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Optimal multifrequency weighting for CMB lensing
Optimal multifrequency weighting for CMB lensing
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Optimal multifrequency weighting for CMB lensing
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Optimal multifrequency weighting for CMB lensing
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Optimal multifrequency weighting for CMB lensing
Optimal multifrequency weighting for CMB lensing
Paper

Optimal multifrequency weighting for CMB lensing

2021
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Overview
Extragalactic foregrounds in Cosmic Microwave Background (CMB) temperature maps lead to significant biases in CMB lensing reconstruction if not properly accounted for. Combinations of multi-frequency data have been used to minimize the overall map variance (internal linear combination, or ILC), or specifically null a given foreground, but these are not tailored to CMB lensing. In this paper, we derive an optimal multi-frequency combination to jointly minimize CMB lensing noise and bias. We focus on the standard lensing quadratic estimator, as well as the \"shear-only\" and source-hardened estimators, whose responses to foregrounds differ. We show that an optimal multi-frequency combination is a compromise between the ILC and joint deprojection, which nulls the thermal Sunyaev-Zel'dovich (tSZ) and Cosmic Infrared Background (CIB) contributions. In particular, for a Simons Observatory-like experiment with \\(_max,T=3000\\), we find that profile hardening alone (with the standard ILC) reduces the bias to the lensing power amplitude by \\(40\\%\\), at a \\(20\\%\\) cost in noise, while the bias to the cross-correlation with a LSST-like sample is reduced by nearly an order of magnitude at a \\(10\\%\\) noise cost, relative to the standard quadratic estimator. With a small amount of joint deprojection the bias to the profile hardened estimator can be further reduced to less than half the statistical uncertainty on the respective amplitudes, at a \\(20\\%\\) and \\(5\\%\\) noise cost for the auto- and cross-correlation respectively, relative to the profile hardened estimator with the standard ILC weights. Finally, we explore possible improvements with more aggressive masking and varying \\(_max,T\\).
Publisher
Cornell University Library, arXiv.org