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.

翻译：天文学 -- -- 精确测量天体位置和运动的精确度量 -- -- 已成为银河系中暗物质群特征的一个很有希望的途径。通过利用最近在基于模拟的推断和神经网络结构结构方面的进展,我们引入了一种新的方法,在天体测量数据集中寻找全球暗物质引发的重力透镜信号。我们基于神经概率拉动估计的方法显示,对冷暗物质群的敏感度大大提高,与基于两点相关统计数据的现有方法相比,测量噪音的比重更加有利,将机器学习确立为使用天体测量数据确定暗物质特征的有力工具。