articleThe Astrophysical JournalNov 9, 2010GREEN OA

THE LARGE-SCALE BIAS OF DARK MATTER HALOS: NUMERICAL CALIBRATION AND MODEL TESTS

JLJeremy L. TinkerBEBrant E. RobertsonAVAndrey V. KravtsovAKAnatoly KlypinMSMichael S. Warren

University of California, Berkeley · California Institute of Technology · +5 more institutions

Indexed inarxivcrossrefdoaj

Abstract

We measure the clustering of dark matter halos in a large set of collisionless cosmological simulations of the flat
\nΛCDM cosmology. Halos are identified using the spherical overdensity algorithm, which finds the mass around
\nisolated peaks in the density field such that the mean density is Δ times the background. We calibrate fitting functions
\nfor the large-scale bias that are adaptable to any value of Δ we examine. We find a ~6% scatter about our best-fit
\nbias relation. Our fitting functions couple to the halo mass functions of Tinker et al. such that the bias of all dark
\nmatter is normalized to unity. We demonstrate that the bias of massive, rare halos is higher than that…

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Authors

7
  • JL
    Jeremy L. TinkerCorresponding

    University of California, Berkeley

  • BE
    Brant E. Robertson

    California Institute of Technology

  • AV
    Andrey V. Kravtsov

    Fermi National Accelerator Laboratory, University of Chicago

  • AK
    Anatoly Klypin

    New Mexico State University

  • MS
    Michael S. Warren

    Los Alamos National Laboratory

Topics & keywords

Keywords
  • Halo
  • Dark matter
  • Halo effect
  • Measure (data warehouse)
  • Dark matter halo
  • Cluster analysis
  • Function (biology)
  • Field (mathematics)
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