基于标记点过程的贝叶斯群体发现算法。使用2MRS数据集的方法和可行性研究
Bayesian group finder based on marked point processes. Method and feasibility study using the 2MRS data set
摘要：
星系群和星系团是强大的宇宙学探测器。它们允许研究环境对星系形成的影响。星系群的可靠探测是一个悬而未决的问题，对正在进行的和未来的宇宙学调查很重要。提出了一种基于标记点过程的概率星系群检测算法。星表中星系群的模式被看作是一组随机的相互作用的物体。这些物体的位置和相互作用由概率密度决定。未知的集群模式的估计是由最大化建议的概率密度的对象的配置。采用贝叶斯框架，建议的概率密度最大化使用模拟退火算法。该方法提供了“免费”的附加信息，如观察域中的一个点或两个点分别属于聚类模式的概率。这些辅助工具允许构建测试和技术，以验证和完善检测结果。为了测试所提出的方法的可行性，我们将其应用到研究良好的2MRS数据集。与以前发表的朋友圈（FoF）组查找器相比，建议贝叶斯组查找器给出了总体相似的结果。拟议的贝叶斯群体发现者的星系红移调查进行了测试，但需要更详细的分析，以了解即将到来的宇宙学调查的算法的实际能力。所提出的数学框架允许它很容易适应其他数据集（在天文学和其他科学领域）。在宇宙学中，一个很有前途的应用是在测光星系红移巡天中探测星系群，同时考虑到完整的测光红移后验。（节略）
英文摘要：
Galaxy groups and clusters are formidable cosmological probes. They permit the studying of the environmental effects on galaxy formation. A reliable detection of galaxy groups is an open problem and is important for ongoing and future cosmological surveys. We propose a probabilistic galaxy group detection algorithm based on marked point processes with interactions. The pattern of galaxy groups in a catalogue is seen as a random set of interacting objects. The positions and the interactions of these objects are governed by a probability density. The estimator of the unknown cluster pattern is given by the configuration of objects maximising the proposed probability density. Adopting the Bayesian framework, the proposed probability density is maximised using a simulated annealing algorithm. The method provides "for free" additional information such as the probabilities that a point or two points in the observation domain belong to the cluster pattern, respectively. These supplementary tools allow the construction of tests and techniques to validate and to refine the detection result. To test the feasibility of the proposed methodology, we applied it to the well-studied 2MRS data set. Compared to previously published Friends-of-Friends (FoF) group finders, the proposed Bayesian group finder gives overall similar results. The proposed Bayesian group finder is tested on a galaxy redshift survey, but more detailed analyses are needed to understand the actual capabilities of the algorithm regarding upcoming cosmological surveys. The presented mathematical framework permits adapting it easily for other data sets (in astronomy and in other fields of sciences). In cosmology, one promising application is the detection of galaxy groups in photometric galaxy redshift surveys, while taking into account the full photometric redshift posteriors. (abridged)
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