Interpolating Masked Weak Lensing Signal with Karhunen-Loeve Analysis

TL;DR

This paper presents a method using Karhunen-Loeve analysis to interpolate and improve the analysis of masked weak lensing shear data, reducing bias and noise effects.

Contribution

It introduces a novel KL-based technique for interpolating shear measurements across masked regions in weak lensing surveys.

Findings

KL method reduces bias from masked regions.

Spurious peak counts from shape noise decreased by ~3 times.

Improves robustness of convergence maps and parameter constraints.

Abstract

We explore the utility of Karhunen Loeve (KL) analysis in solving practical problems in the analysis of gravitational shear surveys. Shear catalogs from large-field weak lensing surveys will be subject to many systematic limitations, notably incomplete coverage and pixel-level masking due to foreground sources. We develop a method to use two dimensional KL eigenmodes of shear to interpolate noisy shear measurements across masked regions. We explore the results of this method with simulated shear catalogs, using statistics of high-convergence regions in the resulting map. We find that the KL procedure not only minimizes the bias due to masked regions in the field, it also reduces spurious peak counts from shape noise by a factor of ~ 3 in the cosmologically sensitive regime. This indicates that KL reconstructions of masked shear are not only useful for creating robust convergence maps from masked shear catalogs, but also offer promise of improved parameter constraints within studies of shear peak statistics.