Storm‐Time Ring Current Plasma Pressure Prediction Based on the Multi‐Output Convolutional Neural Network Model - Yun Yan; Zi‐Kang Xie; Chao Yue; Jiu‐Tong Zhao; Fan Yang; Lun Xie; Qiu‐Gang Zong; Xu‐Zhi Zhou; Shan Wang (Space Weather)
Abstract The terrestrial ring current consists of particles with energy from several keV to 100 s of keV, and its enhancement will result in magnetic field depression, known as geomagnetic storms. The ring current is mainly composed of H+, O+, He+, and electrons, and there has been a longstanding debate regarding their relative contributions. In this study, we employed a multi‐output convolutional neural network to predict the storm‐time ring current plasma pressures of these particles. Taking solar wind parameters, interplanetary magnetic field (IMF) data, and geomagnetic indices with a time history of 3 days as input parameters, the model shows good performances for electron plasma pressure, H+ plasma pressure, He+ plasma pressure, and O+ plasma pressure in both quiet‐time and storm‐time periods, with high correlation coefficients and small root mean square errors between the measured and the predicted values. Our model successfully captures ring current enhancement during the storm main phase and species‐dependent decay during the recovery phase. Moreover, the model's ability to predict plasma pressures of different species simultaneously facilitates a comparative analysis of their respective contributions to the ring current. The storm event where our model was applied demonstrates that during the storm, the contribution of H+ decreases but still dominates, while the contribution of O+ increases dramatically, and electrons and He+ also play roles in some localized regions. The application of this model is in line with previous observations and simulations, which can be utilized for quantitative analysis of storm‐time ring current dynamics.