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

¹ Ruhr University Bochum, Faculty of Physics and Astronomy, Astronomical Institute (AIRUB), German Centre for Cosmological Lensing, 44780 Bochum, Germany
² Lorentz Institute for Theoretical Physics, Leiden University, 2333 CA Leiden, The Netherlands
³ Leiden Observatory, Leiden University, Einsteinweg 55, 2333 CC Leiden, The Netherlands

^⋆ Corresponding author: shiyang@astro.ruhr-uni-bochum.de

Received: 27 June 2025
Accepted: 27 August 2025

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 simulated 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 a 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 arcmin⁻² over the magnitude range of 20.0 to 27.5, compared to 39.17 arcmin⁻² for LSST-like data alone and 30.31 arcmin⁻² for Euclid-like data alone. Restricting the analysis to only the overlapping sources detected in both surveys resulted in a gain of ∼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.