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arXiv 2205.01129astro-ph.GAhep-ph
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通过无监督机器学习测量银河系暗物质

Measuring Galactic Dark Matter through Unsupervised Machine Learning

Matthew R Buckley, Sung Hak Lim, Eric Putney, David Shih

摘要:

测量太阳附近暗物质的密度分布对暗物质理论和实验都有重要意义。在这项工作中,我们将自回归流应用于来自银河系型星系的真实模拟的恒星,以无监督的方式学习恒星相空间密度及其衍生物。有了这些作为输入,并在动态平衡的假设下,引力加速度场和质量密度可以直接从玻尔兹曼方程计算,而不需要假设圆柱对称或星系质量密度的特定函数形式。我们证明了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分布,即使在运动学测量中存在类似盖亚的误差。

英文摘要:

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.

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