The impact of correlated noise on galaxy shape estimation for weak lensing

TL;DR

This paper investigates how correlated pixel noise affects galaxy shape measurements in weak lensing, demonstrating that accounting for noise correlations in detection significance can mitigate bias differences.

Contribution

It introduces a method to account for noise correlations in detection significance, reducing bias discrepancies in galaxy shape estimation for weak lensing.

Findings

Correlated noise significantly impacts shape measurement bias.

Adjusting detection significance for noise correlations reduces bias differences.

Robustness of the method confirmed across various galaxy and PSF properties.

Abstract

The robust estimation of the tiny distortions (shears) of galaxy shapes caused by weak gravitational lensing in the presence of much larger shape distortions due to the point-spread function (PSF) has been widely investigated. One major problem is that most galaxy shape measurement methods are subject to bias due to pixel noise in the images ("noise bias"). Noise bias is usually characterized using uncorrelated noise fields; however, real images typically have low-level noise correlations due to galaxies below the detection threshold, and some types of image processing can induce further noise correlations. We investigate the effective detection significance and its impact on noise bias in the presence of correlated noise for one method of galaxy shape estimation. For a fixed noise variance, the biases in galaxy shape estimates can differ substantially for uncorrelated versus correlated noise. However, use of an estimate of detection significance that accounts for the noise correlations can almost entirely remove these differences, leading to consistent values of noise bias as a function of detection significance for correlated and uncorrelated noise. We confirm the robustness of this finding to properties of the galaxy, the PSF, and the noise field, and quantify the impact of anisotropy in the noise correlations. Our results highlight the importance of understanding the pixel noise model and its impact on detection significances when correcting for noise bias on weak lensing.