合并还是单片？利用机器学习重建模拟星团的动力学历史
Merged or monolithic? Using machine-learning to reconstruct the dynamical history of simulated star clusters
摘要：
上下文机器学习（ML）通过从数据中学习模式来解决问题，只有有限的或没有人类指导。在天文学中，它主要应用于大型观测数据集，例如星系形态分类。目的。我们将ML应用于星团的引力N体模拟，这些星团要么是通过合并两个祖先形成的，要么是孤立进化的，计划以后识别可能有合并历史的球状星团（GC）。方法.我们从模拟的GC中创建模拟观测，从中测量一组参数（在机器学习领域也称为特征）。在特征空间上进行降维之后，得到的数据点被馈送到各种分类算法。使用重复的随机子采样验证，我们检查是否由算法确定的组对应于合并和单片进化模拟之间的基本物理区别。结果我们考虑的三种算法（C5.0树，k-最近邻和支持向量机）在没有参数调整的情况下都达到了约10%的测试误分类率，支持向量机的性能略优于其他算法。特征空间的第一主成分与聚类集中度相关。如果我们从回归中排除它，算法的性能只会略有降低。
英文摘要：
Context. Machine-Learning (ML) solves problems by learning patterns from data, with limited or no human guidance. In Astronomy, it is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims. We apply ML to gravitational N-body simulations of star clusters that are either formed by merging two progenitors or evolved in isolation, planning to later identify Globular Clusters (GCs) that may have a history of merging from observational data. Methods. We create mock-observations from simulated GCs, from which we measure a set of parameters (also called features in the machine-learning field). After dimensionality reduction on the feature space, the resulting datapoints are fed to various classification algorithms. Using repeated random subsampling validation we check whether the groups identified by the algorithms correspond to the underlying physical distinction between mergers and monolithically evolved simulations. Results. The three algorithms we considered (C5.0 trees, k-nearest neighbour, and support-vector machines) all achieve a test misclassification rate of about 10% without parameter tuning, with support-vector machines slightly outperforming the others. The first principal component of feature space correlates with cluster concentration. If we exclude it from the regression, the performance of the algorithms is only slightly reduced.
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