BAM: bias assignment method to generate mock catalogues

Journal Article · 21 November 2018 · Monthly Notices of the Royal Astronomical Society: Letters

DOI: https://doi.org/10.1093/mnrasl/sly220 · OSTI ID:1562507

Balaguera-Antolínez, A. ^[1]; Kitaura, Francisco-Shu ^[1]; Pellejero-Ibáñez, Marcos ^[1]; Zhao, Cheng ^[2]; Abel, Tom ^[3]

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1. Univ. of La Laguna, San Cristobal de La Laguna (Spain). Departamento de Astrofísica, Instituto de Astrofísica de Canarias
2. Chinese Academy of Sciences (CAS), Beijing (China). National Astronomy Observatories
3. SLAC National Accelerator Lab., Menlo Park, CA (United States). Kavli Inst. for Particle Astrophysics and Cosmology

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 ~1% accuracy in the majority of the k-range up to the Nyquist frequency without systematic deviations for power spectra (about $k ~ 1h$ 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-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.

• Research Organization: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC02-76SF00515
• OSTI ID: 1562507
• Journal Information: Monthly Notices of the Royal Astronomical Society: Letters, Journal Name: Monthly Notices of the Royal Astronomical Society: Letters Journal Issue: 1 Vol. 483; ISSN 1745-3925
• Publisher: Royal Astronomical SocietyCopyright Statement
• Country of Publication: United States
• Language: English