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arXiv 0810.1885astro-ph
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银河巡天的统计分析- I。两点聚类统计量的稳健误差估计

Statistical Analysis of Galaxy Surveys - I. Robust error estimation for 2-point clustering statistics

Peder Norberg, Carlton M. Baugh, Enrique Gaztanaga, Darren J. Croton

摘要:

我们对2点聚类统计的不同误差估计量进行了测试,适用于当前和未来的大星系红移调查。使用非常大的暗物质LambdaCDM N-体模拟合奏,我们比较内部误差估计(刀切和自助)外部的(蒙特卡洛实现)。对于3维聚类统计,我们发现,没有内部误差的方法研究能够再现既不准确,也不稳健的外部估计1至25百万pc/小时规模的错误。标准自助法在所有尺度上高估了xi(s)的方差约40%,但以稳健的方式恢复了基础协方差矩阵的主特征向量。刀切法在大尺度上返回正确的方差,但在小尺度上显著高估了它。这种规模依赖的折刀影响恢复的特征向量,这往往不同意在小尺度上与外部估计。我们的研究结果有重要的意义,使用星系团在宇宙学参数的约束。例如,在一个2参数拟合的投影关联函数,我们发现,标准的自助系统高估了95%的置信区间,而刀切法仍然有偏见,但在较小的程度。对于高斯统计,我们在实现之间发现的分散意味着从内部估计量推断的2-sigma置信区间在实践中可以对应于从1-sigma到3-sigma的任何值。最后,通过子体积的过采样,可以获得与外部误差估计一致的自举方差和置信区间,但不清楚这种处方是否适用于一般情况。

英文摘要:

We present a test of different error estimators for 2-point clustering statistics, appropriate for present and future large galaxy redshift surveys. Using an ensemble of very large dark matter LambdaCDM N-body simulations, we compare internal error estimators (jackknife and bootstrap) to external ones (Monte-Carlo realizations). For 3-dimensional clustering statistics, we find that none of the internal error methods investigated are able to reproduce neither accurately nor robustly the errors of external estimators on 1 to 25 Mpc/h scales. The standard bootstrap overestimates the variance of xi(s) by ~40% on all scales probed, but recovers, in a robust fashion, the principal eigenvectors of the underlying covariance matrix. The jackknife returns the correct variance on large scales, but significantly overestimates it on smaller scales. This scale dependence in the jackknife affects the recovered eigenvectors, which tend to disagree on small scales with the external estimates. Our results have important implications for the use of galaxy clustering in placing constraints on cosmological parameters. For example, in a 2-parameter fit to the projected correlation function, we find that the standard bootstrap systematically overestimates the 95% confidence interval, while the jackknife method remains biased, but to a lesser extent. The scatter we find between realizations, for Gaussian statistics, implies that a 2-sigma confidence interval, as inferred from an internal estimator, could correspond in practice to anything from 1-sigma to 3-sigma. Finally, by an oversampling of sub-volumes, it is possible to obtain bootstrap variances and confidence intervals that agree with external error estimates, but it is not clear if this prescription will work for a general case.

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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