Source-lens clustering and intrinsic-alignment bias of weak-lensing estimators
read the original abstract
We estimate the amplitude of the source-lens clustering bias and of the intrinsic-alignment bias of weak lensing estimators of the two-point and three-point convergence and cosmic-shear correlation functions. We use a linear galaxy bias model for the galaxy-density correlations, as well as a linear intrinsic-alignment model. For the three-point and four-point density correlations, we use analytical or semi-analytical models, based on a hierarchical ansatz or a combination of one-loop perturbation theory with a halo model. For two-point statistics, we find that the source-lens clustering bias is typically several orders of magnitude below the weak lensing signal, except when we correlate a very low-redshift galaxy ($z_2 \la 0.05$) with a higher redshift galaxy ($z_1 \ga 0.5$), where it can reach $10\%$ of the signal for the shear. For three-point statistics, the source-lens clustering bias is typically of order $10\%$ of the signal, as soon as the three galaxy source redshifts are not identical. The intrinsic-alignment bias is typically about $10\%$ of the signal for both two-point and three-point statistics. Thus, both source-lens clustering bias and intrinsic-alignment bias must be taken into account for three-point estimators aiming at a better than $10\%$ accuracy.
This paper has not been read by Pith yet.
Forward citations
Cited by 1 Pith paper
-
Machine-learning applications for weak-lensing cosmology
Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.