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arXiv 1012.3462astro-ph.CO
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SDSS星系的三点关联函数:约束星系质量偏差

Three-Point Correlation Functions of SDSS Galaxies: Constraining Galaxy-Mass Bias

Cameron K. McBride, Andrew J. Connolly, Jeffrey P. Gardner, Ryan Scranton, Roman Scoccimarro, Andreas A. Berlind, Felipe Marin, Donald P. Schneider

摘要:

我们约束的线性和二次偏置参数的配置依赖的三点关联函数(3 PCF)在红移和投影空间,利用测量的光谱星系在斯隆数字巡天(SDSS)的主要星系样本。通过准线性区(9-27 Mpc/h)的误差分析,我们发现明亮星系(M_r < -21.5)是质量的有偏示踪物,在红移空间和投影空间中的测量值分别为4.5 σ和2.5 σ。对较暗星系样本的测量与无偏模型一致。我们证明,一个线性偏差模型似乎足以解释我们的样本的星系质量偏差,虽然使用线性和二次项的模型结果在一个更好的拟合。相比之下,从线性模型获得的偏差值似乎与通过检查相对偏差获得的数据更一致,并且产生与当前约束更一致的sigma_8的隐含值。我们调查的协方差的3 PCF,这本身就是一个测量星系聚类。我们评估我们的误差估计的准确性,通过比较模拟星系目录的折刀重新采样方法的结果。我们确定的协方差结构的显着差异。然而,这些差异的影响似乎是减轻了本征模式分析,可以占嘈杂的,未解决的模式。我们的研究结果表明,即使使用不太理想的方法来估计误差,使用这种技术也足以消除潜在的系统性。

英文摘要:

We constrain the linear and quadratic bias parameters from the configuration dependence of the three-point correlation function (3PCF) in both redshift and projected space, utilizing measurements of spectroscopic galaxies in the Sloan Digital Sky Survey (SDSS) Main Galaxy Sample. We show that bright galaxies (M_r < -21.5) are biased tracers of mass, measured at a significance of 4.5 sigma in redshift space and 2.5 sigma in projected space by using a thorough error analysis in the quasi-linear regime (9-27 Mpc/h). Measurements on a fainter galaxy sample are consistent with an unbiased model. We demonstrate that a linear bias model appears sufficient to explain the galaxy-mass bias of our samples, although a model using both linear and quadratic terms results in a better fit. In contrast, the bias values obtained from the linear model appear in better agreement with the data by inspection of the relative bias, and yield implied values of sigma_8 that are more consistent with current constraints. We investigate the covariance of the 3PCF, which itself is a measurement of galaxy clustering. We assess the accuracy of our error estimates by comparing results from mock galaxy catalogs to jackknife re-sampling methods. We identify significant differences in the structure of the covariance. However, the impact of these discrepancies appears to be mitigated by an eigenmode analysis that can account for the noisy, unresolved modes. Our results demonstrate that using this technique is sufficient to remove potential systematics even when using less-than-ideal methods to estimate errors.

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