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

Context. 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 Λ cold dark matter (ΛCDM) simulations. Aims. 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 ΛCDM fields, which would significantly reduce computational costs. Methods. We employed the bias assignment method (<ns0:monospace>BAM</ns0:monospace>) to model halo distribution statistics by leveraging seven high-resolution <ns0:monospace>COLA</ns0:monospace> 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 ΛCDM density field. Results. <ns0:monospace>BAM</ns0:monospace> generates MG halo catalogues from both calibration experiments with excellent summary statistics, achieving a ~1% accuracy in the power spectrum across a wide range of k modes, with minimal differences well below 10% for modes subject to cosmic variance, particularly below k < 0.07 h Mpc−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 field.