A Neural-Network based estimator to search for primordial
  non-Gaussianity in Planck CMB maps
  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 $\mathtt{SMICA}$ map we found
${f}_{\rm \,NL} = 33 \pm 23$, at $1\sigma$ 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
${f}_{\rm \,NL} \in [33, 41]$, concomitant with the fact that these maps
manifest distinct features in reported analyses, like having different pixel's
noise intensities.
