A Versatile and Accurate Method for Halo Mass Determination from Phase-Space Distribution of Satellite Galaxies

TL;DR

This paper introduces a Bayesian-based method to accurately estimate galaxy halo mass and concentration from satellite galaxy kinematics, effectively handling observational effects and outperforming previous steady-state approaches.

Contribution

It presents a novel, versatile approach that constructs phase-space distribution functions from simulations and applies Bayesian inference for precise halo property estimation.

Findings

Achieves ~20% halo mass accuracy with 20 tracers

Handles observational effects like selection and measurement errors

Small intrinsic uncertainty of ~10% in estimates

Abstract

We propose a versatile and accurate method to estimate the halo mass and concentration from the kinematics of satellite galaxies. We construct the 6D phase-space distribution function of satellites from a cosmological simulation based on the similarity of internal dynamics for different halos. Within the Bayesian statistical framework, not only can we infer the halo mass and concentration efficiently, but also treat various observational effects, including the selection function, incomplete data, and measurement errors, in a rigorous and straightforward manner. Through tests with mock samples, we show that our method is valid and accurate, and more precise than pure steady-state methods. It can constrain the halo mass to within ~ 20% using only 20 tracers and has a small intrinsic uncertainty of ~ 10%. In addition to the clear application to the Milky Way and similar galaxies, our method can be extended to galaxy groups or clusters.