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Plasmaspheric dynamics studied using a three-dimensional machine learning-based plasma density model in the inner magnetosphere and the ionosphere
Ace, Hannah
Ace, Hannah
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Abstract
Plasmaspheric density and composition strongly influence wave growth and propagation, as well as energetic particle scattering. Previous statistical, empirical plasma density models of the inner magnetosphere have limited capability to make accurate predictions. Consequently, these models cannot be used to adequately quantify complex global processes and nonlinear responses to driving conditions, factors of critical importance during geomagnetic storms. Recent advancements in machine learning techniques have enabled a more dynamic study of the space environment. Here we present two three-dimensional dynamic electron density models (one magnetospheric model and one ionospheric model) based on an artificial neural network. The models use feedforward neural networks which were generated using electron densities from satellite missions of CRRES, ISEE, IMAGE, POLAR, and DMSP. The three-dimensional electron density model takes spacecraft location and time series solar wind conditions (e.g., flow speed, plasma density, solar radiance) and geomagnetic indices (e.g., AP index, AE and AL indices, F10.7 index, Dst index, KP index, PCN index, and solar Lyman-Alpha flux) obtained from NASA’s OMNI database as inputs. When compared with the out-of-sample data, the three-dimensional models predict equatorial and field-aligned density profiles from satellite measurements with root mean square errors of 0.410 and 0.622, respectively. When the three-dimensional magnetospheric model is applied to a geomagnetic storm, successful reconstruction of the expected plasmaspheric dynamics, such as the plasmaspheric erosion, and plume formation in three dimensions was achieved.
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Date
2021-01-01