Action-based dynamical models of M31-like galaxies

TL;DR

This paper develops an action-based dynamical model for M31-like galaxies, accurately constraining their mass distribution and stellar halo properties using a Bayesian approach and testing it on simulated data.

Contribution

It introduces a novel action-based equilibrium model with a Bayesian fitting algorithm for M31-like galaxies, validated on mock and simulated data.

Findings

Model achieves ~10% error in total mass estimation out to 100 kpc.

Double-power law distribution function effectively describes stellar halos.

Model confirms equilibrium assumption and analyzes anisotropy profiles.

Abstract

In this work, we present an action-based dynamical equilibrium model to constrain the phase-space distribution of stars in the stellar halo, present-day dark matter distribution, and the total mass distribution in M31-like galaxies. The model comprises a three-component gravitational potential (stellar bulge, stellar disk, and a dark matter halo), and a double-power law distribution function (DF), $f(\mathbf{J})$, which is a function of actions. A Bayesian model-fitting algorithm was implemented that enabled both parameters of the potential and DF to be explored. After testing the model-fitting algorithm on mock data drawn from the model itself, it was applied to a set of three M31-like haloes from the Auriga simulations (Auriga 21, Auriga 23, Auriga 24). Furthermore, we tested the equilibrium assumption and the ability of a double-power law distribution function to represent the stellar halo stars. The model incurs an error in the total enclosed mass of around 10 percent out to 100 kpc, thus justifying the equilibrium assumption. Furthermore, the double-power law DF used proves to be an appropriate description of the investigated M31-like halos. The anisotropy profiles of the halos were also investigated and discussed from a merger history point of view.