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Measuring the escape velocity of the Milky Way is critical in obtaining the mass of the Milky Way, understanding the dark matter velocity distribution, and building the dark matter density profile. In Necib $&$ Lin (2021), we introduced a strategy to robustly measure the escape velocity. Our approach takes into account the presence of kinematic substructures by modeling the tail of the stellar distribution with multiple components, including the stellar halo and the debris flow called the Gaia Sausage (Enceladus). In doing so, we can test the robustness of the escape velocity measurement for different definitions of the tail of the velocity distribution, and the consistency of the data with different underlying models. In this paper, we apply this method to the second data release of Gaia and find that a model with at least two components is preferred. Based on a fit with three bound components to account for the disk, relaxed halo, and the Gaia Sausage, we find the escape velocity of the Milky Way at the solar position to be $v_{rm{esc}}= 484.6^{+17.8}_{-7.4}$ km/s. Assuming a Navarro-Frenck-White dark matter profile, and taken in conjunction with a recent measurement of the circular velocity at the solar position of $v_c = 230 pm 10$ km/s, we find a Milky Way concentration of $c_{200} = 13.8^{+6.0}_{-4.3}$ and a mass of $M_{200} = 7.0^{+1.9}_{-1.2} times 10^{11} M_{odot}$, which is considerably lighter than previous measurements.
The local escape velocity provides valuable inputs to the mass profile of the Galaxy, and requires understanding the tail of the stellar speed distribution. Following Leonard $&$ Tremaine (1990), various works have since modeled the tail of the stell
We measure the escape speed curve of the Milky Way based on the analysis of the velocity distribution of $sim 2850$ counter-rotating halo stars from the Gaia DR2. The distances were estimated through the StarHorse code, and only stars with distance e
The velocity distribution of stars is a sensitive probe of the gravitational potential of the Galaxy, and hence of its dark matter distribution. In particular, the shape of the dark halo (e.g. spherical, oblate, or prolate) determines velocity correl
We model the fastest moving (v_tot > 300 km/s) local (D < 3 kpc) halo stars using cosmological simulations and 6-dimensional Gaia data. Our approach is to use our knowledge of the assembly history and phase-space distribution of halo stars to constra
Until the recent advent of $Gaia$ Data Release 2 (DR2) and deep multi-object spectroscopy, it has been difficult to obtain 6-D phase space information for large numbers of stars beyond 4 kpc, in particular towards the Galactic centre, where dust and