一种基于机器学习的方法，用于在N体模拟中为暗物质晕填充亚结构
A machine learning-based method for populating dark matter halos in N-body simulations with substructure
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中文总结 AI 辅助
提出一种基于机器学习的方法，利用分类器和最近邻搜索，从高分辨率模拟中为低分辨率N体模拟的暗物质晕快速填充亚晕，预测误差低于5%。
中文摘要 AI 辅助
能够高精度解析晕亚结构（亚晕）的暗物质N体模拟，可用于通过星系-晕连接模型生成可靠的中央星系和卫星星系目录。然而，此类模拟计算成本高昂，尤其是在需要大量实现来进行统计分析（如对（统计）宇宙学量的协方差矩阵进行稳健估计）时。在本工作中，我们提出了一种快速方法，用于在给定模拟盒中为暗物质晕填充从高分辨率模拟盒中提取的亚晕，该高分辨率模拟盒具有相同的宇宙学参数但初始条件任意。对于给定测试集中的每个晕，该方法首先使用基于决策树回归器构建的分类器预测其是否包含至少一个高于给定质量阈值的亚晕。然后，对于每个预测的主晕，该方法在选定的晕属性空间中进行最近邻搜索，在给定的高分辨率盒中找到另一个主晕，并适当地将该晕的亚晕映射到测试晕上。通过将该方法应用于不同的测试集，我们预测了各种亚晕群体和子群体的丰度、分布以及三维和投影（二维）两点相关函数。在大多数情况下，我们预测的百分比误差远低于5%。总的来说，该方法可用于在低分辨率模拟盒中为晕填充来自高分辨率模拟的亚晕，也可用于研究在给定的暗物质模拟中哪些晕属性与亚晕丰度和聚集（更强烈地）相关。
英文摘要
Dark matter N-body simulations that resolve halo substructure (subhalos) with high accuracy can be used for generating reliable catalogs of central and satellite galaxies via galaxy-halo connection models. However, such simulations are computationally expensive, especially when large numbers of realizations are required for statistical analyses such as robust estimations of covariance matrices for (statistical) cosmological quantities. In this work, we present a fast method for populating dark matter halos in a given simulation box with subhalos extracted from a high-resolution simulation box with the same cosmology but arbitrary initial conditions. For each halo in a given test set, the method first predicts whether it hosts at least one subhalo above a given mass threshold using a classifier built on a decision tree regressor. Then, for each predicted host, the method finds another host halo in a given high-resolution box, using a nearest neighbor search in the space of selected halo properties, and appropriately maps subhalos from that halo to the test one. By applying the method to different test sets, we make predictions for abundances, distributions and three-dimensional and projected (two-dimensional) two-point correlation functions of various subhalo populations and sub-populations. In most cases, the percent errors of our predictions are well below 5%. In general, the method can be used to populate halos in low-resolution simulation boxes with subhalos from a high-resolution simulation and also to investigate which halo properties are (more strongly) correlated with subhalo abundance and clustering in a given dark matter simulation.
发表机构
- Center for Cosmology and Particle Physics, Department of Physics, New York University（纽约大学物理系宇宙学与粒子物理中心）
- Department of Physics, Sharif University of Technology（谢里夫理工大学物理系）
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