GSST/CBERS-5 International Symposium on Machine Learning in Heliophysics and Space Weather

Identifying Satellite Crossings of the Earth's Magnetosheath Using Unsupervised Learning Algorithms

Not scheduled

20m

Fernando de Mendonça - LIT (National Institute for Space Research, São José dos Campos, SP, Brazil)

Oral Machine Learning in Space, Earth & Atmospheric Sciences Oral Contributions

Speaker

javier silva (Instituto Nacional de Pesquisas Espaciais)

Description

In this study, we applied machine learning techniques to perform an unsupervised clustering of THEMIS satellite orbits to detect magnetosheath crossings. We used the DBSCAN algorithm to analyze crossings within a range of less than 40 Earth radii, focusing on data from the THEMIS-B (THB) and THEMIS-C (THC) spacecraft during 2008 and 2009. These spacecraft were selected due to their eccentric orbits, which facilitate multiple crossings through the magnetosheath. Using electron, ion, and magnetic field data, our algorithm effectively identified several magnetosheath crossings, demonstrating the robustness and applicability of the unsupervised approach. As a final result, we created a consolidated database compiling the magnetosheath crossings identified for the THEMIS mission, which constitutes a valuable resource for the detailed study of magnetospheric dynamics and has the potential to contribute to the development of more accurate models of the magnetosphere in the future.