Fechar
Metadados

@InCollection{SantosSouzMuraSant:2019:HyNeNe,
               author = "Santos, Rafael Duarte Coelho dos and Souza, Felipe Carvalho de and 
                         Muralikrishna, Amita and Santos J{\'u}nior, Walter Augusto dos",
                title = "A hybrid neural network approach to estimate galaxy redshifts from 
                         multi-band photometric surveys",
            booktitle = "Astronomical data analysis software and systems XXVIII",
            publisher = "Astronomical Society of the Pacific",
                 year = "2019",
               editor = "Teuben, P. J. and Pound, M. W. and Thomas, B. A. and Warner, E. 
                         M.",
                pages = "103--106",
                 note = "28th Annual Conference on Astronomical Data Analysis Software and 
                         Systems (ADASS XXVIII), 11-15 nov. 2018, University of Maryland, 
                         MD.",
             keywords = "neural network, galaxy.",
             abstract = "Machine learning methods have been used in cosmological studies to 
                         estimate variables that would be hard or costly to measure 
                         precisely, like, for example, estimating redshifts from 
                         photometric data. Previous work showed good results for estimating 
                         photometric redshifts using nonlinear regression based on an 
                         artificial neural network (MultiLayer Perceptron or MLP). In this 
                         work we explore a hybrid neural network approach that uses a 
                         Self-Organizing Map (SOM) to separate the original data into 
                         different groups, then applying the MLP to each neuron on the SOM 
                         to obtain different regression models for each group. Preliminary 
                         results indicate that in some cases better results can be 
                         achieved, although the computational cost may be increased.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
             language = "en",
          seriestitle = "Astronomical Society of the Pacific Conference Series",
           targetfile = "santos_hybrid.pdf",
               volume = "523",
        urlaccessdate = "10 abr. 2021"
}


Fechar