%0 Journal Article
%4 sid.inpe.br/mtc-m21b/2016/06.01.18.19
%2 sid.inpe.br/mtc-m21b/2016/06.01.18.19.22
%@doi 10.1002/2015SW001349
%@issn 1542-7390
%T A neural network approach for identifying particle pitch angle distributions in Van Allen Probes data
%D 2016
%8 Apr.
%9 journal article
%A Souza, Vitor Moura Cardoso e Silva,
%A Vieira, Luis Eduardo Antunes,
%A Medeiros, Cláudia,
%A Silva, Lígia Alves da,
%A Alves, Livia Ribeiro,
%A Koga, Daiki,
%A Sibeck, D. G.,
%A Walsh, B. M.,
%A Kanekal, S. G.,
%A Jauer, P. R.,
%A Rockenbach da Silva, Marlos,
%A Dal Lago, Alisson,
%A Silveira, Marcos Vinicius Dias,
%A Marchezi, José Paulo,
%A Mendes, Odim,
%A Gonzalez Alarcon, Walter Demétrio,
%A Baker, D. N.,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation NASA Goddard Space Flight Center
%@affiliation Boston University
%@affiliation NASA Goddard Space Flight Center
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation University of Colorado Boulder
%@electronicmailaddress vitor.souza@inpe.br
%@electronicmailaddress luis.vieira@inpe.br
%@electronicmailaddress claudia.medeiros@inpe.br
%@electronicmailaddress ligia.silva@inpe.br
%@electronicmailaddress livia.alves@inpe.br
%@electronicmailaddress daiki.koga@inpe.br
%@electronicmailaddress
%@electronicmailaddress
%@electronicmailaddress
%@electronicmailaddress
%@electronicmailaddress marlos.silva@inpe.br
%@electronicmailaddress alisson.dallago@inpe.br
%@electronicmailaddress marcos.silveira@inpe.br
%@electronicmailaddress jose.marchezi@inpe.br
%@electronicmailaddress odim.mendes@inpe.br
%@electronicmailaddress walter.alarcon@inpe.br
%B Space Weather
%V 14
%N 4
%P 275-284
%K pitch angle distributions, self-organizing maps, Van Allen belt's monitoring.
%X 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.
%@language en
%3 souza_a neural.pdf