@Article{ArantesFoRosaGuim:2021:InSuCl,
               author = "Arantes Filho, Lu{\'{\i}}s R. and Rosa, Reinaldo Roberto and 
                         Guimar{\~a}es, Lamartine N. F.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Departamento de 
                         Ci{\^e}ncia e Tecnologia Aeroespacial (DCTA)}",
                title = "Intelligent supernovae classification systems in the kdust 
                         context",
              journal = "Anais da Academia Brasileira de Ci{\^e}ncias",
                 year = "2021",
               volume = "93",
                pages = "e20200862",
             abstract = "With the advent of large astronomical surveys plus multi-messenger 
                         astronomy, both automatic detection and classification of Type Ia 
                         supernovae have been addressed by different machine learning 
                         techniques. In this article we present three solutions aimed at 
                         the future spectrometer of the KDUST project, within a scope of 
                         benchmark, considering three different methodologies. The systems 
                         presented here are the following: CINTIA (based on hierarchical 
                         neural network architecture), SUZAN (which incorporates the 
                         solution known as fuzzy systems) and DANI (based on Deep Learning 
                         with Convolutional Neural Networks). The characteristics of the 
                         systems are presented and the benchmark is performed considering a 
                         data set containing 15.134 spectra. The best performance is 
                         obtained by the DANI architecture which provides 96% accuracy in 
                         the classification of Type Ia supernovae in relation to other 
                         spectral types.",
                  doi = "10.1590/0001-3765202120200862",
                  url = "http://dx.doi.org/10.1590/0001-3765202120200862",
                 issn = "0001-3765",
             language = "en",
           targetfile = "download.pdf",
        urlaccessdate = "20 maio 2024"
}