@Article{BarchiCRSSMCGSM:2020:CoSt,
               author = "Barchi, Paulo Henrique and Carvalho, Reinaldo Ramos de and Rosa, 
                         Reinaldo Roberto and Sautter, Rubens Andreas and Soares Santos, M. 
                         and Marques, B. A. D. and Clua, E. and Gon{\c{c}}alves, T. S. and 
                         S{\'a} Freitas, C. de and Moura, T. C.",
          affiliation = "{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 {Brandeis University} and {Universidade 
                         Federal Fluminense (UFF)} and {Universidade Federal Fluminense 
                         (UFF)} and {Universidade Federal do Rio de Janeiro (UFRJ)} and 
                         {Universidade Federal do Rio de Janeiro (UFRJ)} and {Universidade 
                         de S{\~a}o Paulo (USP)}",
                title = "Machine and deep learning applied to galaxy morphology: a 
                         comparative study",
              journal = "Astronomy and Computing",
                 year = "2020",
               volume = "30",
                pages = "e100334",
                month = "Jan.",
             keywords = "Galaxies: photometry, Methods: data analysis, Machine learning, 
                         Techniques: image processing, Galaxies: General, Catalogs.",
             abstract = "Morphological classification is a key piece of information to 
                         define samples of galaxies aiming to study the large-scale 
                         structure of the universe. In essence, the challenge is to build 
                         up a robust methodology to perform a reliable morphological 
                         estimate from galaxy images. Here, we investigate how to 
                         substantially improve the galaxy classification within large 
                         datasets by mimicking human classification. We combine accurate 
                         visual classifications from the Galaxy Zoo project with machine 
                         and deep learning methodologies. We propose two distinct 
                         approaches for galaxy morphology: one based on non-parametric 
                         morphology and traditional machine learning algorithms; and 
                         another based on Deep Learning. To measure the input features for 
                         the traditional machine learning methodology, we have developed a 
                         system called CyMorph, with a novel non-parametric approach to 
                         study galaxy morphology. The main datasets employed comes from the 
                         Sloan Digital Sky Survey Data Release 7 (SDSS-DR7). We also 
                         discuss the class imbalance problem considering three classes. 
                         Performance of each model is mainly measured by Overall Accuracy 
                         (OA). A spectroscopic validation with astrophysical parameters is 
                         also provided for Decision Tree models to assess the quality of 
                         our morphological classification. In all of our samples, both Deep 
                         and Traditional Machine Learning approaches have over 94.5% OA to 
                         classify galaxies in two classes (elliptical and spiral). We 
                         compare our classification with state-of-the-art morphological 
                         classification from literature. Considering only two classes 
                         separation, we achieve 99% of overall accuracy in average when 
                         using our deep learning models, and 82% when using three classes. 
                         We provide a catalog with 670,560 galaxies containing our best 
                         results, including morphological metrics and classification.",
                  doi = "10.1016/j.ascom.2019.100334",
                  url = "http://dx.doi.org/10.1016/j.ascom.2019.100334",
                 issn = "2213-1337",
             language = "en",
           targetfile = "barchi_machine.pdf",
        urlaccessdate = "27 abr. 2024"
}