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Local scaling properties of the co-added foreground-cleaned three-year Wilkinson Microwave Anisotropy Probe (WMAP) data are estimated using weighted scaling indices. The scaling index method (SIM) is - for the first time - adapted and applied to the case of spherical symmetric spatial data. The results are compared with 1000 Monte Carlo simulations based on Gaussian fluctuations with a best fit $Lambda$CDM power spectrum and WMAP-like beam and noise properties. Statistical quantities based on the scaling indices, namely the moments of the distribution and probability-based measures are determined. We find for most of the test statistics significant deviations from the Gaussian hypothesis. We find pronounced asymmetries, which can be interpreted as a global lack of structure in the northern hemisphere, which is consistent with previous findings. Furthermore, we detect a localized anomaly in the southern hemisphere, which gives rise to highly significant signature for non-Gaussianity in the spectrum of scaling indices. We identify this signature as the cold spot, which was also already detected in the first year WMAP data. Our results provide further evidence for both the presence of non-Gaussianities and asymmetries in the WMAP three-year data. More detailed bandand year-wise analyses are needed to elucidate the origin of the detected anomalies. In either case the scaling indices provide powerful nonlinear statistics to analyse CMB maps.
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
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
We present an analysis of the foreground emission present in the WMAP 3-year data as determined by the method of Independent Component Analysis. We derived coupling coefficients between the WMAP data and foreground templates which are then used to in
We perform a blind multi-component analysis of the WMAP 1 year foreground cleaned maps using SMICA (Spectral Matching Independent Component Analysis). We provide a new estimate of the CMB power spectrum as well as the amplitude of the CMB anisotropie
We present a model-independent investigation of the WMAP data with respect to scale- dependent non-Gaussianities (NGs) by employing the method of constrained randomization. For generating so-called surrogate maps a shuffling scheme is applied to the