Inferring the gravitational potential of the Milky Way with a few precisely measured stars

TL;DR

This paper introduces a flexible, probabilistic method for inferring the Milky Way's gravitational potential using tidal streams, achieving high precision with minimal star data and accounting for observational uncertainties.

Contribution

It presents a novel phase-space based approach that does not require action calculations or potential integrability, improving potential inference from limited stellar data.

Findings

With four well-measured stars, potential parameters can be inferred with 5-7% precision.

Without proper motions, constraints are around 15-25%.

Method effectively accounts for observational uncertainties and missing data.

Abstract

The dark matter halo of the Milky Way is expected to be triaxial and filled with substructure. It is hoped that streams or shells of stars produced by tidal disruption of stellar systems will provide precise measures of the gravitational potential to test these predictions. We develop a method for inferring the Galactic potential with tidal streams based on the idea that the stream stars were once close in phase space. Our method can flexibly adapt to any form for the Galactic potential: it works in phase-space rather than action-space and hence relies neither on our ability to derive actions nor on the integrability of the potential. Our model is probabilistic, with a likelihood function and priors on the parameters. The method can properly account for finite observational uncertainties and missing data dimensions. We test our method on synthetic datasets generated from N-body simulations of satellite disruption in a static, multi-component Milky Way including a triaxial dark matter halo with observational uncertainties chosen to mimic current and near-future surveys of various stars. We find that with just four well-measured stream stars, we can infer properties of a triaxial potential with precisions of order 5-7 percent. Without proper motions we obtain 15 percent constraints on potential parameters and precisions around 25 percent for recovering missing phase-space coordinates. These results are encouraging for the eventual goal of using flexible, time-dependent potential models combined with larger data sets to unravel the detailed shape of the dark matter distribution around the Milky Way.