SHELS: Testing Weak Lensing Maps with Redshift Surveys

TL;DR

This study assesses the effectiveness of weak lensing maps in detecting galaxy clusters by combining a dense redshift survey with lensing data, revealing limitations and the need for multi-method approaches.

Contribution

It provides a detailed evaluation of weak lensing detection efficiency and completeness using the SHELS redshift survey and DLS lensing map, highlighting detection challenges.

Findings

Only 4 of 12 high signal-to-noise peaks correspond to massive clusters.

Approximately 50% of known massive clusters are detected in weak lensing maps.

Some known clusters are undetected or only detectable with additional structure.

Abstract

Weak lensing surveys are emerging as an important tool for the construction of "mass selected" clusters of galaxies. We evaluate both the efficiency and completeness of a weak lensing selection by combining a dense, complete redshift survey, the Smithsonian Hectospec Lensing Survey (SHELS), with a weak lensing map from the Deep Lens Survey (DLS). SHELS includes 11,692 redshifts for galaxies with R < 20.6 in the four square degree DLS field; the survey is a solid basis for identifying massive clusters of galaxies with redshift z < 0.55. The range of sensitivity of the redshift survey is similar to the range for the DLS convergence map. Only four the twelve convergence peaks with signal-to-noise > 3.5 correspond to clusters of galaxies with M > 1.7 x 10^14 solar masses. Four of the eight massive clusters in SHELS are detected in the weak lensing map yielding a completeness of roughly 50%. We examine the seven known extended cluster x-ray sources in the DLS field: three can be detected in the weak lensing map, three should not be detected without boosting from superposed large-scale structure, and one is mysteriously undetected even though its optical properties suggest that it should produce a detectable lensing signal. Taken together, these results underscore the need for more extensive comparisons among different methods of massive cluster identification.