@Article{CarrubaAljCarDomMar:2022:OpArNe,
               author = "Carruba, Val{\'e}rio and Aljbaae, Safwan and Carit{\'a}, Gabriel 
                         Antonio and Domingos, R. C. and Martins, B.",
          affiliation = "{Universidade Estadual Paulista (UNESP)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Universidade Estadual Paulista 
                         (UNESP)} and {Universidade Estadual Paulista (UNESP)}",
                title = "Optimization of artificial neural networks models applied to the 
                         identification of images of asteroids’ resonant arguments",
              journal = "Celestial Mechanics and Dynamical Astronomy",
                 year = "2022",
               volume = "134",
               number = "6",
                pages = "e59",
                month = "Dec.",
             keywords = "Asteroids, General, Minor planets, Time domain astronomy, Time 
                         series analysis.",
             abstract = "The asteroidal main belt is crossed by a web of mean motion and 
                         secular resonances that occur when there is a commensurability 
                         between fundamental frequencies of the asteroids and planets. 
                         Traditionally, these objects were identified by visual inspection 
                         of the time evolution of their resonant argument, which is a 
                         combination of orbital elements of the asteroid and the perturbing 
                         planet(s). Since the population of asteroids affected by these 
                         resonances is, in some cases, of the order of several thousand, 
                         this has become a taxing task for a human observer. Recent works 
                         used convolutional neural network (CNN) models to perform such 
                         task automatically. In this work, we compare the outcome of such 
                         models with those of some of the most advanced and publicly 
                         available CNN architectures, like the VGG, Inception, and ResNet. 
                         The performance of such models is first tested and optimized for 
                         overfitting issues, using validation sets and a series of 
                         regularization techniques like data augmentation, dropout, and 
                         batch normalization. The three best-performing models were then 
                         used to predict the labels of larger testing databases containing 
                         thousands of images. The VGG model, with and without 
                         regularizations, proved to be the most efficient method to predict 
                         labels of large datasets. Since the Vera C. Rubin observatory is 
                         likely to discover up to four million new asteroids in the next 
                         few years, the use of these models might become quite valuable to 
                         identify populations of resonant minor bodies.",
                  doi = "10.1007/s10569-022-10110-7",
                  url = "http://dx.doi.org/10.1007/s10569-022-10110-7",
                 issn = "0923-2958",
             language = "en",
           targetfile = "s10569-022-10110-7.pdf",
        urlaccessdate = "21 maio 2024"
}