Foreground-immune CMB lensing reconstruction with polarization

TL;DR

This paper extends foreground mitigation techniques to polarization-based CMB lensing reconstruction, demonstrating their effectiveness in reducing biases for future experiments like CMB-S4, especially at small scales.

Contribution

It introduces polarization-adapted source hardening and shear-only estimators, analyzing their bias reduction capabilities in the large-lens limit and through simulations.

Findings

Biases are negligible for Simons Observatory.

Biases can be reduced by up to two orders of magnitude for CMB-S4.

Optimal estimator combinations achieve significant bias mitigation with minimal noise increase.

Abstract

Extragalactic foregrounds are known to generate significant biases in temperature-based CMB lensing reconstruction. Several techniques, which include ``source hardening'' and ``shear-only estimators'' have been proposed to mitigate contamination and have been shown to be very effective at reducing foreground-induced biases. Here we extend both techniques to polarization, which will be an essential component of CMB lensing reconstruction for future experiments, and investigate the ``large-lens'' limit analytically to gain insight on the origin and scaling of foreground biases, as well as the sensitivity to their profiles.Using simulations of polarized point sources, we estimate the expected bias to both Simons Observatory and CMB-S4 like (polarization-based) lensing reconstruction, finding that biases to the former are minuscule while those to the latter are potentially non-negligible at small scales ($L\sim1000-2000$). In particular, we show that for a CMB-S4 like experiment, an optimal linear combination of point-source hardened estimators can reduce the (point-source induced) bias to the CMB lensing power spectrum by up to two orders of magnitude, at a $\sim4\%$ noise cost relative to the global minimum variance estimator.