Inferring the mass content of galaxy clusters with satellite kinematics and Jeans Anisotropic modeling
Abstract
Satellite galaxies can be used to indicate the dynamical mass of galaxy groups and clusters. In this study, we apply the axis-symmetric Jeans Anisotropic Multi-Gaussian Expansion JAM modeling to satellite galaxies in 28 galaxy clusters selected from the TNG300-1 simulation with halo mass of 10M200/M>14.3. If using true bound satellites as tracers, the best constrained total mass within the half-mass radius of satellites, M(<rhalf), and the virial mass, M200, have average biases of -0.01 and 0.03~dex, with average scatters of 0.11~dex and 0.15~dex. If selecting companions in redshift space with line-of-sight depth of 2,000~km/s, the biases are -0.06 and 0.01~dex, while the scatters are 0.12 and 0.18~dex for M(<rhalf) and M200. By comparing the best-fitting and actual density profiles, we find 29% of best-fitting density profiles show very good agreement with the truth, 32% display over or under estimates at most of the radial range with biased M(<rhalf), and 39% show under/over estimates in central regions and over/under estimates in the outskirts, with good constraints on M(<rhalf), yet most of the best constraints are still consistent with the true profiles within 1-σ statistical uncertainties for the three circumstances. Using a mock DESI Bright Galaxy Survey catalog with the effect of fiber incompleteness, we find DESI fiber assignments and the choice of flux limits barely modify the velocity dispersion profiles and are thus unlikely to affect the dynamical modeling outcomes. Our results show that with current and future deep spectroscopic surveys, JAM can be a powerful tool to constrain the underlying density profiles of individual massive galaxy clusters.
Turn this paper into a full lesson
ArcXiv compiles a staged curriculum from this paper: 8-12 lessons across beginner → advanced, synthesised section guides, visuals, flashcards, a quiz, exercises, and on-demand deep dives per section. Grounded in the abstract, never invented.