@Article{MedeirosSVSRSAMJSDK:2020:BaAlAp,
               author = "Medeiros, Claudia and Souza, Vitor Moura Cardoso e Silva and 
                         Vieira, Luis Eduardo Antunes and Sibeck, D. G. and Remya, B. and 
                         Silva, Ligia Alves da and Alves, Livia Ribeiro and Marchezi, 
                         Jos{\'e} Paulo and Jauer, Paulo Ricardo and Silva, Marlos 
                         Rockenbach da and Dal Lago, Alisson and Kletzing, C. A.",
          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 {Indian Institute of Geomagnetism} 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 = "Electromagnetic ion cyclotron waves pattern recognition based on a 
                         deep learning technique: bag-of-features algorithm applied to 
                         spectrograms",
              journal = "Astrophysical Journal Supplement Series",
                 year = "2020",
               volume = "249",
               number = "1",
                pages = "e13",
                month = "July",
             keywords = "Planetary magnetosphere, Van Allen radiation belt, Neural 
                         networks, Space plasmas, pp waves, Support vector machine, 
                         Classification.",
             abstract = "Several studies have shown the importance of electromagnetic ion 
                         cyclotron (EMIC) waves to the pitch angle scattering of energetic 
                         particles in the radiation belt, especially relativistic 
                         electrons, thus contributing to their net loss 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 deep 
                         learning technique referred to as the 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, in a 
                         semi-automated way, several patterns in these spectrograms that 
                         can be relevant to describe physical aspects of EMIC waves. Each 
                         spectrogram image provided as an input to the BoF corresponds to 
                         the windowed Fourier transform of a similar to 40 minutes to 1 
                         hour interval of Van Allen Probes' high time-resolution vector 
                         magnetic field observations. Our data set spans the 2012 September 
                         8 to 2016 December 31 period and is at geocentric distances larger 
                         than 3 Earth radii. A total of 66,204 spectrogram images are 
                         acquired in this interval, and about 45% of them, i.e., 30,190 
                         images, are visually inspected to validate the BoF technique. The 
                         BoF's performance in identifying spectrograms with likely EMIC 
                         wave signatures is comparable to the visual inspection method, 
                         with the enormous advantage that the BoF technique greatly 
                         expedites the analysis by accomplishing the task in just a few 
                         minutes.",
                  doi = "10.3847/1538-4365/ab9697",
                  url = "http://dx.doi.org/10.3847/1538-4365/ab9697",
                 issn = "0067-0049 and 1538-4365",
             language = "en",
           targetfile = "medeiros_electromagnetic.pdf",
        urlaccessdate = "11 maio 2024"
}