Inferring the gravitational potential of the Milky Way with a few precisely measured stars
- Department of Astronomy, Columbia University, 550 West 120th Street, New York, NY 10027 (United States)
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 data sets 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 eight well-measured stream stars, we can infer properties of a triaxial potential with precisions of the order of 5%-7%. Without proper motions, we obtain 10% constraints on most potential parameters and precisions around 5%-10% for recovering missing phase-space coordinates. These results are encouraging for the 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.
- OSTI ID:
- 22370527
- Journal Information:
- Astrophysical Journal, Vol. 794, Issue 1; Other Information: Country of input: International Atomic Energy Agency (IAEA); ISSN 0004-637X
- Country of Publication:
- United States
- Language:
- English
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