论文标题
从压缩模态双光谱迈向宇宙学的限制：真实空间双光谱估计器的强大比较
Towards cosmological constraints from the compressed modal bispectrum: a robust comparison of real-space bispectrum estimators
论文作者
论文摘要
高阶聚类统计数据，例如Galaxy Biseptrum，可以将互补的宇宙学信息添加到可访问的两点统计数据（例如功率谱）中。虽然测量双光谱的标准方法涉及在大量的傅立叶三角箱中估计双光谱值，但压缩的模态双光谱近似Biseptrum作为基础函数的线性组合，并根据所选的基础估算了膨胀系数。在这项工作中，我们通过使用并行管道来分析用$ n $ n $ simulations测量的真实空间halo Biseptrum来比较这两个估计器，该估计量与$ n $ - 体积的模拟相对应$ \ sim 1 {，} 000 \，h^{ - h^{ - 3} \，{\ rm gpc}^3 $ halo cataliance iStric catliance iSTRIC。我们发现，模态双光谱产生与标准双光谱分析一致且具有竞争力的约束：对于树级晕圈双光谱模型中的光环偏见和射击噪声参数，最高可达$ k _ {\ rm max} \ of $ k _ {\ rm max} \大约0.06 \，（0.10）\，（0.10）\，（0.10）。 （10）模态膨胀系数对于获得与标准双光谱估计器相当的约束，使用$ \ sim $ \ sim $ 20到1,600三角箱，这是必要的，具体取决于垃圾箱的宽度。对于这项工作，我们首次使用Markov Chain Carlo模拟实施了模态估计器管道，并详细讨论了参数后代和模态扩展如何对模态Bispectrum Pipeline中的几个用户设置的强大或敏感。实现的高效压缩和可用的大量模拟目录的组合使我们能够量化模态双光谱约束如何取决于用于估计协方差矩阵的模拟数量和可能性的功能形式。
Higher-order clustering statistics, like the galaxy bispectrum, can add complementary cosmological information to what is accessible with two-point statistics, like the power spectrum. While the standard way of measuring the bispectrum involves estimating a bispectrum value in a large number of Fourier triangle bins, the compressed modal bispectrum approximates the bispectrum as a linear combination of basis functions and estimates the expansion coefficients on the chosen basis. In this work, we compare the two estimators by using parallel pipelines to analyze the real-space halo bispectrum measured in a suite of $N$-body simulations corresponding to a total volume of $\sim 1{,}000 \,h^{-3}\,{\rm Gpc}^3$, with covariance matrices estimated from 10,000 mock halo catalogs. We find that the modal bispectrum yields constraints that are consistent and competitive with the standard bispectrum analysis: for the halo bias and shot noise parameters within the tree-level halo bispectrum model up to $k_{\rm max} \approx 0.06 \, (0.10) \,h\,{\rm Mpc}^{-1}$, only 6 (10) modal expansion coefficients are necessary to obtain constraints equivalent to the standard bispectrum estimator using $\sim$ 20 to 1,600 triangle bins, depending on the bin width. For this work, we have implemented a modal estimator pipeline using Markov Chain Monte Carlo simulations for the first time, and we discuss in detail how the parameter posteriors and modal expansion are robust to, or sensitive to, several user settings within the modal bispectrum pipeline. The combination of the highly efficient compression that is achieved and the large number of mock catalogs available allows us to quantify how our modal bispectrum constraints depend on the number of mocks that are used to estimate covariance matrices and the functional form of the likelihood.
