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

TL;DR

This study uses needlet-based analysis of WMAP 5-year data to constrain primordial non-Gaussianity, finding results consistent with Gaussianity and improving previous bounds on the non-linear coupling parameter.

Contribution

The paper introduces a needlet-based estimator for $nl$ and applies it to WMAP data, providing improved constraints on primordial non-Gaussianity.

Findings

Constraints on $nl$ are $-80<nl<120$ at 95% CL.

Needlet space three-point correlation improves constraints to $-50<nl<110$.

Results are consistent with Gaussian primordial fluctuations.

Abstract

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 $\fnl$. We apply a third-order estimator of the needlet coefficients skewness and compute the $\chi^2$ statistics of its distribution. We obtain $-80<\fnl<120$ 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 $\fnl$ 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 $\fnl$ improve to $-50<\fnl<110$ at 95% confidence level.