@Article{NovaesBernFerrWuen:2015:NeBaEs,
author = "Novaes, Camila P. and Berni, A. and Ferreira, Ivan S. and
Wuensche, Carlos Alexandre",
affiliation = "{Observat{\'o}rio Nacional (ON)} and {Observat{\'o}rio Nacional
(ON)} and {Universidade de Bras{\'{\i}}lia (UnB)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "A neural-network based estimator to search for primordial
non-Gaussianity in Planck CMB maps",
journal = "Journal of Cosmology and Astroparticle Physics",
year = "2015",
volume = "2015",
number = "9",
month = "Sept.",
keywords = "CMBR theory, non-gaussianity.",
abstract = "We present an upgraded combined estimator, based on Minkowski
Functionals and Neural Networks, with excellent performance in
detecting primordial non-Gaussianity in simulated maps that also
contain a weighted mixture of Galactic contaminations, besides
real pixel's noise from Planck cosmic microwave background
radiation data. We rigorously test the efficiency of our estimator
considering several plausible scenarios for residual
non-Gaussianities in the foreground-cleaned Planck maps, with the
intuition to optimize the training procedure of the Neural Network
to discriminate between contaminations with primordial and
secondary non-Gaussian signatures. We look for constraints of
primordial local non-Gaussianity at large angular scales in the
foreground-cleaned Planck maps. For the SMICA map we found fNL =
33 ± 23, at 1\σ confidence level, in excellent agreement
with the WMAP-9yr and Planck results. In addition, for the other
three Planck maps we obtain similar constraints with values in the
interval fNL [33, 41], concomitant with the fact that these maps
manifest distinct features in reported analyses, like having
different pixel's noise intensities.",
doi = "10.1088/1475-7516/2015/09/064",
url = "http://dx.doi.org/10.1088/1475-7516/2015/09/064",
issn = "1475-7516",
language = "en",
targetfile = "novaes.pdf",
urlaccessdate = "24 abr. 2024"
}