论文标题

DS+:识别群集子结构的方法

DS+: a method for the identification of cluster substructures

论文作者

Benavides, Jose A., Biviano, Andrea, Abadi, Mario G.

论文摘要

群集子结构的研究对于确定集群动态状态,组装历史和聚类星系的演变非常重要,并且可以构成对暗物质和宇宙学参数性质的约束。我们提出和测试DS+,这是一种识别和表征集群中组大小子结构的新方法。我们的新方法基于聚类星系的预计位置和视线速度,这是Fressler&Shectman传统方法的改进和扩展(1988)。我们在从Illustristng模拟中提取的群集大小的宇宙学晕圈进行测试,并带有病毒质量$ \ rm {14 \ sillesim \ log(m_ {200}/m _ {\ odot})\ Lessim 14.6} $,其中包含平均$ \ sim $ \ sim 190 $ galaxies。我们还向真实数据集(子弹群集)中介绍了我们的方法的应用。 DS+能够将真实团体星系的$ \ sim 80 \%$确定为子结构的成员,至少有60 \%分配给子结构的星系属于真实组。实际组的物理特性与相应检测到的子结构的物理性质显着相关,尽管具有显着的散射,并且平均被高估了。将DS+方法应用于子弹群,确认了高速碰撞的存在和主要特性,并沿主簇轴识别其他子结构。 DS+被证明是识别簇中的子结构的可靠方法。该方法可以作为Python代码免费提供给社区。

The study of cluster substructures is important for the determination of the cluster dynamical status, assembly history, and the evolution of cluster galaxies, and it allows to set of constraints on the nature of dark matter and cosmological parameters. We present and test DS+, a new method for the identification and characterization of group-sized substructures in clusters. Our new method is based on the projected positions and line-of-sight velocities of cluster galaxies, and it is an improvement and extension of the traditional method of Dressler & Shectman (1988). We test it on cluster-size cosmological halos extracted from the IllustrisTNG simulations, with virial masses $\rm{14 \lesssim \log (M_{200}/M_{\odot}) \lesssim 14.6}$, that contain on average $\sim 190$ galaxies. We also present an application of our method on a real data set, the Bullet cluster. DS+ is able to identify $\sim 80\%$ of real group galaxies as members of substructures, and at least 60\% of the galaxies assigned to substructures belong to real groups. The physical properties of the real groups are significantly correlated with those of the corresponding detected substructures, albeit with significant scatter, and overestimated on average. Application of the DS+ method to the Bullet cluster confirms the presence and main properties of the high-speed collision and identifies other substructures along the main cluster axis. DS+ proves to be a reliable method for the identification of substructures in clusters. The method is made freely available to the community as a Python code.

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