Lensing power spectrum of the Cosmic Microwave Background with deep polarization experiments

Abstract

Precise reconstruction of the cosmic microwave background lensing potential can be achieved with deep polarization surveys by iteratively removing lensing-induced $B$ modes. We introduce a lensing spectrum estimator and its likelihood for such optimal iterative reconstruction. Our modelling share similarities to the state-of-the-art likelihoods for quadratic estimator-based (QE) lensing reconstruction. In particular, we generalize the $N_L^{(0)}$ and $N_L^{(1)}$ lensing biases, and design a realization-dependent spectrum debiaser, making this estimator robust to uncertainties in the data modelling. We demonstrate unbiased recovery of the cosmology using map-based reconstructions, focussing on lensing-only cosmological constraints and neutrino mass measurement in combination with CMB spectra and acoustic oscillation data. We find this spectrum estimator is essentially optimal and with a diagonal covariance matrix. For a CMB-S4 survey, this likelihood can double the constraints on the lensing amplitude compared to the QE on a wide range of scales, while at the same time keeping numerical cost under control and being robust to errors.

10 pages, 7 figures, submitted to PRD

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• CMB-S4: Iterative Internal Delensing and $r$ Constraints
• Improved cosmic microwave background (de-)lensing using general spherical harmonic transforms
• Foreground Separation and Constraints on Primordial Gravitational Waves with the PICO Space Mission
• Real-world CMB lensing quadratic estimator power spectrum response
• Cosmological Probes of Structure Growth and Tests of Gravity
• Robust and efficient CMB lensing power spectrum from polarization surveys
• CMB lensing with shear-only reconstruction on the full sky
• Cluster profiles from beyond-the-QE CMB lensing mass maps
• Lensing reconstruction from the cosmic microwave background polarization with machine learning
• Impact of Galactic non-Gaussian foregrounds on CMB lensing measurements
• Characterizing CMB noise anisotropies from CMB delensing