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

TL;DR

This study assesses how blending affects the synergy between Euclid-like and LSST-like weak-lensing data using image simulations with grid (blending-free) and random (blending-present) galaxy placements. It demonstrates that blending significantly biases shear measurements, especially for LSST-like data, and shows that the strongest catalogue-level synergy arises when combining all galaxies detected in either survey, yielding $n_{eff}=44.08\,\mathrm{arcmin^{-2}}$ over $[20.0,27.5]$ mag compared with $39.17$ and $30.31\,\mathrm{arcmin^{-2}}$ for LSST-like and Euclid-like data alone. The overlapping-object approach provides only ~12% improvement, and the results highlight that pixel-level joint analyses, which can better exploit complementary strengths, are a promising direction for future work. The findings stress the necessity of including realistic blending in simulations and motivate development of unified calibration pipelines and joint-modeling methods to fully harness Euclid-LSST synergy for precise cosmology.

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.

Figures (12)

• Figure 1: Cut-outs of a $1\farcm8 \times 1\farcm2$ region for a 10-year LSST-like survey with randomly placed sources (left) and the corresponding Euclid-like image (right). The PSF of the LSST-like image has FWHM of $0\farcs7$, while that of the Euclid-like image is $0\farcs16$. The effective background noise corresponds to a equivalent surface brightness of 27.50 $\rm mag~arcsec^{-2}$ for the LSST-like images and 24.71 $\rm mag~arcsec^{-2}$ for the Euclid-like images. Detailed information on the image simulation setup is provided in Table \ref{['Tab: setup']}.
• Figure 2: The fraction of jointly detected galaxies as a function of the input $r$-band magnitude. The black solid line represents the results from the random simulations, while the red dashed line corresponds to the grid simulations.
• Figure 3: The ratio of detected number densities between random and grid cases for each survey (blue: Euclid-like; red: LSST-like). For reference, for galaxies with an input magnitude of 25, the mean S/N, computed as the average of FLUX_AUTO / FLUXERR_AUTO, is 7.13 for the Euclid-like survey and 16.20 for the LSST-like survey when galaxies are placed randomly.
• Figure 4: The difference between the measured magnitude and the input magnitude as a function of the input magnitude. Background points show individual galaxies for the all cases. The solid lines trace the median values of the difference for each magnitude bin in the random cases and the dashed lines show the median for the grid cases.
• Figure 5: An example stamp before (left) and after (right) masking. The neighbouring object is masked by replacing its pixels with zero values.