Optimizing the Zel'dovich Approximation
Abstract
We have recently learned that the Zeldovich approximation can be successfully used for a far wider range of gravitational instability scenarios than formerly proposed; we study here how to extend this range. In previous work we studied the accuracy of several analytic approximations to gravitational clustering in the mildly nonlinear regime. We found that the ``truncated Zel'dovich approximation" (TZA) was better than any other (except in one case the ordinary Zeldovich approximation) over a wide range from linear to mildly nonlinear () regimes. TZA sets Fourier amplitudes equal to zero for {\it all} wavenumbers greater than , where marks the transition to the nonlinear regime. Here, we study crosscorrelation of generalized TZA with a group of --body simulations for three shapes of window function: sharp --truncation (as in CMS), tophat in coordinate space, or a Gaussian. We also study the crosscorrelation as a function of initial scale within each window type. We find --truncation, which was so much better than other things tried in CMS, is the {it worst} of these three window shapes. We find that a Gaussian window applied to the intial Fourier amplitudes is the best choice. It produces a greatly improved crosscorrelation all cases we studied. The optimum choice of for the Gaussian window is (spectrum-- dependent) 1--1.5 times , with defined by (3). Although all three windows produce similar power spectra and density distribution functions after application of the Zeldovich approximation, phase agreement with the --body simulation is better for the Gaussian window. We ascribe Gaussian window success to its superior treatment of phase evolution.
Cite
@article{arxiv.astro-ph/9312044,
title = {Optimizing the Zel'dovich Approximation},
author = {A. L. Melott and T. F. Pellman and S. F. Shandarin},
journal= {arXiv preprint arXiv:astro-ph/9312044},
year = {2015}
}
Comments
Submitted to MNRAS. TeX file; for figures send request to [email protected]. Replacment has shorter abstract