Non-Gaussian Signatures in the five-year WMAP data as identified with
  isotropic scaling indices
  We continue the analysis of non-Gaussianities in the CMB by means of the
scaling index method (SIM, Raeth, Schuecker & Banday 2007) by applying this
method on the 5-year WMAP data. We compare each of the results with 1000 Monte
Carlo simulations mimicing the Gaussian properties of the best fit $\Lambda
CDM$-model. Based on the scaling indices, scale-dependent empirical probability
distributions, moments of these distributions and $\chi^2$-combinations of them
are calculated, obtaining similar results as in the former analysis of the
3-year data: We derive evidence for non-Gaussianity with a probability of up to
97.3% for the mean when regarding the KQ75-masked full sky and summing up over
all considered length scales by means of a diagonal $\chi^2$-statistics.
Looking at only the northern or southern hemisphere, we obtain up to 98.5% or
96.6%, respectively. For the standard deviation, these results appear as 95.6%
for the full sky (99.7% north, 89.4% south) and for a $\chi^2$-combination of
both measurements as 97.4% (99.1% north, 95.5% south). By performing an
analysis of rotated hemispheres, we detect an obvious asymmetry in the data. In
addition to these investigations, we present a method of filling the mask with
Gaussian noise to eliminate boundary effects caused by the mask. With the help
of this technique, we identify several local features on the map, of which the
most significant one turns out to be the well-known cold spot. When excluding
all these spots from the analysis, the deviation from Gaussianity increases,
which shows that the discovered local anomalies are not the reason of the
global detection of non-Gaussianity, but actually were damping the deviations
on average. Our analyses per band and per year suggest, however, that it is
very unlikely that the detected anomalies are due to foreground effects.
