Impact of projection-induced optical selection bias on the weak lensing mass calibration of galaxy clusters

Weak gravitational lensing signals of optically identified clusters are impacted by a selection bias – halo triaxiality and large-scale structure along the line of sight simultaneously boost the lensing signal and richness (the inferred number of galaxies associated with a cluster). As a result, a cluster sample selected by richness has a mean lensing signal higher than expected from its mean mass, and the inferred mass will be biased high. This selection bias is currently limiting the accuracy of cosmological parameters derived from optical clusters. In this paper, we quantify the bias in mass calibration due to this selection bias. Using two simulations, MiniUchuu and Cardinal, with different galaxy models and cluster finders, we find that the selection bias leads to an overestimation of lensing mass at the 20-50% level, with a larger bias (20-80%) for large-scale lensing (>3 Mpc). Even with a moderate projection model, this selection bias significantly outweighs other currently known cluster lensing systematics. This work confirms the need to account for this bias in future optical cluster cosmology analyses, and we discuss strategies for mitigating this bias.

💡 Research Summary

This paper investigates a previously under‑appreciated source of systematic error in the weak‑lensing mass calibration of optically selected galaxy clusters: a selection bias that arises because the same line‑of‑sight (LOS) projection effects that inflate a cluster’s richness (λ) also boost its lensing signal (ΔΣ). The authors argue that clusters selected by richness therefore have a mean lensing amplitude that is higher than expected for their true mean mass, leading to an over‑estimation of the mass–observable relation and, consequently, biased cosmological constraints.

The study uses two independent mock catalogs to quantify the effect. The first, built on the MiniUchuu N‑body simulation, employs a simple halo‑occupation distribution (HOD) to populate galaxies and defines richness via a counts‑in‑cylinder method with a LOS depth of ±30 h⁻¹ cMpc. The second, the Cardinal mock, uses the ADDGALS algorithm for galaxy assignment and the redMaPPer red‑sequence cluster finder, with a deeper LOS projection of ±100 h⁻¹ cMpc. Both mocks adopt a ΛCDM cosmology (MiniUchuu: Planck‑2018 parameters; Cardinal: Ωₘ≈0.286, σ₈≈0.82) and cover a redshift range 0.2 < z < 0.65. Clusters are binned in richness (λ =