@Article{CarrubaAljDomLucFur:2020:MaLeCl,
               author = "Carruba, Valerio and Aljbaae, Safwan and Domingos, R. C. and 
                         Lucchini, A. and Furlaneto, P.",
          affiliation = "{Universidade Estadual Paulista (UNESP)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {Universidade Estadual Paulista 
                         (UNESP)} and {Universidade Estadual Paulista (UNESP)} and 
                         {Universidade Estadual Paulista (UNESP)}",
                title = "Machine learning classification of new asteroid families members",
              journal = "Monthly Notices of the Royal Astronomical Society",
                 year = "2020",
               volume = "496",
               number = "1",
                pages = "540--549",
                month = "Jun",
             keywords = "software: data analysis – celestial mechanics – minor planets, 
                         asteroids: general.",
             abstract = "Asteroid families are groups of asteroids that are the product of 
                         collisions or of the rotational fission of a parent object. These 
                         groups are mainly identified in proper elements or frequencies 
                         domains. Because of robotic telescope surveys, the number of known 
                         asteroids has increased from 10 000 in the early 1990s to more 
                         than 750 000 nowadays. Traditional approaches for identifying new 
                         members of asteroid families, like the hierarchical clustering 
                         method (HCM), may struggle to keep up with the growing rate of new 
                         discoveries. Here we used machine learning classification 
                         algorithms to identify new family members based on the orbital 
                         distribution in proper (a, e, sin (i)) of previously known family 
                         constituents. We compared the outcome of nine classification 
                         algorithms from stand-alone and ensemble approaches. The extremely 
                         randomized trees (ExtraTree) method had the highest precision, 
                         enabling to retrieve up to 97 per cent of family members 
                         identified with standard HCM.",
                  doi = "10.1093/mnras/staa1463",
                  url = "http://dx.doi.org/10.1093/mnras/staa1463",
                 issn = "0035-8711 and 1365-2966",
             language = "en",
           targetfile = "carruba_mahine.pdf",
        urlaccessdate = "24 abr. 2024"
}