The effect of the linear term on the wavelet estimator of primordial non-Gaussianity
A. Curto, E. Martinez-Gonzalez, R. B. Barreiro

TL;DR
This study assesses the impact of including a linear term correction in wavelet-based estimators of primordial non-Gaussianity using WMAP data, finding minimal improvement and suggesting mean subtraction suffices for optimality.
Contribution
It demonstrates that, for WMAP data, the linear term correction offers negligible benefit, simplifying the wavelet estimator approach for primordial non-Gaussianity analysis.
Findings
Linear term correction improves error-bars by less than 1% for WMAP data.
Mean subtraction achieves near-optimal results without linear correction.
Negligible improvement (0.4%) for Planck simulations with linear correction.
Abstract
In this work we present constraints on different shapes of primordial non-Gaussianity using the Wilkinson Microwave Anisotropy Probe (WMAP) 7-year data and the spherical Mexican hat wavelet fnl estimator including the linear term correction. In particular we focus on the local, equilateral and orthogonal shapes. We first analyse the main statistical properties of the wavelet estimator and show the conditions to reach optimality. We include the linear term correction in our estimators and compare the estimates with the values already published using only the cubic term. The estimators are tested with realistic WMAP simulations with anisotropic noise and the WMAP KQ75 sky cut. The inclusion of the linear term correction shows a negligible improvement (< 1 per cent) in the error-bar for any of the shapes considered. The results of this analysis show that, in the particular case of the…
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