BAM: Bias Assignment Method to generate mock catalogs

TL;DR

BAM is a new method for generating accurate mock galaxy catalogs by modeling halo bias using dark matter simulations, achieving high precision in power spectra and bispectra without systematic deviations.

Contribution

The paper introduces BAM, a novel bias assignment method that efficiently produces highly accurate mock catalogs by combining dark matter statistics and density fields with an iterative sampling process.

Findings

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

Reproduces bispectra within 10% in the quasi-nonlinear regime.

Highly efficient and parameter-free, suitable for future surveys.

Abstract

We present BAM: a novel Bias Assignment Method envisaged to generate mock catalogs. Combining the statistics of dark matter tracers from a high resolution cosmological $N$-body simulation and the dark matter density field calculated from down-sampled initial conditions using efficient structure formation solvers, we extract the halo-bias relation on a mesh of a $3\,h^{-1}$ Mpc cell side resolution as a function of properties of the dark matter density field (e.g. local density, cosmic web type), automatically including stochastic, deterministic, local and non-local components. We use this information to sample the halo density field, accounting for ignored dependencies through an iterative process. By construction, our approach reaches $\sim 1\%$ accuracy in the majority of the $k$-range up to the Nyquist frequency without systematic deviations for power spectra (about $k \sim 1\, h$ Mpc$^{-1}$) using either particle mesh or Lagrangian perturbation theory based solvers. When using phase-space mapping to compensate the low resolution of the approximate gravity solvers, our method reproduces the bispectra of the reference within $10\%$ precision studying configurations tracing the quasi-nonlinear regime. BAM has the potential to become a standard technique to produce mock halo and galaxy catalogs for future galaxy surveys and cosmological studies being highly accurate, efficient and parameter free.