Integrated Markov Chain Monte Carlo (MCMC) analysis of primordial non-Gaussianity (f_NL) in the recent CMB data

TL;DR

This paper performs an MCMC analysis of primordial non-Gaussianity (f_NL) using WMAP data, simultaneously constraining cosmological parameters to accurately estimate f_NL and its confidence interval.

Contribution

It introduces a joint MCMC approach that accounts for uncertainties in cosmological parameters when estimating primordial non-Gaussianity from CMB data.

Findings

Best-fit f_NL values agree with previous studies

Confidence intervals differ slightly due to non-Gaussian likelihoods

Fisher matrix estimates are less accurate for this analysis

Abstract

We have made a Markov Chain Monte Carlo (MCMC) analysis of primordial non-Gaussianity (f_NL) using the WMAP bispectrum and power spectrum. In our analysis, we have simultaneously constrained f_NL and cosmological parameters so that the uncertainties of cosmological parameters can properly propagate to the f_NL estimation. Investigating the parameter likelihoods deduced from MCMC samples, we find slight deviation from Gaussian shape, which makes a Fisher matrix estimation less accurate. Therefore, we have estimated the confidence interval of f_NL by exploring the parameter likelihood without using the Fisher matrix. We find that the best-fit values of our analysis make a good agreement with other results, but the confidence interval is slightly different.