Optimal limits on f_{NL}^{local} from WMAP 5-year data

TL;DR

This paper applies an optimal estimator to WMAP 5-year data to constrain the local non-Gaussianity parameter f_{NL}^{local}, achieving tighter bounds and confirming the absence of foreground contamination.

Contribution

The study introduces an optimal estimator for f_{NL}^{local} and demonstrates its effectiveness on WMAP data, reducing error margins compared to previous methods.

Findings

-4 < f_{NL}^{local} < 80 at 95% CL

Error bars are approximately 40% smaller than previous analyses

No evidence of residual foreground contamination

Abstract

We have applied the optimal estimator for f_{NL}^{local} to the 5 year WMAP data. Marginalizing over the amplitude of foreground templates we get -4 < f_{NL}^{local} < 80 at 95% CL. Error bars of previous (sub-optimal) analyses are roughly 40% larger than these. The probability that a Gaussian simulation, analyzed using our estimator, gives a result larger in magnitude than the one we find is 7%. Our pipeline gives consistent results when applied to the three and five year WMAP data releases and agrees well with the results from our own sub-optimal pipeline. We find no evidence of any residual foreground contamination.