Mitigating Foreground Biases in CMB Lensing Reconstruction Using Cleaned Gradients

TL;DR

This paper presents a method to reduce foreground biases in CMB lensing maps by cleaning large-scale temperature gradients, enabling more accurate cosmological measurements with existing multi-frequency data.

Contribution

The authors introduce a gradient cleaning technique that mitigates foreground biases in CMB lensing reconstruction using existing Planck and WMAP data, requiring minimal additional effort.

Findings

Bias to halo mass estimates is eliminated using clean gradients.

Minimal signal-to-noise loss in cross-correlation measurements.

No masking, in-painting, or extensive modeling needed.

Abstract

Reconstructed maps of the lensing convergence of the cosmic microwave background (CMB) will play a major role in precision cosmology in coming years. CMB lensing maps will enable calibration of the masses of high-redshift galaxy clusters and will yield precise measurements of the growth of cosmic structure through cross-correlations with galaxy surveys. During the next decade, CMB lensing reconstruction will rely heavily on temperature data, rather than polarization, thus necessitating a detailed understanding of biases due to extragalactic foregrounds. In the near term, the most significant bias among these is that due to the thermal Sunyaev-Zel'dovich (tSZ) effect. Moreover, high-resolution observations will be available at only a few frequencies, making full foreground cleaning challenging. In this paper, we demonstrate a solution to the foreground bias problem that involves cleaning only the large-scale gradients of the CMB temperature map. We show that the data necessary for tSZ-bias-free CMB lensing maps already exist in the form of large-scale measurements of the CMB across multiple frequencies by the Planck and WMAP satellite experiments. Specifically, we show that the bias to halo masses inferred from CMB lensing is eliminated by the utilization of clean gradients obtained from multi-frequency component separation involving Planck and WMAP data, and that special lensing maps for galaxy cross-correlations can be prepared with only a small penalty in signal-to-noise while requiring no masking, in-painting, modeling, or simulation effort for the tSZ bias. While we focus on cross-correlations, we also show that gradient cleaning can mitigate biases to the CMB lensing autospectrum that arise from the presence of foregrounds in temperature and polarization with minimal loss of signal-to-noise.