论文标题
一种新的生成方法，用于来自水力动力模拟的星团
A novel generative method for star clusters from hydro-dynamical simulations
论文作者
论文摘要
大多数恒星以块状和亚型式簇形成。这些特性也出现在恒星形成云的水力动态模拟中，这些模拟提供了一种为$ n- $ n $ noung stellar clusters运行的现实初始条件的方法。但是，在计算时间方面，通过水力学模拟生产大量的初始条件非常昂贵。我们介绍了一种新型技术，该技术以微小的计算成本从给定的水力学模拟样本中生成新的初始条件。特别是，我们应用层次聚类算法来学习恒星之间空间和运动学关系的树表示，其中叶子代表单恒星，节点描述了在越来越大的尺度下群集的结构。通过简单地修改恒星群集的全局结构，在使小规模的属性未经改变时，可以将该过程用作随机生成新恒星的基础。
Most stars form in clumpy and sub-structured clusters. These properties also emerge in hydro-dynamical simulations of star-forming clouds, which provide a way to generate realistic initial conditions for $N-$body runs of young stellar clusters. However, producing large sets of initial conditions by hydro-dynamical simulations is prohibitively expensive in terms of computational time. We introduce a novel technique for generating new initial conditions from a given sample of hydro-dynamical simulations, at a tiny computational cost. In particular, we apply a hierarchical clustering algorithm to learn a tree representation of the spatial and kinematic relations between stars, where the leaves represent the single stars and the nodes describe the structure of the cluster at larger and larger scales. This procedure can be used as a basis for the random generation of new sets of stars, by simply modifying the global structure of the stellar cluster, while leaving the small-scale properties unaltered.
