The Effects of Halo Assembly Bias on Self-Calibration in Galaxy Cluster Surveys

TL;DR

This paper investigates how halo assembly bias, a secondary dependence of halo clustering on formation history, affects the accuracy of self-calibration methods in galaxy cluster surveys for constraining dark energy.

Contribution

It analyzes the impact of assembly bias on self-calibration techniques across current and future surveys, highlighting when this bias significantly influences cosmological parameter estimation.

Findings

Assembly bias has minimal impact on SDSS-like surveys.

It may significantly affect DES and LSST surveys due to higher scatter.

Impact on SPT surveys depends on the scatter and correlation levels.

Abstract

Self-calibration techniques for analyzing galaxy cluster counts utilize the abundance and the clustering amplitude of dark matter halos. These properties simultaneously constrain cosmological parameters and the cluster observable-mass relation. It was recently discovered that the clustering amplitude of halos depends not only on the halo mass, but also on various secondary variables, such as the halo formation time and the concentration; these dependences are collectively termed assembly bias. Applying modified Fisher matrix formalism, we explore whether these secondary variables have a significant impact on the study of dark energy properties using the self-calibration technique in current (SDSS) and the near future (DES, SPT, and LSST) cluster surveys. The impact of the secondary dependence is determined by (1) the scatter in the observable-mass relation and (2) the correlation between observable and secondary variables. We find that for optical surveys, the secondary dependence does not significantly influence an SDSS-like survey; however, it may affect a DES-like survey (given the high scatter currently expected from optical clusters) and an LSST-like survey (even for low scatter values and low correlations). For an SZ survey such as SPT, the impact of secondary dependence is insignificant if the scatter is 20% or lower but can be enhanced by the potential high scatter values introduced by a highly correlated background. Accurate modeling of the assembly bias is necessary for cluster self-calibration in the era of precision cosmology.