ScamPy——一个基于子晕聚类与丰度匹配的 Python 接口，用于在暗物质晕/子晕层级上绘制星系
ScamPy -- A sub-halo clustering & abundance matching based Python interface for painting galaxies on the dark matter halo/sub-halo hierarchy
AI总结：
本文提出基于子晕聚类与丰度匹配（SCAM）的Python计算框架ScamPy，在暗物质晕/子晕层级上绘制星系，特别适用于高红移研究，并验证了其在再电离物理量分析中的应用。
摘要：
我们提出了一个计算框架，用于在从N-body模拟获得的Dark Matter Halo/Sub-Halo层级上“绘制”星系。我们使用的方法基于子晕聚类和丰度匹配（SCAM）方案，该方案需要观测我们想要重现的目标（观测）种群的1点和2点统计量。该方法特别针对高红移研究，因此依赖于观测到的高红移星系光度函数和相关特性。核心功能用C++编写，并利用面向对象编程，广泛使用多态性，以实现灵活性和高计算效率。为了拥有一个易于访问的接口，所有库都被封装在Python中，并提供了详尽的文档。我们验证了结果，并给出了一个简单且定量的再电离应用，研究了与星系种群、电离分数和气泡大小分布相关的物理量。
英文摘要：
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.
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