论文标题
scampy-基于次级聚类和丰富匹配的基于python界面，用于在暗物质光环/次距离层次上绘制星系
ScamPy -- A sub-halo clustering & abundance matching based Python interface for painting galaxies on the dark matter halo/sub-halo hierarchy
论文作者
论文摘要
我们提出了一个计算框架，用于从N-Body模拟获得的暗物质光环/次距离层次结构上的“绘画”星系。我们使用的方法是基于下半升聚类和丰度匹配（SCAM）方案，该方案需要观察我们要复制的目标（观察到的）人群的1和2分统计。该方法特别针对高红移研究量身定制，因此依赖于观察到的高红移星系光度函数和相关性能。核心功能以C ++编写，并用广泛使用多态性来利用面向对象的编程，以实现灵活性和高计算效率。为了具有易于访问的界面，所有库都包裹在Python中，并提供了广泛的文档。我们验证了我们的结果，并为电离提供了简单而定量的应用，并研究了与星系群，电离分数和气泡大小分布相关的物理量。
We present a computational framework for "painting" galaxies on top of the Dark Matter Halo/Sub-Halo hierarchy obtained from N-body simulations. The method we use is based on the sub-halo clustering and abundance matching (SCAM) scheme which requires observations of the 1- and 2-point statistics of the target (observed) population we want to reproduce. This method is particularly tailored for high redshift studies and thereby relies on the observed high-redshift galaxy luminosity functions and correlation properties. The core functionalities are written in c++ and exploit Object Oriented Programming, with a wide use of polymorphism, to achieve flexibility and high computational efficiency. In order to have an easily accessible interface, all the libraries are wrapped in python and provided with an extensive documentation. We validate our results and provide a simple and quantitative application to reionization, with an investigation of physical quantities related to the galaxy population, ionization fraction and bubble size distribution.
