BAM: bias assignment method to generate mock catalogues. (22nd November 2018)

• Record Type: Journal Article
• Authors: Balaguera-Antolínez, A; Kitaura, Francisco-Shu; Pellejero-Ibáñez, Marcos; Zhao, Cheng; Abel, Tom
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 483:Number 1(2019)
• Page Start: L58
• Page End: L63
• Publication Date: 2018-11-22
• Subjects: cosmology: theory -- large-scale structure of Universe; Astronomy -- Research -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnrasl/sly220
• Languages: English
• ISSNs: 1745-3933

Abstract

ABSTRACT: We present BAM: a novel Bias Assignment Method envisaged to generate mock catalogues. 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 ${\sim } 1\, {\rm {per\, cent}}$ 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 $10\, {\rm {per\, cent}}$ precision studying configurations tracing the quasi-non-linear regime. BAM has the potential to become a standard technique to produce mock halo and galaxy catalogues for future galaxy surveys and cosmological studies being highly accurate, efficient and parameter free.