@Article{SouzaVMSAKSWKJRDSMMGB:2016:NeNeAp,
author = "Souza, Vitor Moura Cardoso e Silva and Vieira, Luis Eduardo
Antunes and Medeiros, Cl{\'a}udia and Silva, L{\'{\i}}gia Alves
da and Alves, Livia Ribeiro and Koga, Daiki and Sibeck, D. G. and
Walsh, B. M. and Kanekal, S. G. and Jauer, P. R. and Rockenbach da
Silva, Marlos and Dal Lago, Alisson and Silveira, Marcos Vinicius
Dias and Marchezi, Jos{\'e} Paulo and Mendes, Odim and Gonzalez
Alarcon, Walter Dem{\'e}trio and Baker, D. N.",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
{NASA Goddard Space Flight Center} and {Boston University} and
{NASA Goddard Space Flight Center} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {University of Colorado Boulder}",
title = "A neural network approach for identifying particle pitch angle
distributions in Van Allen Probes data",
journal = "Space Weather",
year = "2016",
volume = "14",
number = "4",
pages = "275--284",
month = "Apr.",
keywords = "pitch angle distributions, self-organizing maps, Van Allen belt's
monitoring.",
abstract = "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° 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's monitoring.",
doi = "10.1002/2015SW001349",
url = "http://dx.doi.org/10.1002/2015SW001349",
issn = "1542-7390",
language = "en",
targetfile = "souza_a neural.pdf",
urlaccessdate = "2024, Apr. 29"
}