Inferring the Andromeda Galaxy's mass from its giant southern stream with Bayesian simulation sampling

TL;DR

This paper uses Bayesian simulation sampling with N-body models to accurately infer the mass of the Andromeda Galaxy by analyzing its giant southern stream and associated tidal debris, integrating multiple observational constraints.

Contribution

It introduces a Bayesian sampling approach combined with N-body simulations to constrain M31's mass and progenitor properties from tidal debris data.

Findings

M31's stellar mass at last pericenter is log(M_s/Msun)=9.5±0.1

M31's virial mass is log(M200)=12.3±0.1

Progenitor is unlikely to be M32 or NGC 205, but possibly an undiscovered overdensity

Abstract

M31 has a giant stream of stars extending far to the south and a great deal of other tidal debris in its halo, much of which is thought to be directly associated with the southern stream. We model this structure by means of Bayesian sampling of parameter space, where each sample uses an N-body simulation of a satellite disrupting in M31's potential. We combine constraints on stellar surface densities from the Isaac Newton Telescope survey of M31 with kinematic data and photometric distances. This combination of data tightly constrains the model, indicating a stellar mass at last pericentric passage of log(M_s / Msun) = 9.5+-0.1, comparable to the LMC. Any existing remnant of the satellite is expected to lie in the NE Shelf region beside M31's disk, at velocities more negative than M31's disk in this region. This rules out the prominent satellites M32 or NGC 205 as the progenitor, but an overdensity recently discovered in M31's NE disk sits at the edge of the progenitor locations found in the model. M31's virial mass is constrained in this model to be log(M200) = 12.3+-0.1, alleviating the previous tension between observational virial mass estimates and expectations from the general galactic population and the timing argument. The techniques used in this paper, which should be more generally applicable, are a powerful method of extracting physical inferences from observational data on tidal debris structures.