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arXiv 1503.08823astro-ph.CO
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用焦散线方法识别星系团子结构

Identification of galaxy cluster substructures with the Caustic method

Heng Yu, Ana Laura Serra, Antonaldo Diaferio, Marco Baldi

摘要:

我们调查的权力焦散技术识别子结构的星系团光红移数据。焦散线技术的目的是估计质量分布的星系团的半径远远超过维里半径,动力学平衡不成立。该技术的两个副产品是识别集群成员和识别集群子结构。我们测试焦散技术作为一个子结构检测器上的两个样本的150模拟红移调查的集群,集群是从一个大的宇宙学$N$ -体模拟的$Λ$ CDM模型,并有质量的$M_{200} \sim 10^{14} h^{-1} M_{\odot}$和$M_{200} \sim 10^{15} h^{-1} M_{\odot}$在两个样本。我们限制我们的分析子结构中确定的模拟与质量大于$10^{13} h^{-1} M_{\odot}$。通过对$3R_{200}$内200个星系的模拟红移巡天,(1)焦散技术恢复了$\sim 30-50$ \%的真实子结构,(2)焦散技术识别的子结构中有$\sim 15-20$ \%对应于中心星系团的真实子结构,其余部分是周围区域的低质量子结构、星系团或星系团子结构,或不相关星系的偶然排列。这些令人鼓舞的结果表明,焦散技术是一种很有前途的方法来研究复杂的星系团的动力学。

英文摘要:

We investigate the power of the caustic technique for identifying substructures of galaxy clusters from optical redshift data alone. The caustic technique is designed to estimate the mass profile of galaxy clusters to radii well beyond the virial radius, where dynamical equilibrium does not hold. Two by-products of this technique are the identification of the cluster members and the identification of the cluster substructures. We test the caustic technique as a substructure detector on two samples of 150 mock redshift surveys of clusters; the clusters are extracted from a large cosmological $N$-body simulation of a $Λ$CDM model and have masses of $M_{200} \sim 10^{14} h^{-1} M_{\odot}$ and $M_{200} \sim 10^{15} h^{-1} M_{\odot}$ in the two samples. We limit our analysis to substructures identified in the simulation with masses larger than $10^{13} h^{-1} M_{\odot}$. With mock redshift surveys with 200 galaxies within $3R_{200}$, (1) the caustic technique recovers $\sim 30-50$\% of the real substructures, and (2) $\sim 15-20$\% of the substructures identified by the caustic technique correspond to real substructures of the central cluster, the remaining fraction being low-mass substructures, groups or substructures of clusters in the surrounding region, or chance alignments of unrelated galaxies. These encouraging results show that the caustic technique is a promising approach for investigating the complex dynamics of galaxy clusters.

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