community.Several researchers examined the dependence of SEP characteristics on flare parameters (

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

“…Here, we appraise its capability in determining SOC systems. We also compute the average clustering coefficients 6,7,10,55,56 of the HVGs and compare the outcomes with random processes.…”

“…Whilst, the maximum eigenvalues of Bak and Manna sandpile models are in the range of 5.5 − 6.3. The average clustering coefficients 6,7,10,55,56 of mentioned TS are also presented in Figure 5. Likewise the maximum eigenvalues, the average clustering coefficients of both sand-pile models are distinguishable from the random and chaos processes.…”

“…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] .…”

Evidence of self-organized criticality in time series by the horizontal visibility graph approach

“…Here, we appraise its capability in determining SOC systems. We also compute the average clustering coefficients 6 , 7 , 10 , 65 , 66 of the HVGs and compare the outcomes with random processes.…”

“…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 both, because on the one hand there is no all-inclusive interpretation of these systems 2 – 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 – 10 . Given the breadth and intricacy mentioned, classifying complex systems is an outstanding issue that has attracted vast research interests 11 – 14 .…”