Analytical Noise Bias Correction for Precise Weak Lensing Shear Inference

TL;DR

This paper presents an analytical noise bias correction method for weak lensing shear measurements, demonstrating its effectiveness in simulations that mimic LSST data, achieving high accuracy without external calibration.

Contribution

The paper introduces an improved analytical noise bias correction integrated into the AnaCal framework, capable of second-order shear recovery and rapid processing, suitable for large-scale surveys.

Findings

Achieves shear bias less than a few tenths of a percent.

Handles complex observational challenges like cosmic rays and PSF variations.

Processes over 1000 galaxies per second.

Abstract

Noise bias is a significant source of systematic error in weak gravitational lensing measurements that must be corrected to satisfy the stringent standards of modern imaging surveys in the era of precision cosmology. This paper reviews the analytical noise bias correction method and provides analytical derivations demonstrating that we can recover shear to its second order using the 'renoising' noise bias correction approach introduced by Metacalibration. We implement this analytical noise bias correction within the AnaCal shear estimation framework and propose several enhancements to the noise bias correction algorithm. We evaluate the improved AnaCal using simulations designed to replicate Rubin LSST imaging data. These simulations feature semi-realistic galaxies and stars, complete with representative distributions of magnitudes and Galactic spatial density. We conduct tests under various observational challenges, including cosmic rays, defective CCD columns, bright star saturation, bleed trails, and spatially variable point spread functions. Our results indicate a multiplicative bias in weak lensing shear recovery of less than a few tenths of a percent, meeting LSST DESC requirements without requiring calibration from external image simulations. Additionally, our algorithm achieves rapid processing, handling one galaxy in less than a millisecond.