The Large Scale Bias of Dark Matter Halos: Numerical Calibration and Model Tests
Jeremy L. Tinker, Brant E. Robertson, Andrey V. Kravtsov, Anatoly, Klypin, Michael S. Warren, Gustavo Yepes, Stefan Gottlober

TL;DR
This paper calibrates and tests models of dark matter halo bias using large cosmological simulations, revealing systematic differences from theoretical predictions and weak redshift evolution.
Contribution
It provides new calibration functions for halo bias across different overdensities and tests the peak-background split model against simulation data.
Findings
Bias of massive halos exceeds Sheth, Mo, & Tormen predictions.
Halo bias evolution with redshift is very weak.
Systematic differences depend on halo identification method.
Abstract
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…
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