INCREASING THE FISHER INFORMATION CONTENT IN THE MATTER POWER SPECTRUM BY NONLINEAR WAVELET WIENER FILTERING
INCREASING THE FISHER INFORMATION CONTENT IN THE MATTER POWER SPECTRUM BY NONLINEAR WAVELET WIENER FILTERING
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DOI：
10.1088/0004-637x/728/1/35
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发表时间：
2010-08
期刊：
影响因子：
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通讯作者：
Tong-Jie Zhang;Hao-Ran Yu;J. Harnois-D'eraps;I. MacDonald;U. Pen
中科院分区：
文献类型：
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作者：
Tong-Jie Zhang;Hao-Ran Yu;J. Harnois-D'eraps;I. MacDonald;U. Pen
我们开发了一个纯数学的工具来恢复在大规模结构的非线性倒塌中丢失的一些信息。从一组141个暗物质密度场的模拟，我们构造了一个非线性维纳滤波器，以分离高斯和非高斯结构的小波空间。我们发现，在较小的尺度上，非高斯功率占主导地位，正如预期的结构形成理论，而高斯对应的阻尼小尺度上的一个数量级。我们发现，这是可能的，以增加费雪信息的三个因素之前，达到translinear高原，效果与其他技术，如密度场的线性重建。
We develop a purely mathematical tool to recover some of the information lost in the nonlinear collapse of large-scale structure. From a set of 141 simulations of dark matter density fields, we construct a nonlinear Wiener filter in order to separate Gaussian and non-Gaussian structure in wavelet space. We find that the non-Gaussian power is dominant at smaller scales, as expected from the theory of structure formation, while the Gaussian counterpart is damped by an order of magnitude on small scales. We find that it is possible to increase the Fisher information by a factor of three before reaching the translinear plateau, an effect comparable to other techniques like the linear reconstruction of the density field.