Efficient simulations of large scale structure in modified gravity cosmologies with comoving Lagrangian acceleration

TL;DR

This paper adapts the COLA simulation method to efficiently model non-linear structure formation in modified gravity cosmologies, accurately predicting power spectra and relative halo mass function changes for upcoming large-scale surveys.

Contribution

The paper introduces an adapted COLA approach for modified gravity models, effectively modeling screening effects and extending its applicability beyond standard cosmology.

Findings

Achieves 1% accuracy in dark matter power spectra up to k~2.5 h/Mpc.

Effectively models redshift space distortions with a Lorentzian velocity dispersion.

Predicts relative changes in halo mass functions within 1σ uncertainties.

Abstract

We implement an adaptation of the COLA approach, a hybrid scheme that combines Lagrangian perturbation theory with an N-body approach, to model non-linear collapse in chameleon and symmetron modified gravity models. Gravitational screening is modeled effectively through the attachment of a suppression factor to the linearized Klein-Gordon equations. The adapted COLA approach is benchmarked, with respect to an N-body code both for the $\Lambda$CDM scenario and for the modified gravity theories. It is found to perform well in the estimation of the dark matter power spectra, with consistency of 1 % to $k\sim2.5$ h/Mpc. Redshift space distortions are shown to be effectively modeled through a Lorentzian parameterization with a velocity dispersion fit to the data. We find that COLA performs less well in predicting the halo mass functions, but has consistency, within $1\sigma$ uncertainties of our simulations, in the relative changes to the mass function induced by the modified gravity models relative to $\Lambda$CDM. The results demonstrate that COLA, proposed to enable accurate and efficient, non-linear predictions for $\Lambda$CDM, can be effectively applied to a wider set of cosmological scenarios, with intriguing properties, for which clustering behavior needs to be understood for upcoming surveys such as LSST, DESI, Euclid and WFIRST.