We demonstrate the feasibility to generate surrogates by Fourier-based methods for an incomplete data set. This is performed for the case of a CMB analysis, where astrophysical foreground emission, mainly present in the Galactic plane, is a major cha
llenge. The shuffling of the Fourier phases for generating surrogates is now enabled by transforming the spherical harmonics into a new set of basis functions that are orthonormal on the cut sky. The results show that non-Gaussianities and hemispherical asymmetries in the CMB as identified in several former investigations, can still be detected even when the complete Galactic plane (|b| < 30{deg}) is removed. We conclude that the Galactic plane cannot be the dominant source for these anomalies. The results point towards a violation of statistical isotropy.
In the recent years, non-Gaussianity and statistical isotropy of the Cosmic Microwave Background (CMB) was investigated with various statistical measures, first and foremost by means of the measurements of the WMAP satellite. In this Review, we focus
on the analyses that were accomplished with a measure of local type, the so-called Scaling Index Method (SIM). The SIM is able to detect structural characteristics of a given data set, and has proven to be highly valuable in CMB analysis. It was used for comparing the data set with simulations as well as surrogates, which are full sky maps generated by randomisation of previously selected features of the original map. During these investigations, strong evidence for non-Gaussianities as well as asymmetries and local features could be detected. In combination with the surrogates approach, the SIM detected the highest significances for non-Gaussianity to date.
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 mimi
cing 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.
