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arXiv 0912.0446astro-ph.CO
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大尺度晕分布中的局部偏差模型

The Local Bias Model in the Large Scale Halo Distribution

Marc Manera, Enrique Gaztanaga

摘要:

我们探讨的偏见相比,暗物质(DM)在模拟中的聚类统计晕。我们在(中间)MICEL 1536模拟的大尺度下观察二阶和三阶统计量,并直接测量DM波动δ之间的局部偏差关系h = f(δ),在模拟中的一个点处的礼帽半径Rs上平滑,并在同一点处测量其相应的示踪剂h(即晕)。该局部关系可以被泰勒展开以定义线性(b1)和非线性(b2)偏置参数。模拟中的b1和b2的值随着Rs的变化而变化,在Rs > 30 - 60 Mpc/h附近接近恒定值。我们使用的本地关系来预测的DM的一个方面的示踪剂的聚类。对于r_12 > 15 Mpc/h的晕2-点相关系数ε(r_12),这种预测非常有效(大约为百分比水平),但只有当我们使用在非常大的平滑半径Rs > 30 - 60 Mpc/h时发现的偏置值时才有效。我们发现f(δ)展开式中的随机项或次首阶项没有影响。但我们确实发现了三点函数中的一些差异,需要进一步理解。我们还研究了平滑矩的聚类,方差和偏度是体积平均相关性,因此包括较小尺度的聚类。在这种情况下,我们发现,在10 - 20%的水平上,都需要(对局部模型的)次领先阶和离散性校正。散粒噪声可以用项σe^2/n校正,其中σe^2 < 1,即,总是小于泊松校正。我们还比较了这些结果与峰背景分裂预测从测量晕质量函数。我们发现5-10%的系统(和类似的统计)错误的质量估计时,我们使用晕模型偏置预测校准的质量。

英文摘要:

We explore the biasing in the clustering statistics of halos as compared to dark matter (DM) in simulations. We look at the second and third order statistics at large scales of the (intermediate) MICEL1536 simulation and also measure directly the local bias relation h = f(δ) between DM fluctuations, δ, smoothed over a top-hat radius Rs at a point in the simulation and its corresponding tracer h (i.e. halos) at the same point. This local relation can be Taylor expanded to define a linear (b1) and non-linear (b2) bias parameters. The values of b1 and b2 in the simulation vary with Rs approaching a constant value around Rs > 30 - 60 Mpc/h. We use the local relation to predict the clustering of the tracer in terms of the one of DM. This prediction works very well (about percent level) for the halo 2-point correlation ξ(r_12) for r_12 > 15 Mpc/h, but only when we use the biasing values that we found at very large smoothing radius Rs > 30 - 60 Mpc/h. We find no effect from stochastic or next to leading order terms in the f(δ) expansion. But we do find some discrepancies in the 3-point function that needs further understanding. We also look at the clustering of the smoothed moments, the variance and skewness which are volume average correlations and therefore include clustering from smaller scales. In this case, we find that both next to leading order and discreetness corrections (to the local model) are needed at the 10 - 20% level. Shot-noise can be corrected with a term σe^2/n where σe^2 < 1, i.e., always smaller than the Poisson correction. We also compare these results with the peak-background split predictions from the measured halo mass function. We find 5-10% systematic (and similar statistical) errors in the mass estimation when we use the halo model biasing predictions to calibrate the mass.

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