Beta Dips in the Gaia Era: Simulation Predictions of the Galactic Velocity Anisotropy Parameter (β) for Stellar Halos

Loebman, Sarah R., Valluri, Monica, Hattori, Kohei, Debattista, Victor P orcid iconORCID: 0000-0001-7902-0116, Bell, Eric F., Stinson, Gregory Stewart, Christensen, Charlotte R., Brooks, Alyson, Quinn, Thomas R. et al (2018) Beta Dips in the Gaia Era: Simulation Predictions of the Galactic Velocity Anisotropy Parameter (β) for Stellar Halos. The Astrophysical Journal, 853 (2). p. 196. ISSN 0004-637X

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The velocity anisotropy parameter, β, is a measure of the kinematic state of orbits in the stellar halo, which holds promise for constraining the merger history of the Milky Way (MW). We determine global trends for β as a function of radius from three suites of simulations, including accretion-only and cosmological hydrodynamic simulations. We find that the two types of simulations are consistent and predict strong radial anisotropy ($\langle \beta \rangle \sim 0.7$) for Galactocentric radii greater than 10 kpc. Previous observations of β for the MW's stellar halo claim a detection of an isotropic or tangential "dip" at r ~ 20 kpc. Using the N-body+SPH simulations, we investigate the temporal persistence, population origin, and severity of "dips" in β. We find that dips in the in situ stellar halo are long-lived, while dips in the accreted stellar halo are short-lived and tied to the recent accretion of satellite material. We also find that a major merger as early as z ~ 1 can result in a present-day low (isotropic to tangential) value of β over a broad range of radii and angles. While all of these mechanisms are plausible drivers for the β dip observed in the MW, each mechanism in the simulations has a unique metallicity signature associated with it, implying that future spectroscopic surveys could distinguish between them. Since an accurate knowledge of β(r) is required for measuring the mass of the MW halo, we note that significant transient dips in β could cause an overestimate of the halo's mass when using spherical Jeans equation modeling.

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