Large-Scale Polarized Foreground Component Separation for Planck

TL;DR

This paper employs Bayesian methods to analyze simulated Planck data for polarized foreground separation, enabling improved estimation of cosmological parameters like reionization optical depth and tensor-to-scalar ratio.

Contribution

It introduces a Bayesian component separation approach using Gibbs sampling for polarized foregrounds in Planck data, enhancing cosmological parameter estimation accuracy.

Findings

Estimated sigma(tau)~0.004 for tau=0.1

Estimated sigma(r)~0.03 for r=0.1

Set a 95% upper limit r<0.02 for r=0.0

Abstract

We use Bayesian component estimation methods to examine the prospects for large-scale polarized map and cosmological parameter estimation with simulated Planck data assuming simplified white noise properties. The sky signal is parametrized as the sum of the CMB, synchrotron emission, and thermal dust emission. The synchrotron and dust components are modelled as power-laws, with a spatially varying spectral index for synchrotron and a uniform index for dust. Using the Gibbs sampling technique, we estimate the linear polarisation Q and U posterior amplitudes of the CMB, synchrotron and dust maps as well as the two spectral indices in ~4 degree pixels. We use the recovered CMB map and its covariance in an exact pixel likelihood algorithm to estimate the optical depth to reionization tau, the tensor-to-scalar ratio r, and to construct conditional likelihood slices for the EE and BB spectra. Given our foreground model, we find sigma(tau)~0.004 for tau=0.1, sigma(r)~0.03 for a model with r=0.1, and a 95% upper limit of r<0.02 for r=0.0.