Analysing large scale structure: II. Testing for primordial non-Gaussianity in CMB maps using surrogates

Abstract

The identification of non-Gaussian signatures in cosmic microwave background (CMB) temperature maps is one of the main cosmological challenges today. We propose and investigate altenative methods to analyse CMB maps. Using the technique of constrained randomisation we construct surrogate maps which mimic both the power spectrum and the amplitude distribution of simulated CMB maps containing non-Gaussian signals. Analysing the maps with weighted scaling indices and Minkowski functionals yield in both cases statistically significant identification of the primordial non-Gaussianities. We demonstrate that the method is very robust with respect to noise. We also show that Minkowski functionals are able to account for non-linearities at higher noise level when applied in combination with surrogates than when only applied to noise added CMB maps and phase randomised versions of them, which only reproduce the power spectrum.

17 pages, 12 figures, MNRAS, accepted for publication

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• Non-Gaussian Signatures in the five-year WMAP data as identified with isotropic scaling indices
• Scale dependent non-Gaussianities in the CMB data identified with Minkowski Functionals and Scaling Indices
• Scale-dependent non-Gaussianities in the WMAP data as identified by using surrogates and scaling indices
• Characterization of Anisotropic Gaussian Random Fields by Minkowski Tensors
• Search for non-Gaussianities in the WMAP data with the Scaling Index Method