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

TL;DR

This paper introduces an optimal iterative lensing spectrum estimator for deep polarization CMB surveys, improving cosmological constraints and neutrino mass measurements while maintaining robustness and computational efficiency.

Contribution

It develops a generalized likelihood-based estimator for CMB lensing that outperforms quadratic estimators in precision and robustness, especially for future deep polarization experiments.

Findings

Estimator is nearly optimal with a diagonal covariance matrix.

For CMB-S4, it doubles lensing amplitude constraints over quadratic estimators.

The method remains robust to modeling uncertainties and is computationally efficient.

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.