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Journal Article

Weak-lensing analysis of SPT-selected galaxy clusters using Dark Energy Survey Science Verification data


Mohr,  J. J.
Optical and Interpretative Astronomy, MPI for Extraterrestrial Physics, Max Planck Society;

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Stern, C., Dietrich, J. P., Bocquet, S., Applegate, D., Mohr, J. J., Bridle, S. L., et al. (2019). Weak-lensing analysis of SPT-selected galaxy clusters using Dark Energy Survey Science Verification data. Monthly Notices of the Royal Astronomical Society, 485(1), 69-87. doi:10.1093/mnras/stz234.

Cite as: https://hdl.handle.net/21.11116/0000-0003-B75C-8
We present weak-lensing (WL) mass constraints for a sample of massive galaxy clusters detected by the South Pole Telescope (SPT) via the Sunyaev–Zel’dovich effect (SZE). We use griz imaging data obtained from the Science Verification (SV) phase of the Dark Energy Survey (DES) to fit the WL shear signal of 33 clusters in the redshift range 0.25 ≤ z ≤ 0.8 with NFW profiles and to constrain a four-parameter SPT mass–observable relation. To account for biases in WL masses, we introduce a WL mass to true mass scaling relation described by a mean bias and an intrinsic, lognormal scatter. We allow for correlated scatter within the WL and SZE mass–observable relations and use simulations to constrain priors on nuisance parameters related to bias and scatter from WL. We constrain the normalization of the ζ−M500 relation, ASZ=12.0+2.6−6.7 when using a prior on the mass slope BSZ from the latest SPT cluster cosmology analysis. Without this prior, we recover ASZ=10.8+2.3−5.2 and BSZ=1.30+0.22−0.44⁠. Results in both cases imply lower cluster masses than measured in previous work with and without WL, although the uncertainties are large. The WL derived value of BSZ is ≈20 per cent lower than the value preferred by the most recent SPT cluster cosmology analysis. The method demonstrated in this work is designed to constrain cluster masses and cosmological parameters simultaneously and will form the basis for subsequent studies that employ the full SPT cluster sample together with the DES data.