Bayesian delensing of CMB temperature and polarization

TL;DR

This paper introduces a novel Bayesian algorithm for jointly estimating the CMB temperature, polarization, lensing potential, and cosmological parameters, promising optimal constraints for future CMB surveys like CMB-S4.

Contribution

The paper presents extsc{LenseFlow}, a new lensing algorithm enabling joint Bayesian inference of CMB fields and parameters, improving upon existing methods for next-generation surveys.

Findings

Validated on flat-sky simulations with non-uniform noise and masking.

Demonstrated potential for improved constraints on tensor-to-scalar ratio r.

Provided a non-perturbative proof of lensing determinant unity in weak-lensing regime.

Abstract

We develop the first algorithm able to jointly compute the maximum {\it a posteriori} estimate of the Cosmic Microwave Background (CMB) temperature and polarization fields, the gravitational potential by which they are lensed, and cosmological parameters such as the tensor-to-scalar ratio, $r$. This is an important step towards sampling from the joint posterior probability function of these quantities, which, assuming Gaussianity of the CMB fields and lensing potential, contains all available cosmological information and would yield theoretically optimal constraints. Attaining such optimal constraints will be crucial for next-generation CMB surveys like CMB-S4, where limits on $r$ could be improved by factors of a few over currently used sub-optimal quadratic estimators. The maximization procedure described here depends on a newly developed lensing algorithm, which we term \textsc{LenseFlow}, and which lenses a map by solving a system of ordinary differential equations. This description has conceptual advantages, such as allowing us to give a simple non-perturbative proof that the lensing determinant is equal to unity in the weak-lensing regime. The algorithm itself maintains this property even on pixelized maps, which is crucial for our purposes and unique to \textsc{LenseFlow} as compared to other lensing algorithms we have tested. It also has other useful properties such as that it can be trivially inverted (i.e. delensing) for the same computational cost as the forward operation, and can be used to compute lensing adjoint, Jacobian, and Hessian operators. We test and validate the maximization procedure on flat-sky simulations covering up to 600\,deg$^2$ with non-uniform noise and masking.