@InProceedings{MedeiroSVSASMJSDMK:2018:RePaEM,
author = "Medeiro, Cl{\'a}udia and Souza, Vitor Moura Cardoso e Silva and
Vieira, Lu{\'{\i}}s Eduardo Antunes and Sibeck, David G. and
Alves, Livia Ribeiro and Silva, L. A. da and Marchezi, Jos{\'e}
Paulo and Jauer, Paulo Ricardo and Silva, Marlos Rockenbach da and
Dal Lago, Alisson and Mendes J{\'u}nior, Odim and Kletzing, C.",
affiliation = "{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 Cente}
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 Iowa}",
title = "Recognizing Patterns in EMIC Waves Spectrograms Using Machine
Learning",
booktitle = "Proceedings...",
year = "2018",
organization = "AGU Fall Meeting",
abstract = "The EMFISIS instrument on board the twin Van Allen Probes has been
measuring high resolution magnetic field data during the last 6
years. Spectrograms can be obtained from such measurements which
can evidence the presence of electromagnetic ion cyclotron (EMIC)
waves. Several studies have been showing the relevance of EMIC
waves on the pitch angle scattering of energetic particles into
the loss cone, hence contributing to the net loss of relativistic
electrons from the outer radiation belt to the upper atmosphere.
The huge amount of data collected thus far provides us with the
opportunity to use a data clustering technique based on a neural
network referred to as Bag-of-Features (BoF). When applied to
images of magnetic field spectrograms in the frequency range of
EMIC waves, the BoF allows us to distinguish several patterns in
these spectrograms expediting their analysis which in turn can be
relevant to describe physical aspects of EMIC waves. Specifically,
the BoF technique is able to detect and classify distinct patterns
in the spectrograms which are identified as signatures of EMIC
wave packets. Each spectrogram image provided as input to the BoF
corresponds to the windowed Fourier transform of an one hour
interval of EMFISISs magnetic field data. Our dataset spans the
September 2012 to December 2016 period, where only in situ data
acquired at geocentric distances larger than or equal to 3 Earth
radii were selected. Preliminary results revealed that the
clustering technique employed here successfully detected, in an
automated way, different EMIC waves signatures like propagation in
Hydrogen, Helium and Oxygen bands, as well as, fairly
monochromatic mode waves, and further details that will be
discussed.",
conference-location = "Washington, D. C.",
conference-year = "10-14 dec.",
targetfile = "medeiros_recognizing.pdf",
urlaccessdate = "26 abr. 2024"
}