91̽»¨

Abstract:

We describe different methods for estimating the bispectrum of cosmic microwave background data. In particular, we construct a minimum-variance estimator for the flat-sky limit and compare results with previously studied frequentist methods. Application to the MAXIMA data set shows consistency with primordial Gaussianity. Weak quadratic non-Gaussianity is characterized by a tunable parameter fNL, corresponding to non-Gaussianity at a level of ∼10^-5 fNL (the ratio of non-Gaussian to Gaussian terms), and we find limits of fNL = 1500 ± 950 for the minimum-variance estimator and fNL = 2700 ± 1650 for the usual frequentist estimator. These are the tightest limits on primordial non-Gaussianity, which include the full effects of the radiation transfer function.

Abstract:

Using mean relative peculiar velocity measurements for pairs of galaxies, we estimate the cosmological density parameter $\Omega_m$ and the amplitude of density fluctuations $\sigma_8$. Our results suggest that our statistic is a robust and reproducible measure of the mean pairwise velocity and thereby the $\Omega_m$ parameter. We get $\Omega_m = 0.30^{+0.17}_{-0.07}$ and $\sigma_8 = 1.13^{+0.22}_{-0.23}$. These estimates do not depend on prior assumptions on the adiabaticity of the initial density fluctuations, the ionization history, or the values of other cosmological parameters.

Abstract:

The trispectrum of the cosmic microwave background can be used to assess the level of non-Gaussianity on cosmological scales. It probes the fourth order moment, as a function of angular scale, of the probability distribution function of fluctuations and has been shown to be sensitive to primordial non-gaussianity, secondary anisotropies (such as the Ostriker-Vishniac effect) and systematic effects (such as astrophysical foregrounds). In this paper we develop a formalism for estimating the trispectrum from high resolution sky maps which incorporates the impact of finite sky coverage. This leads to a series of operations applied to the data set to minimize the effects of contamination due to the Gaussian component and correlations between estimates at different scales. To illustrate the effect of the estimation process, we apply our procedure to the BOOMERanG data set and show that it is consistent with Gaussianity. This work presents the first estimation of the CMB trispectrum on sub-degree scales.