Using simulation based inference on tidally perturbed dwarf galaxies: the dynamics of NGC205

TL;DR

This paper introduces a Bayesian simulation-based inference method to analyze the dynamics of tidally perturbed dwarf galaxies, demonstrated on NGC205, revealing how velocity profiles inform on orbit and mass density.

Contribution

The paper presents a novel application of implicit likelihood inference with neural density estimators to study the dynamics of tidally disturbed dwarf galaxies, a method previously used mainly in cosmology.

Findings

Velocity profiles can reveal orbit and mass density even without proper motion data.

Mock data shows the method's effectiveness in recovering galaxy parameters.

Limited observational coverage hampers analysis of NGC205, suggesting need for additional data.

Abstract

We develop a novel approach to performing precision inference on tidally perturbed dwarf galaxies. We use a Bayesian inference framework of implicit likelihood inference, previously applied mainly in the field of cosmology, based on forward simulation, data compression, and likelihood emulation with neural density estimators. We consider the case of NGC205, a satellite of M31. NGC205 exhibits an S-shape in the mean line-of-sight velocity along its semi-major spatial axis, suggestive of tidal perturbation. We demonstrate that this velocity profile can be qualitatively reproduced even if NGC205 was in a spherically symmetric and isotropic state before its most recent pericenter passage. We apply our inference method to mock data and show that the precise shape of a perturbed satellite's sky-projected internal velocity field can be highly informative of both its orbit and total mass density profile, even in the absence of proper motion information. For the actual NGC205, our method is hampered because the available data only covers a line along its semi-major axis, rather than the full sky-projected field. This shortcoming could be addressed with another round of observations.