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We derive a fast way for measuring primordial non-Gaussianity in a nearly full-sky map of the cosmic microwave background. We find a cubic combination of sky maps combining bispectrum configurations to capture a quadratic term in primordial fluctuations. Our method takes only N^1.5 operations rather than N^2.5 of the bispectrum analysis (1000 times faster for l=512), retaining the same sensitivity. A key component is a map of underlying primordial fluctuations, which can be more sensitive to the primordial non-Gaussianity than a temperature map. We also derive a fast and accurate statistic for measuring non-Gaussian signals from foreground point sources. The statistic is 10^6 times faster than the full bispectrum analysis, and can be used to estimate contamination from the sources. Our algorithm has been successfully applied to the Wilkinson Microwave Anisotropy Probe sky maps by Komatsu et al. (2003).
Cosmic microwave background observations are most commonly analyzed by estimating the power spectrum. In the limit where the CMB statistics are perfectly Gaussian, this extracts all the information, but the CMB also contains detectable non-Gaussian c
We compute analytically the small-scale temperature fluctuations of the cosmic microwave background from cosmic (super-)strings and study the dependence on the string intercommuting probability $P$. We develop an analytical model which describes the
The angular power spectrum of the cosmic infrared background (CIB) is a sensitive probe of the local primordial bispectrum. CIB measurements are integrated over a large volume so that the scale dependent bias from the primordial non-Gaussianity leave
We implement and investigate a method for measuring departures from scale-invariance, both scale-dependent as well as scale-free, in the primordial power spectrum of density perturbations using cosmic microwave background (CMB) C_l data and a princip
We analyze WMAP 3 year data using the one-point distribution functions to probe the non-Gaussianity in the Cosmic Microwave Background (CMB) Anisotropy data. Computer simulations are performed to determine the uncertainties of the results. We report