论文标题
如何最佳地结合重建前的完整形状和重建后BAO信号
How to optimally combine pre-reconstruction full shape and post-reconstruction BAO signals
论文作者
论文摘要
我们回顾了从预先构造的功率谱的完整形状组合宇宙学信息的不同方法（通常称为红移空间失真（RSD）分析），以及来自后构造的功率谱的Baryon声学振荡（BAO）峰位置，其目的是找到最佳过程。我们专注于在不同的压缩级别上结合重建的派生数量：1）两点摘要统计量，功率谱多物，$ p^{（\ ell）}（k）$; 2）压缩的BAO变量，$α_ {\ Parallel，\ perp} $; 3）在1）和2之间的混合方法。我们将这些方法应用于数据和合成EZ摩克群的公开发光红色星系目录。我们发现，当使用适当的协方差矩阵估计器时，这三种方法会导致非常一致的后代。平均而言，与其他两种方法相比，$ p^{（\ el）}（k）$级别的组合检索$ 5-10 \％$更紧密的约束，这表明在BAO变量级别组合的标准方法几乎是最佳的。我们得出的结论是，在摘要统计级别上实现一个单个数据实现的BAO后重建和完整形状的预构建信号速度更快，因为它不需要在单个模拟上运行整个管道，并带来了中等$ 10 \％的$ $ $改进，并且与其他两种研究的方法有关。此外，我们检查了潜在的系统学，例如矩阵的构建方式以及有限数量的模拟对可能性估计器的影响，并且没有发现这些对最终结果产生重大影响。在摘要统计级别上结合重建前后信号是一种有吸引力，更快，准确的方法，可在将来和正在进行的光谱调查中使用。
We review the different approaches for combining the cosmological information from the full shape of the pre-reconstructed power spectrum - usually referred as redshift-space distortion (RSD) analysis - and from the baryon acoustic oscillation (BAO) peak position in the post-reconstructed power spectrum with the aim of finding the optimal procedure. We focus on combining the pre- and post-reconstructed derived quantities at different compression levels: 1) the two-point summary statistics, the power spectrum multipoles, $P^{(\ell)}(k)$; 2) the compressed BAO variables, $α_{\parallel,\perp}$; and 3) an hybrid approach between 1) and 2). We apply these methods to the publicly available eBOSS Luminous Red Galaxy catalogues, for both data and synthetic EZ-mocks. We find that the three approaches result in very consistent posteriors when the appropriate covariance matrix estimator is used. On average, the combination at $P^{(\ell)}(k)$ level retrieves $5-10\%$ tighter constraints than the other two approaches, demonstrating that the standard approach of combining at the level of the BAO variables is nearly optimal. We conclude that combining both BAO post-reconstructed and full shape pre-reconstructed signals for the one single data realization at the level of the summary statistics is faster, as it does not require running the whole pipeline on the individual mocks, and brings a moderate $10\%$ improvement, with respect to the other two studied methods. Moreover, we check for potential systematics, such as, the way the matrix is built and the effect of the finite number of mocks on the likelihood estimator and find none of these have a significant impact in the final results. Combining the pre- and post-reconstruction signals at the level of the summary statistics is an attractive, faster and accurate method to be used in future and on-going spectroscopic surveys.
