Abstract
Aiming the extraction of the significant solar wind parameters which trigger the geomagnetic disturbances (disturbances of AL-index), we try to test an extraction of the most influential parameter of solar wind which would cause a magnetic storm seen on 8th September 2017 based on a neural network. Terrestrial magnetosphere is always exposed and disturbed by high-speed plasma flows from the Sun (solar wind). Large-scale geomagnetic disturbance bring various troubles to the electronic instruments in ground. It is important to specify what the solar wind parameters trigger the significant geomagnetic disturbances. To extract the solar wind parameter(s) to trigger a significant geomagnetic disturbance, we adopted a neural network called “potential learning (PL)”, which can predict targets and interpret a model. Utilizing the solar wind parameters, we created a prediction model whose generalization performance, evaluated by correlation coefficient between targets and predictions, was 0.8195. As a result, we can extract a parameter of “dynamic pressure”, obtained by solar wind velocity and number density. Because, in this case, it can be considered that the magnetospheric squeezing would play a role in triggering the AL-index disturbances, this extraction of “solar wind dynamic pressure” due to PL should be a reasonable result. Therefore, we conclude that the PL is useful and important tool in extracting what solar wind parameters affect the significant geomagnetic disturbances.
