CMB lensing reconstruction in the presence of diffuse polarized foregrounds

TL;DR

This study evaluates the impact of polarized Galactic dust on CMB lensing reconstruction and compares methods to mitigate dust bias, demonstrating that template cleaning effectively removes bias under certain conditions.

Contribution

It introduces a detailed analysis of polarized dust contamination in CMB lensing and assesses the effectiveness of component separation and template cleaning methods.

Findings

Polarized dust can significantly bias lensing measurements at low polarization fractions.

Template cleaning method successfully removes dust bias in all tested scenarios.

Accurate estimation of dust spectral index is crucial for bias removal.

Abstract

The measurement and characterization of the lensing of the cosmic microwave background (CMB) is key goal of the current and next generation of CMB experiments. We perform a case study of a three-channel balloon-borne CMB experiment observing the sky at (l,b)=(250deg,-38deg) and attaining a sensitivity of 5.25 muK-arcmin with 8' angular resolution at 150 GHz, in order to assess whether the effect of polarized Galactic dust is expected to be a significant contaminant to the lensing signal reconstructed using the EB quadratic estimator. We find that for our assumed dust model, polarization fractions of about as low as a few percent may lead to a significant dust bias to the lensing convergence power spectrum. We investigated a parametric component separation method, proposed by Stompor et al. (2009), as well as a template cleaning method, for mitigating the effect of this dust bias. The template-based method recovers unbiased convergence power spectrum in all polarization fraction cases we considered, while for the component separation technique we find a dust contrast regime in which the accuracy of the profile likelihood spectral index estimate breaks down, and in which external information on the dust frequency scaling is needed. We propose a criterion for putting a requirement on the accuracy with which the dust spectral index must be estimated or constrained, and demonstrate that if this requirement is met, then the dust bias can be removed.