论文标题

大规模结构分析和发电的新的可解释统计数据

New Interpretable Statistics for Large Scale Structure Analysis and Generation

论文作者

Allys, E., Marchand, T., Cardoso, J. -F., Villaescusa-Navarro, F., Ho, S., Mallat, S.

论文摘要

我们介绍了小波相谐波(WPH)统计数据:可解释的低维统计数据,描述了2D非高斯领域。这些统计数据是从WPH时刻构建的,WPH时刻是在数据科学和机器学习社区中引入的。我们将WPH统计数据应用于宇宙大规模结构的Quijote n体型模拟的预计2D物质密度场。通过计算Fisher信息矩阵,我们发现,与统计数据相比,WPH统计数据对五个宇宙参数的四个对基于功率谱和Biseptrum的组合进行了更为严格的约束。我们还使用具有最大熵模型的WPH统计数据来统计生成新的2D​​密度字段,以准确地重现输入密度场的功率光谱,双光谱,双光谱和Minkowski功能的概率密度函数,平均值和标准偏差。尽管其他方法对于大规模结构的参数估计值或统计合成是有效的,但WPH统计是第一个实现这两个任务和可解释的最新结果的统计数据。

We introduce Wavelet Phase Harmonics (WPH) statistics: interpretable low-dimensional statistics that describe 2D non-Gaussian fields. These statistics are built from WPH moments, which were recently introduced in the data science and machine learning community. We apply WPH statistics to projected 2D matter density fields from the Quijote N-body simulations of the large-scale structure of the Universe. By computing Fisher information matrices, we find that the WPH statistics place more stringent constraints on four of five cosmological parameters when compared to statistics based on the combination of the power spectrum and bispectrum. We also use the WPH statistics with a maximum entropy model to statistically generate new 2D density fields that accurately reproduce the probability density function, the mean and standard deviation of the power spectrum, the bispectrum, and Minkowski functionals of the input density fields. Although other methods are efficient for either parameter estimates or statistical syntheses of the large-scale structure, WPH statistics are the first statistics that achieve state-of-the-art results for both tasks as well as being interpretable.

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