@InProceedings{MedeirosSVSASMJSDM:2018:RePaEM,
               author = "Medeiros, 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, Ligia Alves 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",
          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 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)}",
                title = "Recognizing pattern in EMIC waves spectrograms using machine 
                         learning",
                 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 Self-Organizing Map (SOM). When applied to 
                         images of magnetic field spectrograms in the frequency range of 
                         EMIC waves, the SOM 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 SOM 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 SOM 
                         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.",
             language = "en",
           targetfile = "medeiros_recognizing.pdf",
        urlaccessdate = "03 maio 2024"
}