Effects of overlapping sources on cosmic shear estimation: Statistical sensitivity and pixel-noise bias

TL;DR

This paper analyzes how overlapping sources (blending) in imaging surveys affect cosmic shear measurements, quantifying the impact on statistical sensitivity and pixel-noise bias, especially for LSST.

Contribution

It introduces a Fisher formalism framework to evaluate blending effects on cosmic shear estimation and extends it to predict noise bias for shape estimators.

Findings

Blending causes at least 1% flux overlap in 62% of detected galaxies.

Blending reduces the effective galaxy number density by approximately 18%.

Upper limit of effective galaxy density is 39.4 per arcmin^2 in r band.

Abstract

In Stage-IV imaging surveys, a significant amount of the cosmologically useful information is due to sources whose images overlap with those of other sources on the sky. The cosmic shear signal is primarily encoded in the estimated shapes of observed galaxies and thus directly impacted by overlaps. We introduce a framework based on the Fisher formalism to analyze effects of overlapping sources (blending) on the estimation of cosmic shear. For the Rubin Observatory Legacy Survey of Space and Time (LSST), we present the expected loss in statistical sensitivity for the ten-year survey due to blending. We find that for approximately 62% of galaxies that are likely to be detected in full-depth LSST images, at least 1% of the flux in their pixels is from overlapping sources. We also find that the statistical correlations between measures of overlapping galaxies and, to a much lesser extent the higher shot noise level due to their presence, decrease the effective number density of galaxies, $N_{eff}$, by $\sim$18%. We calculate an upper limit on $N_{eff}$ of 39.4 galaxies per arcmin$^2$ in $r$ band. We study the impact of varying stellar density on $N_{eff}$ and illustrate the diminishing returns of extending the survey into lower Galactic latitudes. We extend the Fisher formalism to predict the increase in pixel-noise bias due to blending for maximum-likelihood (ML) shape estimators. We find that noise bias is sensitive to the particular shape estimator and measure of ensemble-average shape that is used, and properties of the galaxy that include redshift-dependent quantities such as size and luminosity.