  Astrometry -- the precise measurement of positions and motions of celestial
objects -- has emerged as a promising avenue for characterizing the dark matter
population in our Galaxy. By leveraging recent advances in simulation-based
inference and neural network architectures, we introduce a novel method to
search for global dark matter-induced gravitational lensing signatures in
astrometric datasets. Our method based on neural likelihood-ratio estimation
shows significantly enhanced sensitivity to a cold dark matter population and
more favorable scaling with measurement noise compared to existing approaches
based on two-point correlation statistics. We demonstrate the real-world
viability of our method by showing it to be robust to non-trivial modeled as
well as unmodeled noise features expected in astrometric measurements. This
establishes machine learning as a powerful tool for characterizing dark matter
using astrometric data.
