On the optimality of the spherical Mexican hat wavelet estimator for the primordial non-Gaussianity

TL;DR

This paper evaluates the spherical Mexican hat wavelet's effectiveness in detecting primordial non-Gaussianity in the CMB, demonstrating it performs as well as the optimal bispectrum estimator even with real-world data complexities.

Contribution

It introduces a wavelet-based estimator for primordial non-Gaussianity and compares its performance to the bispectrum, showing comparable efficiency in ideal and realistic conditions.

Findings

Wavelet cubic statistics are as efficient as the bispectrum for detecting non-Gaussianity.

The wavelet estimator performs well with incomplete sky coverage and inhomogeneous noise.

The method's variance matches that of the primary bispectrum estimator.

Abstract

We study the spherical Mexican hat wavelet (SMHW) as a detector of primordial non-Gaussianity of the local type on the Cosmic Microwave Background (CMB) anisotropies. For this purpose we define third order statistics based on the wavelet coefficient maps and the original map. We find the dependence of these statistics in terms of the non-linear coupling parameter fnl and the bispectrum of this type of non-Gaussianity. We compare the analytical values for these statistics with the results obtained with non-Gaussian simulations for an ideal full-sky CMB experiment without noise. We study the power of this method to detect fnl, i. e. the variance of this parameter, and compare it with the variance obtained from the primary bispectrum for the same experiment. Finally we apply our wavelet based estimator on WMAP-like maps with incomplete sky and inhomogeneous noise and compare with the optimal bispectrum estimator. The results show that the wavelet cubic statistics are as efficient as the bispectrum as optimal detectors of this type of primordial non-Gaussianity.