论文标题
深度潜力：从相空间的快照中回收重力电位
Deep Potential: Recovering the gravitational potential from a snapshot of phase space
论文作者
论文摘要
银河系动力学领域的主要目标之一是恢复引力潜力领域。映射电势将使我们能够在整个银河系中确定物质的空间分布 - 重生和黑暗。我们提出了一种新的方法，用于从恒星的相位位置的快照中确定重力场，仅基于最小的物理假设。我们首先在观察到的相空间位置的样本上训练归一化流量，从而获得了相位空间分布函数的平滑，可区分的近似值。然后，使用无碰撞的玻尔兹曼方程，我们发现重力电势（由前馈神经网络表示），从而使该分布函数静止。该方法比以前的参数方法更灵活，后者将狭窄的分析模型类别符合数据。这是一种使用丰富的恒星运动学数据集揭示银河系密度结构的有前途的方法，这些数据集将很快可用。
One of the major goals of the field of Milky Way dynamics is to recover the gravitational potential field. Mapping the potential would allow us to determine the spatial distribution of matter - both baryonic and dark - throughout the Galaxy. We present a novel method for determining the gravitational field from a snapshot of the phase-space positions of stars, based only on minimal physical assumptions. We first train a normalizing flow on a sample of observed phase-space positions, obtaining a smooth, differentiable approximation of the phase-space distribution function. Using the collisionless Boltzmann equation, we then find the gravitational potential - represented by a feed-forward neural network - that renders this distribution function stationary. This method is far more flexible than previous parametric methods, which fit narrow classes of analytic models to the data. This is a promising approach to uncovering the density structure of the Milky Way, using rich datasets of stellar kinematics that will soon become available.
