arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1503.00313astro-ph.CO
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从丰度和聚类比较晕偏

Comparing halo bias from abundance and clustering

Kai Hoffmann, Julien Bel, Enrique Gaztanaga

摘要:

我们模拟的$\sim(3 \ \text{Gpc}/h)^3$体积的MICE大挑战模拟的晕的丰度通过拟合通用质量函数与改进的刀刀误差协方差估计,匹配理论预测。我们提出了不同的拟合模型和新的预测线性($b_1$)和非线性($c_2$和$c_3$)晕聚类偏差之间的统一关系。当分析中包括低质量范围($M_h \lesssim 3 \ 10^{12} \ M_{\odot}/h$)时,不同的质量函数拟合显示其性能存在很大差异。结合文献中的拟合结果,我们发现低质量星系的振幅总体变化在$10$ %左右,高质量星系(星系团)的振幅变化在$50$ %左右($M_h > 10^{14} \ M_{\odot}/h$)。这些变化传播为$b_1$预测中的$10$ %变化和$c_2$或$c_3$中的$50$ %变化。尽管有这些强烈的变化,我们发现$b_1$和$c_2$或$c_3$之间的普遍关系,我们提供简单的拟合。排除低质量晕,不同的模型拟合合理的善良在这个分析中,显示百分比水平的协议,在其$b_1$预测,但系统$5-10$ %低于偏差直接测量两点晕质量聚类。这一结果证实了先前从较小体积(和较小质量)得出的发现。偏倚预测的不准确性导致生长测量的$5-10$ %误差。它们还影响来自聚类测量的任何HOD拟合或(聚类)质量校准。

英文摘要:

We model the abundance of haloes in the $\sim(3 \ \text{Gpc}/h)^3$ volume of the MICE Grand Challenge simulation by fitting the universal mass function with an improved Jack-Knife error covariance estimator that matches theory predictions. We present unifying relations between different fitting models and new predictions for linear ($b_1$) and non-linear ($c_2$ and $c_3$) halo clustering bias. Different mass function fits show strong variations in their performance when including the low mass range ($M_h \lesssim 3 \ 10^{12} \ M_{\odot}/h$) in the analysis. Together with fits from the literature we find an overall variation in the amplitudes of around $10$% in the low mass and up to $50$% in the high mass (galaxy cluster) range ($M_h > 10^{14} \ M_{\odot}/h$). These variations propagate into a $10$% change in $b_1$ predictions and a $50$% change in $c_2$ or $c_3$. Despite these strong variations we find universal relations between $b_1$ and $c_2$ or $c_3$ for which we provide simple fits. Excluding low mass haloes, different models fitted with reasonable goodness in this analysis, show percent level agreement in their $b_1$ predictions, but are systematically $5-10$% lower than the bias directly measured with two-point halo-mass clustering. This result confirms previous findings derived from smaller volumes (and smaller masses). Inaccuracies in the bias predictions lead to $5-10$% errors in growth measurements. They also affect any HOD fitting or (cluster) mass calibration from clustering measurements.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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