INCREASING THE FISHER INFORMATION CONTENT IN THE MATTER POWER SPECTRUM BY NONLINEAR WAVELET WIENER FILTERING

We develop a purely mathematical tool to recover some of the information lost in the nonlinear collapse of largescale 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.