Non-Gaussian Signatures in the five-year WMAP data as identified with isotropic scaling indices

TL;DR

This paper applies the scaling index method to five-year WMAP data, confirming non-Gaussian features in the CMB with high statistical significance and analyzing local anomalies like the cold spot.

Contribution

It extends previous analysis by applying the SIM to WMAP 5-year data, revealing persistent non-Gaussian signatures and local features, and introduces a mask-filling technique to study boundary effects.

Findings

Evidence for non-Gaussianity with up to 98.5% confidence.

Detection of hemispherical asymmetry in the data.

Identification of local features, including the cold spot.

Abstract

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.