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Found 2 entries in the Bibliography.
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2018 |
Ultra-low frequency (ULF) waves play a fundamental role in the dynamics of the inner-magnetosphere and outer radiation belt during geomagnetic storms. Broadband ULF wave power can transport energetic electrons via radial diffusion and discrete ULF wave power can energize electrons through a resonant interaction. Using observations from the Magnetospheric Multiscale (MMS) mission, we characterize the evolution of ULF waves during a high-speed solar wind stream (HSS) and moderate geomagnetic storm while there is an enhancement of the outer radiation belt. The Automated Flare Inference of Oscillations (AFINO) code is used to distinguish discrete ULF wave power from broadband wave power during the HSS. During periods of discrete wave power and utilizing the close separation of the MMS spacecraft, we estimate the toroidal mode ULF azimuthal wave number throughout the geomagnetic storm. We concentrate on the toroidal mode as the HSSs compresses the day side magnetosphere resulting in an asymmetric magnetic field topology where toroidal mode waves can interact with energetic electrons. Analysis of the mode structure and wave numbers demonstrates that the generation of the observed ULF waves is a combination of externally driven waves, via the Kelvin-Helmholtz instability, and internally driven waves, via unstable ion distributions. Further analysis of the periods and toroidal azimuthal wave numbers suggests that these waves can couple with the core electron radiation belt population via the drift resonance during the storm. The azimuthal wave number and structure of ULF wave power (broadband or discrete) have important implications for the inner-magnetospheric and radiation belt dynamics. Murphy, Kyle; Inglis, Andrew; Sibeck, David; Rae, Jonathan; Watt, Clare; Silveira, Marcos; Plaschke, Ferdinand; Claudepierre, Seth; Nakamura, Rumi; Published by: Journal of Geophysical Research: Space Physics Published on: 05/2018 YEAR: 2018   DOI: 10.1029/2017JA024877 azimuthal wave number; Geomagnetic storms; mode structure; Radiation belts; ULF waves; Van Allen Probes |
2016 |
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, namely, 90\textdegree peaked, butterfly, and flattop distributions. In order to illustrate the applicability of our methodology, we used relativistic electron flux data from the whole month of November 2014, acquired from the Relativistic Electron-Proton Telescope instrument on board the Van Allen Probes, but it is emphasized that our approach can also be used with multiplatform spacecraft data. Our PAD classification results are in reasonably good agreement with those obtained by standard statistical fitting algorithms. The proposed methodology has a potential use for Van Allen belt\textquoterights monitoring. 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.; Published by: Space Weather Published on: 04/2016 YEAR: 2016   DOI: 10.1002/2015SW001349 pitch angle distributions; self-organizing maps; Van Allen belt\textquoterights monitoring; Van Allen Probes |
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