Approaching the Cramer-Rao Bound in Weak Lensing with PDF Symmetrization

TL;DR

This paper introduces a method to approach the Cramer-Rao bound in weak lensing shear measurements by symmetrizing the shear estimator's PDF, effectively reducing statistical errors without systematic biases.

Contribution

The authors propose a PDF symmetrization technique that enables shear measurement accuracy to near the Cramer-Rao bound without prior PDF knowledge.

Findings

Method effectively minimizes statistical uncertainty in simulations.

Technique works under various observing conditions.

No systematic errors introduced by the method.

Abstract

Weak lensing statistics is typically measured as weighted sum of shear estimators or their products (shear-shear correlation). The weighting schemes are designed in the hope of minimizing the statistical error without introducing systematic errors. It would be ideal to approach the Cramer-Rao bound (the lower bound of the statistical uncertainty) in shear statistics, though it is generally difficult to do so in practice. The reasons may include: difficulties in galaxy shape measurement, inaccurate knowledge of the probability-distribution-function (PDF) of the shear estimator, misidentification of point sources as galaxies, etc.. Using the shear estimators defined in Zhang et al. (2015), we show that one can overcome all these problems, and allow shear measurement accuracy to approach the Cramer-Rao bound. This can be achieved by symmetrizing the PDF of the shear estimator, or the joint PDF of shear estimator pairs (for shear-shear correlation), without any prior knowledge of the PDF. Using simulated galaxy images, we demonstrate that under general observing conditions, this idea works as expected: it minimizes the statistical uncertainty without introducing systematic error.