Probabilistic characterization of blending with LSST and application to cluster lensing cosmology
Abstract
Next-generation galaxy surveys, like the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), will deliver unprecedented depth and sky coverage, enabling precise measurements of cosmic probes such as weak lensing and galaxy clustering. However, increased imaging depth leads to significant blending of galaxy images, particularly in dense fields like galaxy clusters. This blending, exacerbated by atmospheric blurring in ground-based observations, contaminates galaxy property measurements and causes source confusion. To simultaneously capture these effects, we develop a probabilistic framework introducing the blending entropy, a metric quantifying the ambiguity in matching detected objects to true galaxies or external reference sources. Using simulated data from the DESC Data Challenge 2 (DC2), we characterize blending in LSST data and quantify its impact on cluster lensing cosmology around cosmoDC2 halos. We demonstrate that imposing a blending entropy threshold of Sb<0.2 effectively filters out highly blended objects (around 25%), which are especially prevalent near the survey's magnitude limit and are associated with higher errors in shape measurements and photometric redshifts. Applying this cut substantially reduces blending-induced biases in cluster lensing profiles and mass estimates, thereby mitigating systematic errors in cosmological parameters---most notably reducing tension in σ8 estimates. Our method is readily generalizable to other static probes and offers a practical path forward for real data analyses, particularly when leveraging overlapping high-resolution datasets from spaced-based missions such as Euclid or the Roman Space Telescope, where these external datasets can act as reference catalogs to improve the identification of blended sources in LSST data.
Related
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BibTeX
@misc{ramel2026probabilistic,
title = {{Probabilistic characterization of blending with LSST and application to cluster lensing cosmology}},
author = {Manon Ramel and Cyrille Doux and Marine Kuna and Michel Aguena and Céline Combet and Shuang Liang and Constantin Payerne and Camille Avestruz and Alex I Malz and Marina Ricci and Nikolina Šarčević},
howpublished = {HAL},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.12158},
eprint = {2609.12158},
archivePrefix = {arXiv},
url = {https://hal.science/hal-05759908v1},
}
RIS
TY - UNPB
TI - Probabilistic characterization of blending with LSST and application to cluster lensing cosmology
AU - Manon Ramel
AU - Cyrille Doux
AU - Marine Kuna
AU - Michel Aguena
AU - Céline Combet
AU - Shuang Liang
AU - Constantin Payerne
AU - Camille Avestruz
AU - Alex I Malz
AU - Marina Ricci
AU - Nikolina Šarčević
PY - 2026
DA - 2026/09/10
PB - HAL
DO - 10.48550/arxiv.2609.12158
UR - https://hal.science/hal-05759908v1
AB - Next-generation galaxy surveys, like the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), will deliver unprecedented depth and sky coverage, enabling precise measurements of cosmic probes such as weak lensing and galaxy clustering. However, increased imaging depth leads to significant blending of galaxy images, particularly in dense fields like galaxy clusters. This blending, exacerbated by atmospheric blurring in ground-based observations, contaminates galaxy property measurements and causes source confusion. To simultaneously capture these effects, we develop a probabilistic framework introducing the blending entropy, a metric quantifying the ambiguity in matching detected objects to true galaxies or external reference sources. Using simulated data from the DESC Data Challenge 2 (DC2), we characterize blending in LSST data and quantify its impact on cluster lensing cosmology around cosmoDC2 halos. We demonstrate that imposing a blending entropy threshold of S_b<0.2 effectively filters out highly blended objects (around 25%), which are especially prevalent near the survey's magnitude limit and are associated with higher errors in shape measurements and photometric redshifts. Applying this cut substantially reduces blending-induced biases in cluster lensing profiles and mass estimates, thereby mitigating systematic errors in cosmological parameters---most notably reducing tension in σ_8 estimates. Our method is readily generalizable to other static probes and offers a practical path forward for real data analyses, particularly when leveraging overlapping high-resolution datasets from spaced-based missions such as Euclid or the Roman Space Telescope, where these external datasets can act as reference catalogs to improve the identification of blended sources in LSST data.
ER -