A model-independent test for scale-dependent non-Gaussianities in the CMB

TL;DR

This paper introduces a model-independent approach using surrogate data and scaling indices to detect scale-dependent non-Gaussianities in the CMB, revealing potential anomalies on large scales.

Contribution

The method preserves the power spectrum while randomizing higher order correlations, enabling detection of non-Gaussianities without relying on specific models.

Findings

Signatures of non-Gaussianities detected on large scales in WMAP data

Method effectively distinguishes non-Gaussian features from Gaussian simulations

Further analysis needed to determine the origin of anomalies

Abstract

We present a model-independent method to test for scale-dependent non-Gaussianities in combination with scaling indices as test statistics. Therefore, surrogate data sets are generated, in which the power spectrum of the original data is preserved, while the higher order correlations are partly randomised by applying a scale-dependent shuffling procedure to the Fourier phases. We apply this method to the WMAP data of the cosmic microwave background (CMB) and find signatures for non-Gaussianities on large scales. Further tests are required to elucidate the origin of the detected anomalies.