Machine Learning the 6th Dimension: Stellar Radial Velocities from 5D Phase-Space Correlations

TL;DR

This paper introduces a neural network method to infer missing line-of-sight velocities of stars using 5D Gaia data, enabling better understanding of stellar kinematics before full 6D data is available.

Contribution

The study presents a novel neural network architecture trained on complete data to predict missing velocities from 5D astrometry, improving stellar velocity reconstructions.

Findings

Successfully recovers velocity distributions within ~5 kpc of the Sun.

Accurately reconstructs kinematic substructure in the stellar halo.

Demonstrates effectiveness on a mock Gaia catalog.

Abstract

The Gaia satellite will observe the positions and velocities of over a billion Milky Way stars. In the early data releases, the majority of observed stars do not have complete 6D phase-space information. In this Letter, we demonstrate the ability to infer the missing line-of-sight velocities until more spectroscopic observations become available. We utilize a novel neural network architecture that, after being trained on a subset of data with complete phase-space information, takes in a star's 5D astrometry (angular coordinates, proper motions, and parallax) and outputs a predicted line-of-sight velocity with an associated uncertainty. Working with a mock Gaia catalog, we show that the network can successfully recover the distributions and correlations of each velocity component for stars that fall within ~5 kpc of the Sun. We also demonstrate that the network can accurately reconstruct the velocity distribution of a kinematic substructure in the stellar halo that is spatially uniform, even when it comprises a small fraction of the total star count.