  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.
