Annealing a Follow-up Program: Improvement of the Dark Energy Figure of Merit for Optical Galaxy Cluster Surveys

TL;DR

This paper shows that a carefully planned small follow-up program can greatly enhance dark energy constraints from galaxy cluster surveys by optimizing mass calibration efforts, reducing observational costs, and emphasizing the importance of controlling systematic errors.

Contribution

It introduces a simulated annealing approach to optimize follow-up strategies, significantly improving dark energy figure of merit with fewer resources.

Findings

Optimal follow-up can reduce observational costs by up to tenfold.

Approximately 200 low-redshift clusters can improve dark energy constraints by 50%.

Controlling systematic errors at 5% is crucial for maximizing benefits.

Abstract

The precision of cosmological parameters derived from galaxy cluster surveys is limited by uncertainty in relating observable signals to cluster mass. We demonstrate that a small mass-calibration follow-up program can significantly reduce this uncertainty and improve parameter constraints, particularly when the follow-up targets are judiciously chosen. To this end, we apply a simulated annealing algorithm to maximize the dark energy information at fixed observational cost, and find that optimal follow-up strategies can reduce the observational cost required to achieve a specified precision by up to an order of magnitude. Considering clusters selected from optical imaging in the Dark Energy Survey, we find that approximately 200 low-redshift X-ray clusters or massive Sunyaev-Zel'dovich clusters can improve the dark energy figure of merit by 50%, provided that the follow-up mass measurements involve no systematic error. In practice, the actual improvement depends on (1) the uncertainty in the systematic error in follow-up mass measurements, which needs to be controlled at the 5% level to avoid severe degradation of the results; and (2) the scatter in the optical richness-mass distribution, which needs to be made as tight as possible to improve the efficacy of follow-up observations.