Constraints on Primordial Non-Gaussianity from a Needlet Analysis of the WMAP-5 Data

We look for a non-Gaussian signal in the WMAP 5-year temperature anisotropy maps by performing a needlet-based data analysis. We use the foreground-reduced maps obtained by the WMAP team through the optimal combination of the W, V and Q channels, and perform realistic non-Gaussian simulations in order to constrain the non-linear coupling parameter $\text{fnl}$. We apply a third-order estimator of the needlet coefficients skewness and compute the ${\chi }^2$ statistics of its distribution. We obtain $-80<\text{fnl}<120$ at 95% confidence level, which is consistent with a Gaussian distribution and comparable to previous constraints on the non-linear coupling. We then develop an estimator of $\text{fnl}$ based on the same simulations and we find consistent constraints on primordial non-Gaussianity. We finally compute the three point correlation function in needlet space: the constraints on $\text{fnl}$ improve to $-50<\text{fnl}<110$ at 95% confidence level.