@InCollection{SouzaMKAVDSJB:2018:ClMaPa,
author = "Souza, Vitor M. and Medeiros, Cl{\'a}udia and Koga, Daiki and
Alves, Livia Ribeiro and Vieira, Lu{\'{\i}}s Eduardo Antunes and
Dal Lago, Alisson and Silva, Ligia Alves da and Jauer, Paulo
Ricardo and Baker, Daniel",
title = "Classification of magnetospheric particle distributions via neural
networks",
booktitle = "Machine learning techniques for space weather",
publisher = "Elsevier",
year = "2018",
editor = "Camporeale, Enrico and Johnson, Jay and Wing, Simon",
pages = "329--353",
keywords = "Self-organizing map, Pitch angle distribution, Earth’s
magnetosphere.",
abstract = "In this chapter we introduce a special kind of neural network
known as a self-organizing map (SOM) and use it to
cluster/classify pitch angle-resolved particle flux data obtained
by instruments onboard satellites orbiting the Earth. As an
example of the technique, we employ electron flux data at both
relativistic and subrelativistic energies provided by two
instruments onboard one of the twin NASAs Van Allen Probes. For
these data sets the SOM can identify the shapes of three
well-known types of pitch angle distributions, and from that
knowledge one can infer the associated physical mechanisms in the
near-Earth space environment, particularly in the Van Allen
radiation belts region. The SOM-based methodology can be used with
multiplatform spacecraft data, thus enabling a prompt
characterization of the physical processes throughout the Earths
magnetosphere. The steps required to apply our neural
network-based approach to pitch angle-resolved particle flux data
from any spacecraft mission are laid out.",
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
{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {University of
Colorado Boulder}",
doi = "10.1016/B978-0-12-811788-0.00013-5",
url = "http://dx.doi.org/10.1016/B978-0-12-811788-0.00013-5",
isbn = "0128117893",
label = "lattes: 2470949000200852 4 SouzaMKAVDSJB:2018:ClMaPa",
language = "en",
targetfile = "souza_classification.pdf",
urlaccessdate = "28 abr. 2024"
}