论文标题
Locuss:大型星系簇的飞溅半径及其对聚类合并历史的依赖
LoCuSS: The splashback radius of massive galaxy clusters and its dependence on cluster merger history
论文作者
论文摘要
我们使用本地群集子结构调查(Locuss)的大量星系簇(Locuss)的样本介绍了飞溅功能的直接检测。使用光谱确认的群集成员的k波段幅度获得的堆叠光度密度曲线清楚地检测到此功能(高于$5σ$)。我们通过贝叶斯推断获得了最佳拟合模型,该模型将包括飞溅功能的模型排名为对不允许这种过渡的模型的数据描述。此外,我们已经评估了簇动力学状态对出现飞溅功能的影响。我们利用了广泛的多波长Locuss数据集来测试集群形成历史记录的广泛代理,根据群集输入区域中的X射线发射星系组的存在或不存在X射线发射星系组,找到了Splashback特征位置的最显着依赖性。特别是,我们首次报告未显示大量输入组的群集以较小的簇式半径$ r _ {\ rm {sp}}/r _ {\ rm {\ rm {200,m}} = 1.158 \ PM 0.071 $ clusters clussing cltusters呈现较小的簇式半径$ r _ {\ rm {sp}}/r _ {\ rm _ {\ rm _ {\ rm {200,m} $ r _ {\ rm {sp}}/r _ {\ rm {200,m}} = 1.291 \ pm 0.062 $。这两个子样本之间的差异在$4.2σ$上很显着,这表明群集电位的性质与其积聚率和合并历史之间存在相关性。同样,相对于年轻,更活跃的群集,被归类为旧且动态无效的splashback特征的群集具有更强的签名。我们直接观察群集的基本动力学特性如何在截然不同的物理尺度上回荡。
We present the direct detection of the splashback feature using the sample of massive galaxy clusters from the Local Cluster Substructure Survey (LoCuSS). This feature is clearly detected (above $5σ$) in the stacked luminosity density profile obtained using the K-band magnitudes of spectroscopically confirmed cluster members. We obtained the best-fit model by means of Bayesian inference, which ranked models including the splashback feature as more descriptive of the data with respect to models that do not allow for this transition. In addition, we have assessed the impact of the cluster dynamical state on the occurrence of the splashback feature. We exploited the extensive multi-wavelength LoCuSS dataset to test a wide range of proxies for the cluster formation history, finding the most significant dependence of the splashback feature location and scale according to the presence or absence of X-ray emitting galaxy groups in the cluster infall regions. In particular, we report for the first time that clusters that do not show massive infalling groups present the splashback feature at a smaller clustercentric radius $ r_{\rm{sp}}/r_{\rm{200,m}} = 1.158 \pm 0.071$ than clusters that are actively accreting groups $r_{\rm{sp}}/r_{\rm{200,m}} = 1.291 \pm 0.062$. The difference between these two sub-samples is significant at $4.2σ$, suggesting a correlation between the properties of the cluster potential and its accretion rate and merger history. Similarly, clusters that are classified as old and dynamically inactive present stronger signatures of the splashback feature, with respect to younger, more active clusters. We are directly observing how fundamental dynamical properties of clusters reverberate across vastly different physical scales.