arXiv subjects

Ryozo Kitajima

Publications and source records attributed to Ryozo Kitajima.

2 recordsLinked to original sources

arXiv2024

Dependence of the Solar Wind Plasma Density on Moderate- and Extremely High-Geomagnetic Activity Elucidated by Potential Learning

The relationship between moderate and extremely high levels of geomagnetic activity, represented by the Kp index (2- to 5+ and 6- to 9), and solar wind conditions during southward IMF intervals was revealed utilizing a newly developed machine learning technique. Potential learning (PL) is a neural network algorithm that emphasizes input parameters with the highest variance during training and identifies the most significant ones influencing the outputs based on a computed metric called "potentiality". We focus on the dependence of solar wind plasma density on moderate-geomagnetic conditions. It has been unclear from what stage of geomagnetic activity the solar wind density begins to control the Kp level. Previously, PL extracted solar wind velocity as the predominant parameter at extremely low (0 to 1+) and high-Kp ranges under southward IMF. In this study, the IMF three components, solar wind speed, and plasma density from the OMNI database (1998-2019), covering solar cycle 23 to early 25, were used as inputs. Again, PL selected solar wind velocity as the most significant parameter for moderate and extremely high Kp. The potentiality of solar wind density for these ranges was, however, 3.5 times higher than in the previous study, suggesting its impact on geomagnetic activity cannot be ignored. We statistically investigated the relation between solar wind speed and plasma density used as PL inputs under all Kp levels. Above moderate Kp, geomagnetic conditions become high even under slow solar wind if density is large, suggesting that not only velocity but also density contributes significantly. These PL and statistical investigations show that solar wind density begins to regulate Kp above moderate geomagnetic activity under southward IMF. They also help understand the relationship between solar wind and geomagnetic activity and forecast geomagnetic activity under various IMF conditions.

physics.space-ph↗

arXiv2022

Investigation of the Relationship between Geomagnetic Activity and Solar Wind Parameters Based on A Novel Neural Network (Potential Learning)

Predicting geomagnetic conditions based on in-situ solar wind observations allows us to evade disasters caused by large electromagnetic disturbances originating from the Sun to save lives and protect economic activity. In this study, we aimed to examine the relationship between the Kp index, representing global magnetospheric activity level, and solar wind conditions using an interpretable neural network known as potential learning (PL). Data analyses based on neural networks are difficult to interpret; however, PL learns by focusing on the "potentiality of input neurons" and can identify which inputs are significantly utilized by the network. Using the full advantage of PL, we extracted the influential solar wind parameters that disturb the magnetosphere under southward Interplanetary magnetic field (IMF) conditions. The input parameters of PL were the three components of the IMF (Bx, By, -Bz(Bs)), solar wind flow speed (Vx), and proton number density (Np) in geocentric solar ecliptic (GSE) coordinates obtained from the OMNI solar wind database between 1998 and 2019. Furthermore, we classified these input parameters into two groups (targets), depending on the Kp level: Kp = 6- to 9 (positive target) and Kp = 0 to 1+ (negative target). Negative target samples were randomly selected to ensure that numbers of positive and negative targets were equal. The PL results revealed that solar wind flow speed is an influential parameter for increasing Kp under southward IMF conditions, which was in good agreement with previous reports on the statistical relationship between the Kp index and solar wind velocity, and the Kp formulation based on the IMF and solar wind plasma parameters. Based on this new neural network, we aim to construct a more correct and parameter-dependent space weather forecasting model.

physics.space-ph↗