Correlated Component Analysis for diffuse component separation with error estimation on simulated Planck polarization data

TL;DR

This paper introduces a pipeline for CMB polarization data analysis that accurately estimates and propagates errors from component separation to final power spectra, enhancing reliability of the results.

Contribution

It develops a covariance matrix for component separation errors and demonstrates its application to Planck polarization simulations, improving uncertainty quantification.

Findings

Component separation errors are subdominant to noise for Planck polarization data.

The harmonic domain Correlated Component Analysis outperforms real-space methods.

The pipeline effectively propagates errors to the final power spectra.

Abstract

We present a data analysis pipeline for CMB polarization experiments, running from multi-frequency maps to the power spectra. We focus mainly on component separation and, for the first time, we work out the covariance matrix accounting for errors associated to the separation itself. This allows us to propagate such errors and evaluate their contributions to the uncertainties on the final products.The pipeline is optimized for intermediate and small scales, but could be easily extended to lower multipoles. We exploit realistic simulations of the sky, tailored for the Planck mission. The component separation is achieved by exploiting the Correlated Component Analysis in the harmonic domain, that we demonstrate to be superior to the real-space application (Bonaldi et al. 2006). We present two techniques to estimate the uncertainties on the spectral parameters of the separated components. The component separation errors are then propagated by means of Monte Carlo simulations to obtain the corresponding contributions to uncertainties on the component maps and on the CMB power spectra. For the Planck polarization case they are found to be subdominant compared to noise.