Galaxy two-point covariance matrix estimation for next generation surveys
Galaxy two-point covariance matrix estimation for next generation surveys
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DOI：
10.1093/mnras/stx2342
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发表时间：
2017-09
影响因子：
4.8
通讯作者：
C. Howlett;W. Percival
中科院分区：
文献类型：
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作者：
C. Howlett;W. Percival
我们进行了详细的分析的球面平均星系功率谱的协方差矩阵，并提出了一种新的，实用的方法来估计这在一个任意的调查，而不需要运行模拟星系模拟，覆盖整个调查量。该方法使用理论参数来修改从一组小体积立方体星系模拟中测量的协方差矩阵，与较大的模拟相比，这在计算上是便宜的，并且比使用理论建模更准确地匹配测量的小规模星系团。我们包括处方分析占调查的窗口函数，卷积测量的协方差矩阵在一个非平凡的。我们还提出了一种新的方法，包括超样本协方差和模式以外的小模拟体积的影响，不需要额外的模拟，仍然允许我们缩放协方差矩阵。作为验证，我们将使用我们的新方法估计的协方差矩阵与使用最初为分析斯隆数字巡天主要星系样本而创建的500个模拟的蛮力计算进行比较。我们发现所有感兴趣的大规模结构分析，包括那些占主导地位的调查窗口的影响，并在规模上的聚类理论模型通常会崩溃，但新方法产生的协方差矩阵显着更好的信噪比。虽然只有在真实的空间形式上正确的，我们还讨论了如何我们的方法可以扩展到将红移空间扭曲的影响。
We perform a detailed analysis of the covariance matrix of the spherically averaged galaxy power spectrum and present a new, practical method for estimating this within an arbitrary survey without the need for running mock galaxy simulations that cover the full survey volume. The method uses theoretical arguments to modify the covariance matrix measured from a set of small-volume cubic galaxy simulations, which are computationally cheap to produce compared to larger simulations and match the measured small-scale galaxy clustering more accurately than is possible using theoretical modelling. We include prescriptions to analytically account for the window function of the survey, which convolves the measured covariance matrix in a non-trivialway. We also present a newmethod to include the effects of super-sample covariance and modes outside the small simulation volume which requires no additional simulations and still allows us to scale the covariance matrix. As validation, we compare the covariance matrix estimated using our new method to that from a brute-force calculation using 500 simulations originally created for analysis of the Sloan Digital Sky Survey Main Galaxy Sample. We find excellent agreement on all scales of interest for large-scale structure analysis, including those dominated by the effects of the survey window, and on scales where theoretical models of the clustering normally break down, but the new method produces a covariance matrix with significantly better signal-to-noise ratio. Although only formally correct in real space, we also discuss how our method can be extended to incorporate the effects of redshift space distortions.