CMB lensing with shear-only reconstruction on the full sky

Abstract

Reconstruction of gravitational lensing effects in the CMB from current and upcoming surveys is still dominated by temperature anisotropies. Extragalactic foregrounds in temperature maps can induce significant biases in the lensing power spectrum obtained with the standard quadratic estimators. Techniques such as masking cannot remove these foregrounds fully, and the residuals can still lead to large biases if unaccounted for. In this paper, we study the "shear-only" estimator, an example of a class of geometric methods that suppress extragalactic foreground contamination while making only minimal assumptions about foreground properties. The shear-only estimator has only been formulated in the flat-sky limit and so is not easily applied to wide surveys. Here, we derive the full-sky version of the shear-only estimator and its generalisation to an $m=2$ multipole estimator that has improved performance for lensing reconstruction on smaller scales. The multipole estimator is generally not separable, and so is expensive to compute. We explore separable approximations based on a singular-value decomposition, which allow efficient evaluation of the estimator with real-space methods. Finally, we apply these estimators to simulations that include extragalactic foregrounds and verify their efficacy in suppressing foreground biases.

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Cited by in corpus (7)

• The Atacama Cosmology Telescope: DR6 Gravitational Lensing Map and Cosmological Parameters
• The Atacama Cosmology Telescope: A Measurement of the DR6 CMB Lensing Power Spectrum and its Implications for Structure Growth
• Real-world CMB lensing quadratic estimator power spectrum response
• LiteBIRD Science Goals and Forecasts: A full-sky measurement of gravitational lensing of the CMB
• A halo model of extragalactic contamination to CMB lensing, delensing, and cross-correlations
• Delensing for Precision Cosmology: Optimizing Future CMB B-mode Surveys to Constrain r
• Foreground Mitigation for CMB Lensing with the Global Minimum Variance Quadratic Estimator