Determining the Milky Way gravitational potential without selection functions

TL;DR

This paper introduces a new method to determine the Milky Way's gravitational potential that avoids modeling complex spatial selection functions by focusing on the conditional velocity distribution, enabling accurate results with large, complex datasets.

Contribution

The paper presents a variant of the Deep Potential method that models the conditional velocity distribution to bypass the need for spatial selection function modeling.

Findings

Accurately recovers gravitational potential in mock data with complex dust distribution.

Effective for large, complex datasets like Gaia data.

Does not require modeling the spatial selection function.

Abstract

Selection effects, such as interstellar extinction and varying survey depth, complicate efforts to determine the gravitational potential - and thus the distribution of baryonic and dark matter - throughout the Milky Way galaxy using stellar kinematics. We present a new variant of the "Deep Potential" method of determining the gravitational potential from a snapshot of stellar positions and velocities that does not require any modeling of spatial selection functions. Instead of modeling the full six-dimensional phase-space distribution function $f\left(\vec{x},\vec{v}\right)$ of observed kinematic tracers, we model the conditional velocity distribution $p\left(\vec{v}\mid\vec{x}\right)$, which is unaffected by a purely spatial selection function. We simultaneously learn the gravitational potential $\Phi\left(\vec{x}\right)$ and the underlying spatial density of the entire tracer population $n\left(\vec{x}\right)$ - including unobserved stars - using the collisionless Boltzmann equation under the stationarity assumption. The advantage of this method is that unlike the spatial selection function, all of the quantities we model, $p\left(\vec{v}\mid\vec{x}\right)$, $\Phi\left(\vec{x}\right)$, and $n\left(\vec{x}\right)$, typically vary smoothly in both position and velocity. We demonstrate that this "conditional" Deep Potential method is able to accurately recover the gravitational potential in a mock dataset with a complex three-dimensional dust distribution that imprints fine angular structure on the selection function. Because we do not need to model the spatial selection function, our new method can effectively scale to large, complex datasets while using relatively few parameters, and is thus well suited to Gaia data.