Quantifying the impact of detection bias from blended galaxies on cosmic shear surveys
Eray Genc, Peter Schneider, Sandra Unruh, Tim Schrabback

TL;DR
This paper quantifies how detection bias from blended galaxies in cosmic shear surveys causes an underestimation of shear signals, emphasizing its significance for future precision cosmology.
Contribution
It provides an analytic model for detection probability and estimates the bias impact using simulations, highlighting its importance in cosmic shear analysis.
Findings
Detection bias leads to nearly 2% underestimation of S_8.
Analytic expression for detection probability based on galaxy separation and brightness.
Bias cannot be neglected in current and future surveys.
Abstract
Increasingly large areas in cosmic shear surveys lead to a reduction of statistical errors, necessitating to control systematic errors increasingly better. One of these systematic effects was initially studied by Hartlap et al. in 2011, namely that image overlap with (bright foreground) galaxies may prevent some distant (source) galaxies to remain undetected. Since this overlap is more likely to occur in regions of high foreground density -- which tend to be the regions in which the shear is largest -- this detection bias would cause an underestimation of the estimated shear correlation function. This detection bias adds to the possible systematic of image blending, where nearby pairs or multiplets of images render shear estimates more uncertain and thus may cause a reduction in their statistical weight. Based on simulations with data from the Kilo-Degree Survey, we study the conditions…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Gamma-ray bursts and supernovae · Cosmology and Gravitation Theories
