Optimal constraint on g_NL from CMB

TL;DR

The paper introduces an optimal, computationally efficient method to constrain the non-linearity parameter g_NL from CMB data, achieving tighter bounds than previous analyses and providing forecasts for future observations.

Contribution

It presents a new separable estimator for g_NL that combines exact filtering with full covariance, improving constraint precision from CMB data.

Findings

Applied to WMAP 9-year data, obtained g_NL = (-3.3 ± 2.2) × 10^5.

Achieved constraints tighter than previous studies.

Forecasted improved constraints for PLANCK data.

Abstract

An optimal method to constrain the non-linearity parameter g_NL of the local-type non-Gaussianity from CMB data is proposed. Our optimal estimator for g_NL is separable and can be efficiently computed in real space. Combining the exact filtering of CMB maps with the full covariance matrix, our method allows us to extract cosmological information from observed data as much as possible and obtain a tighter constraint on g_NL than previous studies. Applying our method to the WMAP 9-year data, we obtain the constraint g_NL = (-3.3 +- 2.2) 10^5, which is a few times tighter than previous ones. We also make a forecast for PLANCK data by using the Fisher matrix analysis.