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A Neural-Network based estimator to search for primordial non-Gaussianity in Planck CMB maps | | C. P. Novaes
; A. Bernui
; I. S. Ferreira
; C. A. Wuensche
; | | Date: |
13 Sep 2014 | | Abstract: | We present an upgraded combined estimator, based on Minkowski Functionals and
a Neural Network, 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 ef/ficiency of our
estimator considering several plausible scenarios for 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. With a
validated estimator’s performance, showing more than $97 \%$ of hits in a
variety of cases, we look for constraining the primordial non-Gaussianity in
large angular scales analyses of the Planck maps. For the $mathtt{SMICA}$ map
we found that ${f}_{
m ,NL} = 44 pm 14$, at $2sigma$ confidence level,
which is in excellent agreement with the WMAP-9yr and Planck results. In
addition, for the other three Planck maps we obtain larger constraints with
mean values in the interval ${f}_{
m ,NL} in [59, 77]$, concomitant with the
fact that these maps manifest distinct features in reported analyses, like
having larger pixel’s noise intensity. Moreover, our results confirm, as
indicated by the Planck collaboration, that different amounts of foregrounds
residuals are still present in each one of the foreground-cleaned Planck maps,
and that the $mathtt{SMICA}$ map appears as the cleanest one. | | Source: | arXiv, 1409.3876 | | Services: | Forum | Review | PDF | Favorites | |
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