论文标题
来自模式识别技术的星系超级分类器的内部结构
Internal structure of superclusters of galaxies from pattern recognition techniques
论文作者
论文摘要
宇宙的大规模结构（LSS）是一个均匀的星系网络，在密集的复合物，星系的超级群和几乎空的空隙中分开。超级集体是年轻的结构，没有时间通过宇宙时代发展到动态放松的系统中。在内部，它们非常不规则，具有致密的岩心，细丝和星系的周围系统。我们提出了一种使用模式识别技术来绘制星系超级隔离器的内部结构的方法。我们的方法允许：i）识别星系LSS分布中的组和簇； ii）纠正由第三空间协调的部分知识引起的“上帝的手指”投影效应； iii）检测星系的细丝并追踪其骨骼。在本文中，我们介绍算法，讨论自由参数的优化并评估其应用结果。通过这种方法，我们绘制了附近宇宙中42个超级散布器的内部结构（最高为$ z = 0.15 $）。
The Large-Scale Structure (LSS) of the Universe is a homogeneous network of galaxies separated in dense complexes, the superclusters of galaxies, and almost empty voids. The superclusters are young structures that did not have time to evolve into dynamically relaxed systems through the age of the Universe. Internally, they are very irregular, with dense cores, filaments and peripheral systems of galaxies. We propose a methodology to map the internal structure of superclusters of galaxies using pattern recognition techniques. Our approach allows to: i) identify groups and clusters in the LSS distribution of galaxies; ii) correct for the "fingers of God" projection effect, caused by the partial knowledge of the third space coordinate; iii) detect filaments of galaxies and trace their skeletons. In this paper, we present the algorithms, discuss the optimization of the free parameters and evaluate the results of its application. With this methodology, we have mapped the internal structure of 42 superclusters in the nearby universe (up to $z=0.15$).
