Dependence of the Solar Wind Plasma Density on Moderate- and Extremely High-Geomagnetic Activity Elucidated by Potential Learning
Ryozo Kitajima, Motoharu Nowada, Ryotaro Kamimura

TL;DR
This study uses a novel machine learning approach to reveal that solar wind plasma density influences geomagnetic activity levels, especially above moderate Kp, providing insights for better space weather forecasting.
Contribution
The paper introduces potential learning, a neural network method, to identify the significance of solar wind density in geomagnetic activity, highlighting its impact beyond previous velocity-focused analyses.
Findings
Solar wind density significantly affects geomagnetic activity above moderate Kp.
Potential learning identified solar wind velocity as the most significant parameter at extreme Kp levels.
Solar wind density's impact on Kp is 3.5 times higher than previously recognized.
Abstract
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,…
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Taxonomy
TopicsEarthquake Detection and Analysis
