  The 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 fNL. We apply it to these sets of CMB
maps and find \gtrsim 98% of chance of positive detection, even for small
intensity local non-Gaussianity like fNL = 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 and find out that
our method successfully classifies ~ 95% of the tested maps. 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.
