ORCA：过密红序列聚类算法
ORCA: The Overdense Red-sequence Cluster Algorithm
摘要：
我们提出了一种新的集群检测算法设计的全景巡天望远镜和快速响应系统（泛STARRS）的调查，但与任何多波段数据的通用应用程序。该方法不作任何关于星系团性质的预先假设，除了（a）星系团星系颜色的相似性（“红色序列”）和（b）增强的投影表面密度。该探测器有三个主要步骤：（一）通过光度学过滤输入星表以在色星等空间中隔离星系来识别星系团成员，（二）用Voronoi图识别高表面密度区域，（三）用朋友圈技术将星系分组为星系团。当有多种颜色可用时，我们要求系统显示两种颜色的序列。在本文中，我们提出的算法，并证明它在两个数据集。第一个是深斯隆数字巡天赤道条纹（条纹82）的7平方度样本，从中我们检测到97个z<=0.6的集群。得益于更深层次的数据，我们在maxBCG光学选择的集群目录中100%完成（基于较浅的单历元SDSS数据），并发现了另外78个先前未识别的集群。第二个数据集是一个模拟中深巡天（MDS）泛星目录，基于λ CDM模型和半解析星系形成配方。模拟中星系晕成员资格的知识允许量化算法性能。我们在z<=0.6的质量>10^13太阳质量的晕中检测到305个模拟星团，并确定假检测率<1%，与Stripe 82星表的测试一致。该检测器在Lambda-CDM模型簇的恢复中表现良好。（节略）
英文摘要：
We present a new cluster detection algorithm designed for the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) survey but with generic application to any multiband data. The method makes no prior assumptions about the properties of clusters other than (a) the similarity in colour of cluster galaxies (the "red sequence") and (b) an enhanced projected surface density. The detector has three main steps: (i) it identifies cluster members by photometrically filtering the input catalogue to isolate galaxies in colour-magnitude space, (ii) a Voronoi diagram identifies regions of high surface density, (iii) galaxies are grouped into clusters with a Friends-of-Friends technique. Where multiple colours are available, we require systems to exhibit sequences in two colours. In this paper we present the algorithm and demonstrate it on two datasets. The first is a 7 square degree sample of the deep Sloan Digital Sky Survey equatorial stripe (Stripe 82), from which we detect 97 clusters with z<=0.6. Benefiting from deeper data, we are 100% complete in the maxBCG optically-selected cluster catalogue (based on shallower single epoch SDSS data) and find an additional 78 previously unidentified clusters. The second dataset is a mock Medium Deep Survey (MDS) Pan-STARRS catalogue, based on the Lambda-CDM model and a semi-analytic galaxy formation recipe. Knowledge of galaxy-halo memberships in the mock allows a quantification of algorithm performance. We detect 305 mock clusters in haloes with mass >10^13 solar masses at z<=0.6 and determine a spurious detection rate of <1%, consistent with tests on the Stripe 82 catalogue. The detector performs well in the recovery of model Lambda-CDM clusters. (abridged)
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