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Astrometry -- the precise measurement of positions and motions of celestialobjects -- has emerged as a promising avenue for characterizing the dark matterpopulation in our Galaxy. By leveraging recent advances in simulation-basedinference and neural network architectures, we introduce a novel method tosearch for global dark matter-induced gravitational lensing signatures inastrometric datasets. Our method based on neural likelihood-ratio estimationshows significantly enhanced sensitivity to a cold dark matter population andmore favorable scaling with measurement noise compared to existing approachesbased on two-point correlation statistics. We demonstrate the real-worldviability of our method by showing it to be robust to non-trivial modeled aswell as unmodeled noise features expected in astrometric measurements. Thisestablishes machine learning as a powerful tool for characterizing dark matterusing astrometric data.

Inferring dark matter substructure with astrometric lensing beyond the power spectrum