2010/09/30 by Jaiseung Kim
Mathematics · Physics and Astronomy · #Cosmology and Gravitation Theories #Galaxies: Formation, Evolution, Phenomena #Statistical and numerical algorithms #astro-ph.CO
paper · pdf · doi:10.1088/1475-7516/2011/04/018
published as JCAP04(2011)018 · v3: mean likelihoods added, v4: 2D likelihood added, typos corrected, v5: the point sharpened
openalex publication_date 2011/04/14 · arxiv created 2011/04/26 · arxiv updated 2011/04/27 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/30
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 into 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.