http://arxiv.org/abs/2110.01620
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, establishing machine learning as a powerful tool for characterizing dark matter using astrometric data.
S. Mishra-Sharma
Wed, 6 Oct 21
8/56
Comments: 10 pages, 3 figures, extended version of paper submitted to the Machine Learning and the Physical Sciences workshop at NeurIPS 2021