贝叶斯聚类分析：CFHTLS档案研究中的聚类分析
Bayesian Cluster Finder: Clusters in the CFHTLS Archive Research Survey
摘要：
在当前和未来的巡天中对星系团的探测能够测量质量与光的比率、聚集特性、星系团丰度，从而限制宇宙学参数。提出了一种基于贝叶斯匹配滤波算法的星系团探测新方法。该方法能够通过根据星系亮度、密度和光度红移的滤波器的最大化，结合考虑颜色-星等关系和BCG-红移关系的星系团先验，确定星系团的位置、红移和丰度。我们通过真实的模拟星系目录测试了该算法，发现对于z $<$ 1.2和比$Λ_{CL}>$ 20更丰富的星系团（Abell Richness $\sim$ 0，M $\sim4\times10^{14} M_{\odot}$），检测是100%完整的，纯度为80%。如果我们不包括先验信息，完整性和纯度保持大致相同，这意味着该方法能够检测星系团有和没有一个明确的红色序列。我们将该算法应用于CFHTLS档案研究调查（CARS）数据，使用相同或更深的数据加上似乎是真实的额外集群恢复与先前发布的类似检测。
英文摘要：
The detection of galaxy clusters in present and future surveys enables measuring mass-to-light ratios, clustering properties, galaxy cluster abundances and therefore, constraining cosmological parameters. We present a new technique for detecting galaxy clusters, which is based on the Matched Filter Algorithm from a Bayesian point of view. The method is able to determine the position, redshift and richness of the cluster through the maximization of a filter depending on galaxy luminosity, density and photometric redshift combined with a galaxy cluster prior that accounts for color-magnitude relations and BCG-redshift relation. We tested the algorithm through realistic mock galaxy catalogs, revealing that the detections are 100% complete and 80% pure for clusters up to z $<$1.2 and richer than $Λ_{CL}>$20 (Abell Richness $\sim$0, M$\sim4\times10^{14} M_{\odot}$). The completeness and purity remains approximately the same if we do not include the prior information, implying that this method is able to detect galaxy cluster with and without a well defined red sequence. We applied the algorithm to the CFHTLS Archive Research Survey (CARS) data, recovering similar detections as previously published using the same or deeper data plus additional clusters which appear to be real.
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