Analysis of the weak lensing mass-richness relation of redMaPPer clusters in the LSST DESC DC2 simulations

Kavli Affiliate: Eli S. Rykoff

| First 5 Authors: Constantin Payerne, Zhuowen Zhang, Michel Aguena, CĂ©line Combet, Thibault Guillemin

| Summary:

Cluster scaling relations are key ingredients in cluster abundance-based
cosmological studies. In optical cluster cosmology, weak gravitational lensing
has proven to be a powerful tool to constrain the cluster mass-richness
relation. This work is conducted as part of the Dark Energy Science
Collaboration (DESC), which aims to analyze the Legacy Survey of Space and Time
(LSST) of Vera C. Rubin Observatory, starting in 2026. Weak lensing-inferred
cluster properties, such as mass, suffer from several sources of bias. In this
paper, we aim to test the impact of modeling choices and observational
systematics in cluster lensing on the inference of the mass-richness relation.
We constrain the mass-richness relation of 3,600 clusters detected by the
redMaPPer algorithm in the cosmoDC2 extra-galactic mock catalog (covering $440$
deg$^2$) of the LSST DESC DC2 simulation, using number count measurements and
stacked weak lensing profiles in several intervals of richness ($20 leq
lambda leq 200$) and redshift ($0.2 leq z leq 1$). By modeling the mean of
the scaling relation as $langle ln lambda|M_{rm 200c}, zrangle =
lnlambda_0 + mu_zlog[(1+z)/(1+0.5)] + mu_m[log_{10}(M_{rm 200c}) –
14.3]$, our baseline constraints are $lnlambda_0 = 3.37pm 0.03$, $mu_z =
0.08pm 0.07$ and $mu_m = 2.18 pm 0.07$. We have found that, for a LSST-like
source galaxy density, our constraints are robust to a change in
concentration-mass relation and dark matter density profile modeling choices,
when source redshifts and shapes are perfectly known. We have found that
photometric redshift uncertainties can introduce bias at the $1sigma$ level,
which can be mitigated by an overall correcting factor, fitted jointly with
scaling parameters. We find that including positive shear-richness covariance
in the fit shifts the results by up to 0.5$sigma$.

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