暗物质功率谱中Fisher信息恢复的优化
Optimizing the Recovery of Fisher Information in the Dark Matter Power Spectrum
摘要：
我们结合两个高斯化技术-小波非线性维纳滤波器（WNLWF）和密度重建-量化恢复的Fisher信息是丢失的引力坍缩。我们计算位移场，在类似的Zel'dovich近似，并应用小波非线性维纳滤波器，分解成高斯和非高斯分量的重建密度场。从一系列的200个实现的N体模拟，我们计算的恢复性能与暗物质粒子和晕获得的密度场。我们发现，在k > 1.0h/Mpc时，粒子的Fisher信息跨导线性平台的高度增加了一个数量级以上，而单独使用任何一种技术都只能提供三到五倍的单独恢复提升。我们的结论是，这两种技术在共生中工作，因为它们的组合性能比它们各自的贡献之和更强。当应用于晕星表时，我们发现重建对Fisher信息的恢复只有微弱的影响，而非线性小波滤波器使信息提高了约五倍。我们还观察到，非高斯泊松噪声饱和的Fisher信息，和散粒噪声减去测量表现出温和的信息恢复。
英文摘要：
We combine two Gaussianization techniques - Wavelet Non-Linear Wiener Filter (WNLWF) and density reconstruction - to quantify the recovery of Fisher information that is lost in the gravitational collapse. We compute a displacement fields, in analogy with the Zel'dovich approximation, and apply a Wavelet Non-Linear Wiener Filter that decomposes the reconstructed density fields into a Gaussian and a non-Gaussian component. From a series of 200 realizations of N-body simulations, we compute the recovery performance for density fields obtained with both dark matter particles and haloes. We find that the height of the Fisher information trans-linear plateau is increased by more than an order of magnitude at k > 1.0h/Mpc for particles, whereas either technique alone offers an individual recovery boost of only a factor of three to five. We conclude that these two techniques work in a symbiosis, as their combined performance is stronger than the sum of their individual contribution. When applied to the halo catalogues, we find that the reconstruction has only a weak effect on the recovery of Fisher Information, while the non-linear wavelet filter boosts the information by about a factor offive. We also observe that non-Gaussian Poisson noise saturates the Fisher information, and that shot noise subtracted measurements exhibit a milder information recovery.
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