论文标题
Darkmix：用于检测和表征暗物质光晕的混合模型
DarkMix: Mixture Models for the Detection and Characterization of Dark Matter Halos
论文作者
论文摘要
暗物质模拟需要统计技术来正确识别和分类其光晕和结构。非参数解决方案提供了这些结构的目录，但缺乏对基于模型的算法的额外学习，并且可能在合并情况下错误地分类粒子。借助混合模型，我们可以同时将多密度曲线拟合到暗物质模拟中发现的光晕。在这项工作中，我们使用Einasto曲线（Einasto 1965，1968，1969）来模拟在Bolshoi模拟样本中发现的光环（Klypin等，2011），并获得了它们的位置，大小，形状和质量。我们的代码在R统计软件环境中实现，可以在https://github.com/lluishgil/darkmix上访问。
Dark matter simulations require statistical techniques to properly identify and classify their halos and structures. Nonparametric solutions provide catalogs of these structures but lack the additional learning of a model-based algorithm and might misclassify particles in merging situations. With mixture models, we can simultaneously fit multiple density profiles to the halos that are found in a dark matter simulation. In this work, we use the Einasto profile (Einasto 1965, 1968, 1969) to model the halos found in a sample of the Bolshoi simulation (Klypin et al. 2011), and we obtain their location, size, shape and mass. Our code is implemented in the R statistical software environment and can be accessed on https://github.com/LluisHGil/darkmix.
