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arXiv 1109.4483astro-ph.CO
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用SZ团簇的相对质量-红移丰度检验宇宙学模型

Testing cosmological models using relative mass-redshift abundance of SZ clusters

Arman Shafieloo, George F. Smoot

摘要:

最近通过Sunyaev-Zel'dovich观测发现了高红移的大质量星系团,为检验宇宙学模型开辟了一条新途径。众所周知,在一个非常高的红移上检测到一个超大质量的星系团可以排除许多宇宙学模型。然而,由于处理不同的观测偏差使得很难测试假设宇宙学模型的数据的相似性,因此大多数星团数据(除了那些具有高质量红移的数据)在面对具有星团观测的宇宙学模型时保持不变。我们在这里建议,人们可以使用不同质量的星系团在不同的红移的相对丰度来测试宇宙学模型的背景下的数据的相似性。为了这个目的,我们提出了一个简单的参数形式观察集群在不同的质量红移的效率,我们测试如果标准的LCDM模型可以解释观察到的丰富的集群使用这种效率参数化。我们认为,不能期望一个不寻常的和高度参数化的形式的效率函数,以适应观测数据假设的理论模型。使用许多实现的蒙特卡罗模拟,我们表明,标准的空间平坦的LCDM模型是勉强一致的SPT集群数据使用一个简单的和合理的二维效率函数检测的集群。需要更多的聚类数据才能得出强有力的结论。

英文摘要:

Recent detection of high-redshift, massive clusters through Sunyaev-Zel'dovich observations has opened up a new way to test cosmological models. It is known that detection of a single supermassive cluster at a very high redshift can rule out many cosmological models all together. However, since dealing with different observational biases makes it difficult to test the likeliness of the data assuming a cosmological model, most of the cluster data (except those with high mass-redshift) stays untouched in confronting cosmological models with cluster observations. We propose here that one can use the relative abundance of the clusters with different masses at different redshifts to test the likeliness of the data in the context of cosmological models. For this purpose we propose a simple parametric form for the efficiency of observing clusters at different mass-redshift and we test if the standard LCDM model can explain the observed abundance of the clusters using this efficiency parameterization. We argue that one cannot expect an unusual and highly parametric form of the efficiency function to fit the observed data assuming a theoretical model. Using many realizations of Monte Carlo simulations we show that the standard spatially flat LCDM model is barely consistent with the SPT cluster data using a simple and plausible two-dimensional efficiency function for detection of the clusters. More cluster data are needed to make any strong conclusion.

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