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 ΛCDM. 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 comprehensive analysis of large-scale structures. As part of this study we aim at investigating the inclusion of MG into non-local bias to directly map the tracers onto ΛCDM fields, which would save many computational costs. We employ the bias assignment method (BAM) to model halo distribution statistics by leveraging seven high-resolution COLA simulations of MG cosmologies. Taking into account cosmic-web dependencies when learning the bias relations, we design two experiments to map the MG effects: one utilising the consistent MG density fields and the other employing the benchmark ΛCDM density field. BAM generates MG halo catalogues from both calibrations experiments excelling in summary statistics, achieving a ∼1% accuracy in the power spectrum across a wide range of k-modes, with only minimal differences well below 10\% at modes subject to cosmic variance, particularly below k<0.07hMpc−1. The reduced bispectrum remains consistent with the reference catalogues within 10\% 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 ΛCDM dark matter field.
@article{arxiv.2405.10319,
title = {Fast simulation mapping: from standard to modified gravity cosmologies using the bias assignment method},
author = {Jorge Enrique García-Farieta and Andrés Balaguera-Antolínez and Francisco-Shu Kitaura},
journal= {arXiv preprint arXiv:2405.10319},
year = {2024}
}