Shrinking the Quadratic Estimator

TL;DR

This paper introduces a robust Bayesian modification to the quadratic estimator for weak lensing in CMB observations, improving statistical performance and robustness against model misspecification.

Contribution

It proposes an adaptive Wiener filter-based modification to the quadratic estimator, enhancing robustness and performance in weak lensing analysis.

Findings

The modified estimator is robust to spectral density misspecification.

Bayesian analysis offers advantages in estimator performance.

The approach improves statistical accuracy in lensing potential estimation.

Abstract

We study a regression characterization for the quadratic estimator of weak lensing, developed by Hu and Okamoto (2001,2002), for cosmic microwave background observations. This characterization motivates a modification of the quadratic estimator by an adaptive Wiener filter which uses the robust Bayesian techniques described in Strawderman (1971) and Berger (1980). This technique requires the user to propose a fiducial model for the spectral density of the unknown lensing potential but the resulting estimator is developed to be robust to misspecification of this model. The role of the fiducial spectral density is to give the estimator superior statistical performance in a "neighborhood of the fiducial model" while controlling the statistical errors when the fiducial spectral density is drastically wrong. Our estimate also highlights some advantages provided by a Bayesian analysis of the quadratic estimator.