M. Rockenbach

Outer radiation belt dropout dynamics following the arrival of two interplanetary coronal mass ejections

Magnetopause shadowing and wave-particle interactions are recognized as the two primary mechanisms for losses of electrons from the outer radiation belt. We investigate these mechanisms, sing satellite observations both in interplanetary space and within the magnetosphere and particle drift modeling. Two interplanetary shocks sheaths impinged upon the magnetopause causing a relativistic electron flux dropout. The magnetic cloud (C) and interplanetary structure sunward of the MC had primarily northward magnetic field, perhaps leading to a concomitant lack of substorm activity and a 10 day long quiescent period. The arrival of two shocks caused an unusual electron flux dropout. Test-particle simulations have shown 2 to 5 MeV energy, equatorially mirroring electrons with initial values of L 5.5can be lost to the magnetosheath via magnetopause shadowing alone. For electron losses at lower L-shells, coherent chorus wave-driven pitch angle scattering and ULF wave-driven radial transport have been shownto be viable mechanisms.

Raios cósmicos e a heliosfera

This paper synthesizes the modulation of the particle flux of galactic cosmic rays during their transport inside the heliosphere and provides some fundamental references to understanding of this modulation. In this context, all elements, phenomena and events are preferably described in a theoretical-conceptual way, just using the mathematical approach when it is really indispensable. Starting from the fundamental definitions of heliospheric and cosmic rays, we discuss the mutual interactions of these elements, which is severely related to fluid dynamics and electromagnetism. The conclusion indicates the importance of this theme for the geosciences and space sciences.

A Neural Network Approach for Identifying Particle Pitch Angle Distributions in Van Allen Probes Data

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, 90deg 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 belts monitoring.