Fast simulation mapping: From standard to modified gravity cosmologies using the bias assignment method

Abstract

We assess the effectiveness of a non-parametric bias model in generating mock halo catalogues for modified gravity (MG) cosmologies, relying on the distribution of dark matter from either MG or Lambda cold dark matter ( simulations. We aim to generate halo catalogues that effectively capture the distinct impact of MG, ensuring high accuracy in both two- and three-point statistics for a comprehensive analysis of large-scale structures. We investigated the inclusion of MG in non-local bias to directly map the tracers onto fields, which would significantly reduce computational costs. We employed the bias assignment method ( BAM ) to model halo distribution statistics by leveraging seven high-resolution COLA simulations of MG cosmologies. Taking cosmic-web dependences into account when learning the bias relations, we designed two experiments to map the MG effects: one utilising the consistent MG density fields and the other employing the benchmark \ density field. BAM generates MG halo catalogues from both calibration experiments with excellent summary statistics, achieving a $ 1<!PCT!>$ accuracy in the power spectrum across a wide range of $k$ modes, with minimal differences well below 10<!PCT!> for modes subject to cosmic variance, particularly below $k<0.07$ The reduced bispectrum remains consistent with the reference catalogues within 10<!PCT!> for the studied configuration. Our results demonstrate that a non-linear and non-local bias description can model the effects of MG starting from a field.