author = "Pereira, Eduardo dos Santos and Santos, Pedro A. and Campos Velho, 
                         Haroldo Fraga de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "A framework to determine black-hole distribution for large 
                 year = "2018",
         organization = "Internaitonal Conference on Integral Methods in Science and 
                         Engineering, 15. (IMSE)",
             keywords = "Massive and supermassive black holes, inverse problem, regularized 
             abstract = "Massive and super-massive black-holes (BH) can be find in the 
                         center of all galaxies, according to the opinion from the 
                         scientific community. However, an open question is to know the 
                         origin of such BHs. One explanation is consider such objects 
                         coming from the stars with large redshifts very old stars colapsed 
                         during the ancient eras (population-III stars: the first-born 
                         stars created in the universe). A framework to investigate this 
                         scenario is to derive a mathematical model describing the 
                         evolution of the black hole distribution. From the distribution 
                         observed nowadays, the problem is formulated as an inverse problem 
                         to determine a possible disrtibution for large redshift, where the 
                         cost function is given by the square difference between the 
                         observation and the model data under association with a 
                         regularization operator. The regularized inverse solution can be 
                         computed using an optimizer to identify the best solution for the 
                         functional to be minimized. Preliminar results are shown using 
                         synthetic observational data.",
  conference-location = "Brighton, UK",
      conference-year = "16-20 July",
             language = "en",
        urlaccessdate = "05 dez. 2020"