The Large Scale Bias of Dark Matter Halos: Numerical Calibration and
  Model Tests
  We measure the clustering of dark matter halos in a large set of
collisionless cosmological simulations of the flat LCDM cosmology. Halos are
identified using the spherical overdensity algorithm, which finds the mass
around isolated peaks in the density field such that the mean density is Delta
times the background. We calibrate fitting functions for the large scale bias
that are adaptable to any value of Delta we examine. We find a ~6% scatter
about our best fit bias relation. Our fitting functions couple to the halo mass
functions of Tinker et. al. (2008) such that bias of all dark matter is
normalized to unity. We demonstrate that the bias of massive, rare halos is
higher than that predicted in the modified ellipsoidal collapse model of Sheth,
Mo, & Tormen (2001), and approaches the predictions of the spherical collapse
model for the rarest halos. Halo bias results based on friends-of-friends halos
identified with linking length 0.2 are systematically lower than for halos with
the canonical Delta=200 overdensity by ~10%. In contrast to our previous
results on the mass function, we find that the universal bias function evolves
very weakly with redshift, if at all. We use our numerical results, both for
the mass function and the bias relation, to test the peak-background split
model for halo bias. We find that the peak-background split achieves a
reasonable agreement with the numerical results, but ~20% residuals remain,
both at high and low masses.
