Blending in LSST-like image simulations biases galaxy redshift distributions and suppresses small-scale clustering beyond 3 sigma, yet leaves inferred Omega_m and galaxy bias on fiducial linear scales statistically unchanged.
Impact of blending on weak lensing measurements with the Vera C. Rubin Observatory
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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.
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astro-ph.CO 1years
2024 1verdicts
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Galaxy Clustering with LSST: Effects of Number Count Bias from Blending
Blending in LSST-like image simulations biases galaxy redshift distributions and suppresses small-scale clustering beyond 3 sigma, yet leaves inferred Omega_m and galaxy bias on fiducial linear scales statistically unchanged.