THREE-POINT PHASE CORRELATIONS: A NEW MEASURE OF NONLINEAR LARGE-SCALE STRUCTURE
THREE-POINT PHASE CORRELATIONS: A NEW MEASURE OF NONLINEAR LARGE-SCALE STRUCTURE
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三点相位相关性：非线性大尺度结构的新测量
DOI：
10.1088/0004-637x/804/2/132
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
D. Obreschkow
中科院分区：
文献类型：
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作者：
Richard Wolstenhulme;C. Bonvin;D. Obreschkow
我们推导了一种新颖的可观察的大规模结构的解析表达式：线相关函数。线相关函数由密度场相位的三点相关函数构造而成，是一种稳健的统计度量，允许在非线性和非高斯状态下提取信息。我们证明，在微扰理论中，线相关性对耦合核 F2 敏感，耦合核 F2 控制着密度场的非线性引力演化。我们将解析表达式与数值模拟的结果进行比较，发现分离 r ≳ 30 h−1 Mpc 具有 1σ 一致性。小尺度下功率谱和非线性耦合核的拟合公式使我们能够将预测扩展到强非线性区域，其中我们发现与 r ≳ 2 h−1 Mpc 的模拟有 1σ 一致性。我们讨论线相关相对于双谱等标准统计测量的优点。与后者不同的是，在偏差是局部且线性的情况下，线相关性与偏差无关。此外，线相关的方差与密度场模量的高斯方差无关。这表明线相关可以更精确地探测重力的非线性状态，并且功率谱方差的污染更少。
We derive an analytical expression for a novel large-scale structure observable: the line correlation function. The line correlation function, which is constructed from the three-point correlation function of the phase of the density field, is a robust statistical measure allowing the extraction of information in the nonlinear and non-Gaussian regime. We show that, in perturbation theory, the line correlation is sensitive to the coupling kernel F2, which governs the nonlinear gravitational evolution of the density field. We compare our analytical expression with results from numerical simulations and find a 1σ agreement for separations r ≳ 30 h−1 Mpc. Fitting formulae for the power spectrum and the nonlinear coupling kernel at small scales allow us to extend our prediction into the strongly nonlinear regime, where we find a 1σ agreement with the simulations for r ≳ 2 h−1 Mpc. We discuss the advantages of the line correlation relative to standard statistical measures like the bispectrum. Unlike the latter, the line correlation is independent of the bias, in the regime where the bias is local and linear. Furthermore, the variance of the line correlation is independent of the Gaussian variance on the modulus of the density field. This suggests that the line correlation can probe more precisely the nonlinear regime of gravity, with less contamination from the power spectrum variance.