A clustering-based self-calibration of the richness-to-mass relation of CAMIRA galaxy clusters out to $z\approx1.1$ in the Hyper Suprime-Cam survey
I-Non Chiu, Teppei Okumura, Masamune Oguri, Aniket Agrawal, Keiichi, Umetsu, Yen-Ting Lin

TL;DR
This paper presents a self-calibration method for the richness-to-mass relation of galaxy clusters using clustering measurements, accounting for observational effects, and validates the approach with simulations, providing constraints consistent with other methods.
Contribution
The study introduces a novel clustering-based self-calibration technique for the richness-to-mass relation of galaxy clusters, incorporating detailed modeling of observational effects and validation with mock catalogs.
Findings
Constraints on the richness-to-mass normalization with ~20-36% uncertainty.
Results are consistent with independent weak-lensing measurements within ~1.9 sigma.
Clustering-based mass scale is mildly higher than other methods.
Abstract
We perform a self-calibration of the richness-to-mass (-) relation of CAMIRA galaxy clusters with richness at redshift by modeling redshift-space two-point correlation functions. These correlation functions are of CAMIRA clusters, the auto-correlation function of the CMASS galaxies spectroscopically observed in the BOSS survey, and the cross-correlation function between these two samples. We focus on constraining the normalization of the - relation in a forward-modeling approach, carefully accounting for the redshift-space distortion, the Finger-of-God effect, and the uncertainty in photometric redshifts of CAMIRA clusters. The modeling also takes into account the projection effect on the halo bias of CAMIRA clusters. The parameter constraints are shown to be unbiased…
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