Bye-bye, Local-in-matter-density Bias: The Statistics of the Halo Field Are Poorly Determined by the Local Mass Density

Abstract

Bias models relating the dark matter field to the spatial distribution of halos are widely used in current cosmological analyses. Many models predict halos purely from the local Eulerian matter density, yet bias models in perturbation theory require other local properties. We assess the validity of assuming that only the local dark matter density can be used to predict the number density of halos in a model-independent way and in the non-perturbative regime. Utilising $N$-body simulations, we study the properties of the halo counts field after spatial voxels with near-equal dark matter density have been permuted. If local-in-matter-density biasing were valid, the statistical properties of the permuted and un-permuted fields would be indistinguishable since both represent equally fair draws of the stochastic biasing model. If the Lagrangian radius is greater than approximately half the voxel size and for halos less massive than $\sim 10^{15}{h}^{-1}{\mathrm{M}}_{\odot }$, we find the permuted halo field has a scale-dependent bias with greater than 25% more power on scales relevant for current surveys. These bias models remove small-scale power by not modelling correlations between neighbouring voxels, which substantially boosts large-scale power to conserve the field's total variance. This conclusion is robust to the choice of initial conditions and cosmology. Assuming local-in-matter-density halo biasing cannot, therefore, reproduce the distribution of halos across a large range of scales and halo masses, no matter how complex the model. One must either allow the biasing to be a function of other quantities and/or remove the assumption that neighbouring voxels are statistically independent.

arXiv comment10 pages, 5 figures. Accepted in ApJL

References in corpus (45)

• Array Programming with NumPy
• Astropy: A Community Python Package for Astronomy
• The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package
• The cosmological simulation code GADGET-2
• The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package
• Large-Scale Structure of the Universe and Cosmological Perturbation Theory
• The Rockstar Phase-Space Temporal Halo Finder and the Velocity Offsets of Cluster Cores
• Large-Scale Galaxy Bias
• The Effective Field Theory of Cosmological Large Scale Structures
• Biasing and Hierarchical Statistics in Large-scale Structure
• Bayesian physical reconstruction of initial conditions from large scale structure surveys
• Bias in the Effective Field Theory of Large Scale Structures
• Biased Tracers and Time Evolution
• Reconstructed Density and Velocity Fields from the 2MASS Redshift Survey
• Physical Bayesian modelling of the non-linear matter distribution: new insights into the Nearby Universe
• Nonlinear Perturbation Theory Integrated with Nonlocal Bias, Redshift-space Distortions, and Primordial Non-Gaussianity
• Modeling Biased Tracers at the Field Level
• Past and present cosmic structure in the SDSS DR7 main sample
• Bayesian power-spectrum inference for Large Scale Structure data
• The 2dF Galaxy Redshift Survey: Wiener Reconstruction of the Cosmic Web
• Unmasking the Masked Universe: the 2M++ catalogue through Bayesian eyes
• Beyond LIMD bias: a measurement of the complete set of third-order halo bias parameters
• Cosmological Reconstruction From Galaxy Light: Neural Network Based Light-Matter Connection
• The BOSS bispectrum analysis at one loop from the Effective Field Theory of Large-Scale Structure
• Cosmic Cartography of the Large-Scale Structure with Sloan Digital Sky Survey Data Release 6
• A rigorous EFT-based forward model for large-scale structure
• Bayesian inference of cosmic density fields from non-linear, scale-dependent, and stochastic biased tracers
• Cosmological inference from Bayesian forward modelling of deep galaxy redshift surveys
• Bayesian inference from photometric redshift surveys
• On density and velocity fields and beta from the IRAS PSCz survey
• Diagrammatic Methods in Statistics and Biasing in the Large-scale Structure of the Universe
• Cosmology Inference from Biased Tracers using the EFT-based Likelihood
• Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian Deep Learning
• Testing standard perturbation theory and the Eulerian local biasing scheme against N-body simulations
• BAM: Bias Assignment Method to generate mock catalogs
• Consistency tests of field level inference with the EFT likelihood
• A robust measurement of the first higher-derivative bias of dark matter halos
• Impacts of the physical data model on the forward inference of initial conditions from biased tracers
• Cosmic velocity--gravity relation in redshift space
• Cosmology inference at the field level from biased tracers in redshift-space
• Neural physical engines for inferring the halo mass distribution function
• The bias of dark matter tracers: assessing the accuracy of mapping techniques
• One simulation to have them all: performance of the Bias Assignment Method against N-body simulations
• The cosmic web connection to the dark matter halo distribution through gravity
• Lagrangian bias in the local bias model

Cited by in corpus (1)

• Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum