What to expect from dynamical modelling of cluster haloes I. The information content of different dynamical tracers

TL;DR

This study evaluates how well different dynamical tracers and models can recover galaxy cluster masses and concentrations, highlighting the impact of steady-state deviations and systematic uncertainties across various tracers.

Contribution

The paper demonstrates the effectiveness and limitations of a generic minimal assumption method in modeling cluster haloes using multiple tracers, including dark matter, galaxies, and gas.

Findings

Dark matter particles provide unbiased halo mass estimates with ~0.17 dex uncertainty.

Satellite galaxies closely follow dark matter in dynamical state, while intracluster stars show larger deviations.

Gas-based models yield similar systematic uncertainties as dark matter tracers.

Abstract

Using hydrodynamical simulations, we study how well the underlying gravitational potential of a galaxy cluster can be modelled dynamically with different types of tracers. In order to segregate different systematics and the effects of varying estimator performances, we first focus on applying a generic minimal assumption method (oPDF) to model the simulated haloes using the full 6-D phasespace information. We show that the halo mass and concentration can be recovered in an ensemble unbiased way, with a stochastic bias that varies from halo to halo, mostly reflecting deviations from steady state in the tracer distribution. The typical systematic uncertainty is $\sim 0.17$ dex in the virial mass and $\sim 0.17$ dex in the concentration as well when dark matter particles are used as tracers. The dynamical state of satellite galaxies are close to that of dark matter particles, while intracluster stars are less in a steady state, resulting in a $\sim$ 0.26 dex systematic uncertainty in mass. Compared with galactic haloes hosting Milky-Way-like galaxies, cluster haloes show a larger stochastic bias in the recovered mass profiles. We also test the accuracy of using intracluster gas as a dynamical tracer modelled through a generalised hydrostatic equilibrium equation, and find a comparable systematic uncertainty in the estimated mass to that using dark matter. Lastly, we demonstrate that our conclusions are largely applicable to other steady-state dynamical models including the spherical Jeans equation, by quantitatively segregating their statistical efficiencies and robustness to systematics. We also estimate the limiting number of tracers that leads to the systematics-dominated regime in each case.