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The effects of assembly bias on cosmological inference from galaxy-galaxy lensing and galaxy clusters

Published 11 Jan 2016 in astro-ph.CO | (1601.02693v1)

Abstract: The combination of galaxy-galaxy lensing (GGL) and galaxy clustering is a promising route to measuring the amplitude of matter clustering and testing modified gravity theories of cosmic acceleration. Halo occupation distribution (HOD) modeling can extend the approach down to nonlinear scales, but galaxy assembly bias could introduce systematic errors by causing the HOD to vary with large scale environment at fixed halo mass. We investigate this problem using the mock galaxy catalogs created by Hearin & Watson (2013, HW13), which exhibit significant assembly bias because galaxy luminosity is tied to halo peak circular velocity and galaxy colour is tied to halo formation time. The preferential placement of galaxies (especially red galaxies) in older halos affects the cutoff of the mean occupation function $\langle N_\text{cen}(M_\text{min}) \rangle$ for central galaxies, with halos in overdense regions more likely to host galaxies. The effect of assembly bias on the satellite galaxy HOD is minimal. We introduce an extended, environment dependent HOD (EDHOD) prescription to describe these results and fit galaxy correlation measurements. Crucially, we find that the galaxy-matter cross-correlation coefficient, $r_{gm} \equiv \xi_{gm} \cdot [ \xi_{mm} \xi_{gg} ]{-1/2}$, is insensitive to assembly bias on scales $r \gtrsim 1 \; h{-1}\text{Mpc}$, even though $\xi_{gm} $ and $\xi_{gg} $ are both affected individually. We can therefore recover the correct $\xi_{mm} $ from the HW13 galaxy-galaxy and galaxy-matter correlations using either a standard HOD or EDHOD fitting method. For $M_r \leq -19$ or $M_r \leq -20$ samples the recovery of $\xi_{mm}$ is accurate to 2% or better. For a sample of red $M_r \leq -20$ galaxies we achieve 2% recovery at $r \gtrsim 2\;h{-1}\text{Mpc}$ with EDHOD modeling but lower accuracy at smaller scales or with a standard HOD fit.

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