论文标题

对于准确的大规模结构协方差,折刀量表真的很重要吗?

Does jackknife scale really matter for accurate large-scale structure covariances?

论文作者

Favole, Ginevra, Granett, Benjamin R., Lafaurie, Javier Silva, Sapone, Domenico

论文摘要

折刀方法给出了大规模结构调查的内部协方差估计,并在宇宙参数上允许模型独立的错误。使用SDSS-III BOSS CMASS样本,我们研究了折刀的大小和重采样数量如何影响协方差估计值对相关功能多尔的精度以及在推断的Baryon声学量表上的误差。我们将测量值与Multidark Patchy模拟星系目录进行了比较,并且还通过相同的调查几何形状对一组对数正态模拟进行了验证。我们构建了几种折刀配置,这些配置的大小和重新采样数量各不相同。我们在协方差估算中介绍了hartlap因子,该因素取决于折刀的数量。我们还发现,应用锥形方案以估算有限数量的重采样的精度矩阵很有用。 CMASS和模拟目录的结果表明,Baryon声学量表的误差估计不取决于折刀量表。对于Shift参数$α$,我们发现平均误差分别为1.6%,2.2%和1.2%,分别来自CMASS,斑点和原木正常的折刀协方差。尽管这些不确定性由于折刀方法的某些结构性限制而显着波动,但我们的$α$估计值与已发表的重建前分析是合理的一致性。折刀方法将为未来的大规模结构调查提供有价值的互补协方差估计。

The jackknife method gives an internal covariance estimate for large-scale structure surveys and allows model-independent errors on cosmological parameters. Using the SDSS-III BOSS CMASS sample, we study how the jackknife size and number of resamplings impact the precision of the covariance estimate on the correlation function multipoles and the error on the inferred baryon acoustic scale. We compare the measurement with the MultiDark Patchy mock galaxy catalogues, and we also validate it against a set of log-normal mocks with the same survey geometry. We build several jackknife configurations that vary in size and number of resamplings. We introduce the Hartlap factor in the covariance estimate that depends on the number of jackknife resamplings. We also find that it is useful to apply the tapering scheme to estimate the precision matrix from a limited number of resamplings. The results from CMASS and mock catalogues show that the error estimate of the baryon acoustic scale does not depend on the jackknife scale. For the shift parameter $α$, we find an average error of 1.6%, 2.2% and 1.2%, respectively from CMASS, Patchy and log-normal jackknife covariances. Despite these uncertainties fluctuate significantly due to some structural limitations of the jackknife method, our $α$ estimates are in reasonable agreement with published pre-reconstruction analyses. Jackknife methods will provide valuable and complementary covariance estimates for future large-scale structure surveys.

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