Temperature and Polarization CMB Maps from Primordial non-Gaussianities of the Local Type

TL;DR

This paper presents an algorithm for generating high-resolution non-Gaussian CMB maps in temperature and polarization, and validates the use of fast cubic statistics for estimating primordial non-Gaussianity parameters.

Contribution

The authors develop and validate a new simulation algorithm for non-Gaussian CMB maps and demonstrate the effectiveness of fast cubic estimators for analyzing temperature and polarization data.

Findings

Simulation results agree with theoretical Fisher matrix predictions.

The estimator is unbiased and reliable for f_NL estimation.

Error bars depend on the non-Gaussianity level, f_NL.

Abstract

The forthcoming Planck experiment will provide high sensitivity polarization measurements that will allow us to further tighten the f_NL bounds from the temperature data. Monte Carlo simulations of non-Gaussian CMB maps have been used as a fundamental tool to characterize non-Gaussian signatures in the data, as they allow us to calibrate any statistical estimators and understand the effect of systematics, foregrounds and other contaminants. We describe an algorithm to generate high-angular resolution simulations of non-Gaussian CMB maps in temperature and polarization. We consider non-Gaussianities of the local type, for which the level of non-Gaussianity is defined by the dimensionless parameter, f_NL. We then apply the temperature and polarization fast cubic statistics recently developed by Yadav et al. to a set of non-Gaussian temperature and polarization simulations. We compare our results to theoretical expectations based on a Fisher matrix analysis, test the unbiasedness of the estimator, and study the dependence of the error bars on f_NL. All our results are in very good agreement with theoretical predictions, thus confirming the reliability of both the simulation algorithm and the fast cubic temperature and polarization estimator.