Speaker
Description
Galaxy clusters are powerful cosmological probes, as their abundance and mass evolution are highly sensitive to the growth of structure. Weak gravitational lensing provides a key mass calibration by measuring the coherent distortion of background galaxy shapes induced by foreground clusters. Achieving accurate lensing mass calibration requires precise measurements of galaxy shapes and redshifts. This is particularly challenging for deep, ground-based surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), where the high source density and atmosphere-limited resolution lead to significant \emph{blending} of galaxy images, affecting more than $60\%$ of detected sources and introducing systematic biases in shape and photometric redshift measurements.
To simultaneously capture these effects, I develop a probabilistic framework introducing the blending entropy $S_b$, a metric quantifying the ambiguity in matching detected objects to true galaxies. Using simulated data from DESC DC2, I characterize blending in LSST-like observations and quantify its impact on cluster lensing measurements.
I demonstrate that imposing $S_b<0.2$ effectively filters out highly blended objects 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.
In addition to results based on DC2 simulations, I will present first applications to LSST Data Preview 1 (DP1) combined with higher-resolution Euclid and HST observations in the ECDFS field, highlighting the importance of blending mitigation for future cluster cosmology studies.