English

Emulators for the non-linear matter power spectrum beyond $\Lambda$CDM

Cosmology and Nongalactic Astrophysics 2020-01-01 v2 General Relativity and Quantum Cosmology

Abstract

Accurate predictions for the non-linear matter power spectrum are needed to confront theory with observations in current and near future weak lensing and galaxy clustering surveys. We propose a computationally cheap method to create an emulator for modified gravity models by utilizing existing emulators for Λ\LambdaCDM. Using a suite of NN-body simulations we construct a fitting function for the enhancement of both the linear and non-linear matter power spectrum in the commonly studied Hu-Sawicki f(R)f(R) gravity model valid for wave-numbers k510hMpc1k \lesssim 5-10\, h\text{Mpc}^{-1} and redshifts z3z \lesssim 3. We show that the cosmology dependence of this enhancement is relatively weak so that our fit, using simulations coming from only one cosmology, can be used to get accurate predictions for other cosmological parameters. We also show that the cosmology dependence can, if needed, be included by using linear theory, approximate NN-body simulations (such as COLA) and semi-analytical tools like the halo model. Our final fit can easily be combined with any emulator or semi-analytical models for the non-linear Λ\LambdaCDM power spectrum to accurately, and quickly, produce a non-linear power spectrum for this particular modified gravity model. The method we use can be applied to fairly cheaply construct an emulator for other modified gravity models. As an application of our fitting formula we use it to compute Fisher-forecasts for how well galaxy clustering and weak lensing in a Euclid-like survey will be at constraining modifications of gravity.

Keywords

Cite

@article{arxiv.1903.08798,
  title  = {Emulators for the non-linear matter power spectrum beyond $\Lambda$CDM},
  author = {Hans Winther and Santiago Casas and Marco Baldi and Kazuya Koyama and Baojiu Li and Lucas Lombriser and Gong-Bo Zhao},
  journal= {arXiv preprint arXiv:1903.08798},
  year   = {2020}
}

Comments

12 pages, 10 figures. Version accepted for publication in PRD. Data can be found at https://github.com/HAWinther/FofrFittingFunction

R2 v1 2026-06-23T08:14:33.809Z