Mapping gravitational lensing of the CMB using local likelihoods

TL;DR

This paper introduces a Bayesian local likelihood method for mapping the gravitational lensing potential from CMB data, effectively handling observational challenges and avoiding traditional Taylor approximations.

Contribution

The paper presents a novel local Bayesian estimation technique for gravitational lensing mapping that improves robustness and flexibility over existing Fourier-based methods.

Findings

Method successfully reconstructs lensing potential in simulations.

Handles missing data and non-uniform coverage effectively.

Compatible with high-resolution CMB experiments.

Abstract

We present a new estimation method for mapping the gravitational lensing potential from observed CMB intensity and polarization fields. Our method uses Bayesian techniques to estimate the average curvature of the potential over small local regions. These local curvatures are then used to construct an estimate of a low pass filter of the gravitational potential. By utilizing Bayesian/likelihood methods one can easily overcome problems with missing and/or non-uniform pixels and problems with partial sky observations (E and B mode mixing, for example). Moreover, our methods are local in nature which allow us to easily model spatially varying beams and are highly parallelizable. We note that our estimates do not rely on the typical Taylor approximation which is used to construct estimates of the gravitational potential by Fourier coupling. We present our methodology with a flat sky simulation under nearly ideal experimental conditions with a noise level of 1 $\mu K$-arcmin for the temperature field, $\sqrt{2}$ $\mu K$-arcmin for the polarization fields, with an instrumental beam full width at half maximum (FWHM) of 0.25 arcmin.