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Analysis of particle pitch angle distributions (PADs) has been used as a means to comprehend a multitude of different physical mechanisms that lead to flux variations in the Van Allen belts and also to particle precipitation into the upper atmosphere. In this work we developed a neural network-based data clustering methodology that automatically identifies distinct PAD types in an unsupervised way using particle flux data. One can promptly identify and locate three well-known PAD types in both time and radial distance, namel ...
Souza, V.; Vieira, L.; Medeiros, C.; Da Silva, L.; Alves, L.; Koga, D.; Sibeck, D.; Walsh, B.; Kanekal, S.; Jauer, P.; Rockenbach, M.; Dal Lago, A.; Silveira, M.; Marchezi, J.; Mendes, O.; Gonzalez, W.; Baker, D.;
YEAR: 2016   DOI: 10.1002/2015SW001349