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@Article{CarrubaAljDomHuaBar:2022:MaLeAp,
               author = "Carruba, Valerio and Aljbaae, Safwan and Domingos, R. C. and 
                         Huaman, M. and Barletta, W.",
          affiliation = "{Universidade Estadual Paulista (UNESP)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {Universidade Estadual Paulista 
                         (UNESP)} and {Universidad tecnol{\'o}gica del Per{\'u} (UTP)} 
                         and {Universidade Estadual Paulista (UNESP)}",
                title = "Machine learning applied to asteroid dynamics",
              journal = "Celestial Mechanics and Dynamical Astronomy",
                 year = "2022",
               volume = "134",
               number = "4",
                pages = "e36",
                month = "Aug.",
             keywords = "Asteroid belt, Celestial mechanics, Chaotic motions, Statistical 
                         methods.",
             abstract = "Machine learning (ML) is the branch of computer science that 
                         studies computer algorithms that can learn from data. It is mainly 
                         divided into supervised learning, where the computer is presented 
                         with examples of entries, and the goal is to learn a general rule 
                         that maps inputs to outputs, and unsupervised learning, where no 
                         label is provided to the learning algorithm, leaving it alone to 
                         find structures. Deep learning is a branch of machine learning 
                         based on numerous layers of artificial neural networks, which are 
                         computing systems inspired by the biological neural networks that 
                         constitute animal brains. In asteroid dynamics, machine learning 
                         methods have been recently used to identify members of asteroid 
                         families, small bodies images in astronomical fields, and to 
                         identify resonant arguments images of asteroids in three-body 
                         resonances, among other applications. Here, we will conduct a full 
                         review of available literature in the field and classify it in 
                         terms of metrics recently used by other authors to assess the 
                         state of the art of applications of machine learning in other 
                         astronomical subfields. For comparison, applications of machine 
                         learning to Solar System bodies, a larger area that includes 
                         imaging and spectrophotometry of small bodies, have already 
                         reached a state classified as progressing. Research communities 
                         and methodologies are more established, and the use of ML led to 
                         the discovery of new celestial objects or features, or new 
                         insights in the area. ML applied to asteroid dynamics, however, is 
                         still in the emerging phase, with smaller groups, methodologies 
                         still not well-established, and fewer papers producing discoveries 
                         or insights. Large observational surveys, like those conducted at 
                         the Zwicky Transient Facility or at the Vera C. Rubin Observatory, 
                         will produce in the next years very substantial datasets of 
                         orbital and physical properties for asteroids. Applications of ML 
                         for clustering, image identification, and anomaly detection, among 
                         others, are currently being developed and are expected of being of 
                         great help in the next few years.",
                  doi = "10.1007/s10569-022-10088-2",
                  url = "http://dx.doi.org/10.1007/s10569-022-10088-2",
                 issn = "0923-2958",
             language = "en",
           targetfile = "s10569-022-10088-2.pdf",
        urlaccessdate = "28 mar. 2024"
}


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