Substructure at High Speed I: Inferring the Escape Velocity in the Presence of Kinematic Substructure

TL;DR

This paper introduces a novel method to infer the Galaxy's escape velocity by modeling the stellar velocity distribution tail as a sum of multiple power laws, effectively accounting for kinematic substructures like the Gaia Sausage.

Contribution

It presents the first approach to incorporate kinematic substructure into escape velocity estimation without relying on restrictive priors.

Findings

Method is robust against kinematic substructures

Successfully models velocity tail with multiple power laws

Enables more accurate escape velocity measurements

Abstract

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 stellar speed distribution as $\propto (v_{\rm{esc}} -v)^k$, where $v_{\rm{esc}}$ is the escape velocity, and $k$ is the slope of the distribution. In such studies, however, these two parameters were found to be largely degenerate and often a narrow prior is imposed on $k$ in order to constrain $v_{\rm{esc}}$. Furthermore, the validity of the power law form is likely to break down in the presence of multiple kinematic substructures. In this paper, we introduce a strategy that for the first time takes into account the presence of kinematic substructure. We model the tail of the velocity distribution as a sum of multiple power laws without imposing strong priors. Using mock data, we show the robustness of this method in the presence of kinematic structure that is similar to the recently-discovered Gaia Sausage. In a companion paper, we present the new measurement of the escape velocity and subsequently the mass of the Milky Way using Gaia DR2 data.