Scaling relations for galaxy clusters in the Millennium-XXL simulation
Scaling relations for galaxy clusters in the Millennium-XXL simulation
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DOI：
10.1111/j.1365-2966.2012.21830.x
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发表时间：
2012-03
影响因子：
4.8
通讯作者：
R. Angulo;V. Springel;V. Springel;S. White;A. Jenkins;C. Baugh;C. Frenk
中科院分区：
文献类型：
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作者：
R. Angulo;V. Springel;V. Springel;S. White;A. Jenkins;C. Baugh;C. Frenk
我们提出了一个非常大的高分辨率宇宙N体模拟，千禧-XXL或MXXL，它使用3,030亿个粒子来表示整个Λ冷暗物质宇宙学中4.1GPC盒子中暗物质结构的形成。我们创建了天空地图，并使用四种不同观测数据的替代品来识别大样本的星系团：从星系调查估计的丰富度，X射线光度，积分的Sunyaev-Zeldovich(SZ)信号和透镜质量。体积和分辨率的史无前例的结合使我们能够详细地探索这些可观测到的天体如何彼此之间以及与星团质量之间存在比例关系。由于对星系团的内部结构、取向和环境的共同敏感性，以及不相关结构的视线叠加，散射与不同的质量可观测关系之间存在相关性。我们表明，这可以解释最近通过堆叠来自普朗克卫星的数据，在光学和X射线选择的星系团测量的平均温度SZ信号之间发现的明显差异。相关的系统学也会影响在高红移探测到的极端星团的推断。我们的结果表明，来自星系团调查的宇宙学结论关键取决于适当的建模，不仅是相关物理的建模，而且还取决于观测数据的充分分布和星系团识别过程引起的选择偏差。
We present a very large high-resolution cosmological N-body simulation, the Millennium-XXL or MXXL, which uses 303 billion particles to represent the formation of dark matter structures throughout a 4.1 Gpc box in a Λ cold dark matter cosmology. We create sky maps and identify large samples of galaxy clusters using surrogates for four different observables: richness estimated from galaxy surveys, X-ray luminosity, integrated Sunyaev–Zeldovich (SZ) signal and lensing mass. The unprecedented combination of volume and resolution allows us to explore in detail how these observables scale with each other and with cluster mass. The scatter correlates between different mass–observable relations because of common sensitivities to the internal structure, orientation and environment of clusters, as well as to line-of-sight superposition of uncorrelated structure. We show that this can account for the apparent discrepancies uncovered recently between the mean thermal SZ signals measured for optically and X-ray selected clusters by stacking data from the Planck satellite. Related systematics can also affect inferences from extreme clusters detected at high redshift. Our results illustrate that cosmological conclusions from galaxy cluster surveys depend critically on proper modelling, not only of the relevant physics, but also of the full distribution of the observables and of the selection biases induced by cluster identification procedures.