Impact of blending on weak lensing measurements with the Vera C. Rubin Observatory

TL;DR

This paper introduces a new algorithm for detecting galaxy blends in simulated LSST data, demonstrating that removing blends can significantly improve weak lensing measurements crucial for cosmology.

Contribution

A novel catalog matching algorithm called friendly is developed to identify and characterize galaxy blends, reducing bias in weak lensing measurements.

Findings

Removing blends partially corrects the weak lensing signal amplitude.

Blending causes approximately 20% bias in mass estimates from weak lensing.

The algorithm improves the accuracy of galaxy property measurements in deep surveys.

Abstract

Upcoming deep optical surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time will scan the sky to unprecedented depths and detect billions of galaxies. This amount of detections will however cause the apparent superposition of galaxies on the images, called blending, and generate a new systematic error due to the confusion of sources. As consequences, the measurements of individual galaxies properties such as their redshifts or shapes will be impacted, and some galaxies will not be detected. However, galaxy shapes are key quantities, used to estimate masses of large scale structures, such as galaxy clusters, through weak gravitational lensing. This work presents a new catalog matching algorithm, called friendly, for the detection and characterization of blends in simulated LSST data for the DESC Data Challenge 2. By identifying a specific type of blends, we show that removing them from the data may partially correct the amplitude of the $\Delta\Sigma$ weak lensing profile that could be biased low by around 20% due to blending. This would result in impacting clusters weak lensing mass estimate and cosmology.