Euclid and KiDS-1000: Quantifying the impact of source-lens clustering on cosmic shear analyses

Cosmic shear is a powerful probe of cosmological models and the transition from current Stage-III surveys such as the Kilo-Degree Survey (KiDS) to the increased area and redshift range of Stage IV surveys such as will significantly increase the precision of weak lensing analyses. However, with increasing precision, the accuracy of model assumptions needs to be evaluated. In this study, we quantify the impact of the correlated clustering of weak lensing source galaxies with the surrounding large-scale structure, known as source-lens clustering (SLC), which is commonly neglected. We include the impact of realistic scatter in photometric redshift estimates, which impacts the assignment of galaxies to tomographic bins and increases the SLC . For this, we use simulated cosmological datasets with realistically distributed galaxies and measure shear correlation functions for both clustered and uniformly distributed source galaxies. Cosmological analyses are performed for both scenarios to quantify the impact of SLC on parameter inference for a KiDS-like and a setting. We find for Stage III surveys such as KiDS, SLC has a minor impact when accounting for nuisance parameters for intrinsic alignments and shifts of tomographic bins, as these nuisance parameters absorb the effect of SLC, thus changing their original meaning. For KiDS ( the inferred intrinsic alignment amplitude $A_ IA $ changes from $0.11_ $) for data without SLC to $0.28_ $) with SLC. However, fixed nuisance parameters lead to shifts in $S_8$ and $ m $, emphasizing the need for including SLC in the modelling. For we find that $ m $, and $w_0$ are shifted by 0.19, 0.12, and 0.12 sigma , respectively when including free nuisance parameters and by 0.20, 0.16, and 0.32 sigma when fixing the nuisance parameters. Consequently, SLC on its own has only a small impact on the inferred parameter inference when using uninformative priors for nuisance parameters. However, SLC might conspire with the breakdown of other modelling assumptions, such as magnification bias or source obscuration, which could collectively exert a more pronounced effect on inferred parameters.