论文标题
通过无监督的机器学习测量银河系
Measuring Galactic Dark Matter through Unsupervised Machine Learning
论文作者
论文摘要
测量太阳能邻域中暗物质的密度曲线对暗物质理论和实验都具有重要意义。在这项工作中，我们将自动回旋流动从对银河系型银河系的现实模拟中的恒星应用于恒星，以无监督的方式学习出色的相位空间密度及其衍生物。将这些作为输入，在动态平衡的假设下，可以直接从玻尔兹曼方程直接计算重力加速场和质量密度，而无需假定银河质量密度的圆柱形对称性或特定功能形式。我们证明我们的方法可以准确地重建模拟星系的质量密度和加速度曲线，即使在运动学测量中存在类似GAIA的误差的情况下。
Measuring the density profile of dark matter in the Solar neighborhood has important implications for both dark matter theory and experiment. In this work, we apply autoregressive flows to stars from a realistic simulation of a Milky Way-type galaxy to learn -- in an unsupervised way -- the stellar phase space density and its derivatives. With these as inputs, and under the assumption of dynamic equilibrium, the gravitational acceleration field and mass density can be calculated directly from the Boltzmann Equation without the need to assume either cylindrical symmetry or specific functional forms for the galaxy's mass density. We demonstrate our approach can accurately reconstruct the mass density and acceleration profiles of the simulated galaxy, even in the presence of Gaia-like errors in the kinematic measurements.
