@Article{NovaesBernFerrWuen:2014:SePrNo,
               author = "Novaes, Camila Paiva and Bernu{\'{\i}}, Armando and Ferreira, 
                         Ivan Soares and Wuensche, Carlos Alexandre",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Observat{\'o}rio Nacional} and {Universidade de 
                         Bras{\'{\i}}lia} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Searching for primordial non-Gaussianity in Planck CMB maps using 
                         a combined estimator",
              journal = "Journal of Cosmology and Astroparticle Physics",
                 year = "2014",
               volume = "2014",
               number = "1",
                pages = "18",
             keywords = "primordial non-Gaussianity, Planck CMB maps, cosmic microwave 
                         background radiation.",
             abstract = "he extensive search for deviations from Gaussianity in cosmic 
                         microwave background radiation (CMB) data is very important due to 
                         the information about the very early moments of the universe 
                         encoded there. Recent analyses from Planck CMB data do not exclude 
                         the presence of non-Gaussianity of small amplitude, although they 
                         are consistent with the Gaussian hypothesis. The use of different 
                         techniques is essential to provide information about types and 
                         amplitudes of non-Gaussianities in the CMB data. In particular, we 
                         find interesting to construct an estimator based upon the 
                         combination of two powerful statistical tools that appears to be 
                         sensitive enough to detect tiny deviations from Gaussianity in CMB 
                         maps. This estimator combines the Minkowski functionals with a 
                         Neural Network, maximizing a tool widely used to study 
                         non-Gaussian signals with a reinforcement of another tool designed 
                         to identify patterns in a data set. We test our estimator by 
                         analyzing simulated CMB maps contaminated with different amounts 
                         of local primordial non-Gaussianity quantified by the 
                         dimensionless parameter f NL. We apply it to these sets of CMB 
                         maps and find < 98% of chance of positive detection, even for 
                         small intensity local non-Gaussianity like f NL = 38±18, the 
                         current limit from Planck data for large angular scales. 
                         Additionally, we test the suitability to distinguish between 
                         primary and secondary non-Gaussianities: first we train the Neural 
                         Network with two sets, one of nearly Gaussian CMB maps (|f NL| 10) 
                         but contaminated with realistic inhomogeneous Planck noise (i.e., 
                         secondary non-Gaussianity) and the other of non-Gaussian CMB maps, 
                         that is, maps endowed with weak primordial non-Gaussianity (28 f 
                         NL 48); after that we test an ensemble composed of CMB maps either 
                         with one of these non-Gaussian contaminations, and find out that 
                         our method successfully classifies < 95% of the tested maps as 
                         being CMB maps containing primordial or secondary non-Gaussianity. 
                         Furthermore, we analyze the foreground-cleaned Planck maps 
                         obtaining constraints for non-Gaussianity at large-angles that are 
                         in good agreement with recent constraints. Finally, we also test 
                         the robustness of our estimator including cut-sky masks and 
                         realistic noise maps measured by Planck, obtaining successful 
                         results as well.",
                  doi = "10.1088/1475-7516/2014/01/018",
                  url = "http://dx.doi.org/10.1088/1475-7516/2014/01/018",
                 issn = "1475-7516",
                label = "self-archiving-INPE-MCTI-GOV-BR",
             language = "en",
           targetfile = "MF+NN_jcap.pdf",
        urlaccessdate = "27 abr. 2024"
}