Stacking Caustic Masses from Galaxy Clusters

TL;DR

This paper introduces a stacking caustic technique to improve galaxy cluster mass estimates from low-sampling spectroscopic data, significantly reducing mass scatter in ensemble measurements.

Contribution

The authors develop a new stacking algorithm based on the caustic method that enhances mass estimation accuracy for galaxy clusters with limited galaxy data.

Findings

Reduces mass scatter from 70% to less than 10% in ensemble clusters.

Identifies escape-velocity edges with over 1000 galaxies per ensemble.

Minimal bias introduced by binning and sampling in mass estimates.

Abstract

Ongoing and future spectroscopic surveys will measure numerous galaxy redshifts within tens of thousands of galaxy clusters. However, the sampling within these clusters will be low, 15 < N < 50 per cluster. With such data, it will be difficult to achieve accurate and precise mass estimates for individual clusters using phase-space mass estimation techniques. We develop and test a new stacking algorithm based on the caustic technique, which reduces the mass scatter in <log M_caustic | M_200 > for ensemble clusters from 70% for individual clusters to less than 10% for ensemble clusters with only 15 galaxies per cluster and 100 clusters per ensemble. With > 1000 galaxies per ensemble phase-space, the escape-velocity edge becomes readily identifiable and the presence of interloping galaxies is minimized. We develop and test an algorithm to trace the projected phase-space surface directly, which results in minimally biased dynamical mass estimates. We then quantify how binning and sampling affect the phase-space-based mass estimates when using an observational proxy that incorporates realistic mass scatter, like richness, and find the added uncertainty in the binning procedure has minimal influence on the resulting bias and scatter of the stacked mass estimates.