Truthing the stretch: Non-perturbative cosmological realizations with multiscale spherical collapse

TL;DR

The paper introduces MUSCLE, a fast, non-perturbative algorithm for generating accurate low-redshift cosmological particle realizations using multiscale spherical collapse, improving upon existing perturbative methods.

Contribution

It presents MUSCLE, a parameter-free, multiscale spherical collapse algorithm that enhances accuracy and efficiency in cosmological simulations and mock catalog generation.

Findings

MUSCLE outperforms Zel'dovich and 2LPT schemes in accuracy.

Inclusion of void-in-cloud process improves spherical-collapse models.

The method is computationally comparable to initial condition generation for N-body simulations.

Abstract

Here we present a simple, parameter-free, non-perturbative algorithm that gives low-redshift cosmological particle realizations accurate to few-Megaparsec scales, called MUSCLE (MUltiscale Spherical ColLapse Evolution). It has virtually the same cost as producing N-body-simulation initial conditions, since it works with the 'stretch' parameter {\psi}, the Lagrangian divergence of the displacement field. It promises to be useful in quickly producing mock catalogs, and to simplify computationally intensive reconstructions of galaxy surveys. MUSCLE applies a spherical-collapse prescription on multiple Gaussian-smoothed scales. It achieves higher accuracy than perturbative schemes (Zel'dovich and 2LPT), and, by including the void-in-cloud process (voids in large-scale collapsing regions), solves problems with a single-scale spherical-collapse scheme. Slight further improvement is possible by mixing in the 2LPT estimate on large scales. Additionally, we show the behavior of {\psi} for different morphologies (voids, walls, filaments, and haloes). A Python code to produce these realizations is available at http://skysrv.pha.jhu.edu/~neyrinck/muscle.html.