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.
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。通过利用基于模拟的推断和神经网络结构的最新进展,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诱导重力透镜信号。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,比基于两点相关统计的现有方法更有利于测量噪音。我们通过显示非三重模型和非模范的天体测量时预期的噪音特征,展示了我们方法在现实世界的可行性。这确立了机器学习作为使用天体测量数据来描述暗物质特征的强大工具。