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
\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 predicted
\nin the modified ellipsoidal collapse model of Sheth et al. and approaches the predictions of the spherical collapse
\nmodel for the rarest halos. Halo bias results based on friends-of-friends halos identified with linking length 0.2 are
\nsystematically lower than for halos with the canonical Δ = 200 overdensity by ~10%. In contrast to our previous
\nresults on the mass function, we find that the universal bias function evolves very weakly with redshift, if at all. We
\nuse our numerical results, both for the mass function and the bias relation, to test the peak-background split model
\nfor halo bias. We find that the peak-background split achieves a reasonable agreement with the numerical results,
\nbut ~20% residuals remain, both at high and low masses.

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