Non-Gaussianity in SMICA

TL;DR

This paper introduces a new formalism for SMICA that incorporates non-Gaussianity information via bispectrum estimation, improving foreground component analysis in CMB polarization data.

Contribution

The authors develop a binned bispectrum estimator integrated into SMICA for simultaneous multi-component non-Gaussianity analysis, enhancing foreground characterization.

Findings

Bispectrum does not improve power spectrum estimation precision.

Method accurately recovers foreground 3-point correlators.

Framework constrains primordial non-Gaussianity coherently across frequencies.

Abstract

We develop a new formalism for the component separation method Spectral Matching Independent Component Analysis (SMICA) in order to include the information contained in the foregrounds beyond second-order statistics. We also develop a binned bispectrum estimator that works directly using maps of different frequency channels, capable of determining the bispectrum of multiple components at the same time, shifting the traditional approach to non-Gaussianity estimation from a cleaned map to the component separation step, for a better handling of foreground uncertainty. We test our method on 400 E and B polarization simulations based on the LiteBIRD experiment, containing the two main sources of contamination for CMB polarization experiments: polarized dust and synchrotron emission. We show that the bispectrum does not improve the precision of the power spectrum estimation or of the spectral parameters. However, we are capable of recovering the correct 3-point correlator of the foregrounds and standard constraints on primordial non-Gaussianity in a coherent multi-frequency and multi-component framework. The advantage of our approach is that it combines data in an optimal way accounting for the power spectrum and the bispectrum of the various components, which is not true for the standard approach.