Blending effects on shear measurement synergy between Euclid-like and LSST-like surveys

TL;DR

This study investigates how combining high-resolution space-based and deep ground-based imaging improves shear measurement accuracy in weak gravitational lensing, emphasizing the importance of realistic blending effects and joint analysis strategies.

Contribution

It demonstrates that catalogue-level synergy enhances galaxy detection density and shear measurement, and highlights the potential of pixel-level joint analyses to further improve results.

Findings

Blending causes significant biases, especially in LSST-like data.

Combining all galaxies detected in either survey increases effective galaxy density.

Joint-object approach offers a 12% gain over individual surveys.

Abstract

Weak gravitational lensing is a powerful probe for constraining cosmological parameters, but its success relies on accurate shear measurements. In this paper, we use image simulations to investigate how a joint analysis of high-resolution space-based and deep ground-based imaging can improve shear estimation. We simulate two scenarios: a grid-based setup, where galaxies are placed on a regular grid to mimic an idealised, blending-free scenario, and a random setup, where galaxies are randomly distributed to capture the impact of blending. Comparing these cases, we find that blending introduces significant biases, particularly in LSST-like data due to its larger point spread function. This highlights the importance of including realistic blending effects when evaluating the performance of joint analyses. Using simulations that account for blending, we find that the most effective catalogue-level synergy is achieved by combining all galaxies detected in either survey. This approach yields an effective galaxy number density of $44.08~\rm arcmin^{-2}$ over the magnitude range of 20.0 to 27.5, compared to $39.17~\rm arcmin^{-2}$ for LSST-like data alone and $30.31~\rm arcmin^{-2}$ for \textit{Euclid}-like data alone. Restricting the analysis to only the overlapping sources detected in both surveys results in a gain of ${\sim}12\%$ compared to using either survey alone. While this joint-object approach is suboptimal at the catalogue level, it may become more effective in pixel-level analyses, where a joint fit to individual galaxy shapes can better leverage the complementary strengths of both data sets. Studying such pixel-level combinations, with realistic blending effects properly accounted for, remains a promising direction for future work.