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Forecasting the constraints on optical selection bias and projection effects of galaxy cluster lensing with multiwavelength data

Authors :
Zhou, Conghao
Wu, Hao-Yi
Salcedo, Andrés N.
Grandis, Sebastian
Jeltema, Tesla
Leauthaud, Alexie
Costanzi, Matteo
Sunayama, Tomomi
Weinberg, David H.
Zhang, Tianyu
Rozo, Eduardo
To, Chun-Hao
Bocquet, Sebastian
Varga, Tamas
Kwiecien, Matthew
Publication Year :
2023

Abstract

Galaxy clusters identified with optical imaging tend to suffer from projection effects, which impact richness (the number of member galaxies in a cluster) and lensing coherently. Physically unassociated galaxies can be mistaken as cluster members due to the significant uncertainties in their line-of-sight distances, thereby changing the observed cluster richness; at the same time, projection effects alter the weak gravitational lensing signals of clusters, leading to a correlated scatter between richness and lensing at a given halo mass. As a result, the lensing signals for optically selected clusters tend to be biased high. This optical selection bias problem of cluster lensing is one of the key challenges in cluster cosmology. Fortunately, recently available multiwavelength observations of clusters provide a solution. We analyze a simulated data set mimicking the observed lensing of clusters identified by both optical photometry and gas properties, aiming to constrain this selection bias. Assuming a redMaPPer sample from the Dark Energy Survey with South Pole Telescope Sunyaev-Zeldovich effect observations, we find that an overlapping survey of 1300 square deg, 0.2 < z < 0.65, can constrain the average lensing bias to an accuracy of 5 percent. This provides an exciting opportunity for directly constraining optical selection bias from observations. We further show that our approach can remove the optical selection bias from the lensing signal, paving the way for future optical cluster cosmology analyses.<br />Comment: 16 pages, 5 figures. Submitted to PRD

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2312.11789
Document Type :
Working Paper