Photo-z induced selection biases the cosmic magnification coefficient by factors up to 3, and a Random Forest-based re-calibration of that coefficient yields nearly unbiased convergence reconstructions at bright magnitude cuts.
The Mass-Richness Relation of MaxBCG Clusters from Quasar Lensing Magnification using Variability
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abstract
Accurate measurement of galaxy cluster masses is an essential component not only in studies of cluster physics, but also for probes of cosmology. However, different mass measurement techniques frequently yield discrepant results. The SDSS MaxBCG catalog's mass-richness relation has previously been constrained using weak lensing shear, Sunyaev-Zeldovich (SZ), and X-ray measurements. The mass normalization of the clusters as measured by weak lensing shear is >~25% higher than that measured using SZ and X-ray methods, a difference much larger than the stated measurement errors in the analyses. We constrain the mass-richness relation of the MaxBCG galaxy cluster catalog by measuring the gravitational lensing magnification of type I quasars in the background of the clusters. The magnification is determined using the quasars' variability and the correlation between quasars' variability amplitude and intrinsic luminosity. The mass-richness relation determined through magnification is in agreement with that measured using shear, confirming that the lensing strength of the clusters implies a high mass normalization, and that the discrepancy with other methods is not due to a shear-related systematic measurement error. We study the dependence of the measured mass normalization on the cluster halo orientation. As expected, line-of-sight clusters yield a higher normalization; however, this minority of haloes does not significantly bias the average mass-richness relation of the catalog.
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Meta-Calibration of the Cosmic Magnification Coefficient: Toward Unbiased Weak Lensing Reconstruction by Counting Galaxies
Photo-z induced selection biases the cosmic magnification coefficient by factors up to 3, and a Random Forest-based re-calibration of that coefficient yields nearly unbiased convergence reconstructions at bright magnitude cuts.