Foreground Mitigation for CMB Lensing with the Global Minimum Variance Quadratic Estimator

TL;DR

This paper enhances CMB lensing reconstruction by extending the global minimum variance quadratic estimator to include foreground mitigation techniques, significantly reducing bias from astrophysical sources in simulated data.

Contribution

It introduces a new extension of the GMV quadratic estimator that incorporates tSZ-deproj and cross-ILC methods for improved foreground mitigation in CMB lensing.

Findings

Bias at low multipoles is reduced from ~4% to <1% with new methods.

Foreground-cleaned lensing maps are suitable for cross-correlation analyses.

Methods are validated on simulated SPT-3G and SO configurations.

Abstract

Weak gravitational lensing of the cosmic microwave background (CMB) is a powerful probe of cosmology, providing insight into structure formation and the evolution of the universe. Current and upcoming CMB experiments such as SPT-3G and the Simons Observatory (SO) provide high-resolution, low-noise temperature and polarization maps that are ideal for lensing reconstruction. The global minimum variance (GMV) quadratic estimator for CMB lensing reduces reconstruction noise over the standard quadratic estimator (SQE). In this work, we extend the GMV framework to incorporate the tSZ-deproj and cross-ILC foreground mitigation techniques, which enhance robustness against contamination from astrophysical sources. For a simulation study using SPT-3G Ext-10k and SO Extended configurations at $\ell_{\mathrm{max}}^T = 3500$, the lensing bias at $L < 1000$ is reduced from $\sim4\%$ with standard GMV and SQE to $2\%$ with tSZ-deproj, and to $< 1\%$ with cross-ILC. These methods enable the construction of foreground-cleaned lensing maps suitable for cross-correlation analyses, with direct relevance for current and future surveys.