Searching for non-Gaussianity in the WMAP data

TL;DR

This paper introduces two new indicators based on skewness and kurtosis to detect non-Gaussianity in WMAP CMB data, revealing deviations in unmasked maps but not in masked ones, thus aiding understanding of early universe physics.

Contribution

The paper proposes novel large-scale non-Gaussianity indicators that produce sky maps, enabling better identification of non-Gaussian features in CMB data.

Findings

No significant non-Gaussianity in masked WMAP maps.

Deviations detected in unmasked K and Ka maps.

Indicators are robust across methods.

Abstract

Some analyses of recent cosmic microwave background (CMB) data have provided hints that there are deviations from Gaussianity in the WMAP CMB temperature fluctuations. Given the far reaching consequences of such a non-Gaussianity for our understanding of the physics of the early universe, it is important to employ alternative indicators in order to determine whether the reported non-Gaussianity is of cosmological origin, and/or extract further information that may be helpful for identifying its causes. We propose two new non-Gaussianity indicators, based on skewness and kurtosis of large-angle patches of CMB maps, which provide a measure of departure from Gaussianity on large angular scales. A distinctive feature of these indicators is that they provide sky maps of non-Gaussianity of the CMB temperature data, thus allowing a possible additional window into their origins. Using these indicators, we find no significant deviation from Gaussianity in the three and five-year WMAP ILC map with KQ75 mask, while the ILC unmasked map exhibit deviation from Gaussianity, quantifying therefore the WMAP team recommendation to employ the new mask KQ75 for tests of Gaussianity. We also use our indicators to test for Gaussianity the single frequency foreground unremoved WMAP three and five-year maps, and show that the K and Ka maps exhibit clear indication of deviation from Gaussianity even with the KQ75 mask. We show that our findings are robust with respect to the details of the method.