http://arxiv.org/abs/1507.01948
Subhalo abundance matching (SHAM) is a widely-used method to connect galaxies with dark matter structures in numerical simulations. SHAM predictions agree remarkably well with observations, yet they still lack strong theoretical support. Here we examine the performance, search for the best implementation, and analyse the key assumptions of SHAM using cosmological simulations from the EAGLE project. We find that $V_{\rm relax}$, the highest value of the circular velocity attained by a subhalo while it satisfies a relaxation criterion, is the subhalo property that correlates most strongly with galaxy stellar mass ($M_{\rm star}$). Using this parameter in SHAM, we retrieve the real-space clustering of EAGLE to within our statistical uncertainties on scales greater than $2$ Mpc for galaxies with $8.77<\log_{10}(M_{\rm star}[M_\odot])<10.77$. On the other hand, clustering is overestimated by $30\%$ on scales below $2$ Mpc because SHAM slightly overpredicts the fraction of satellites in massive haloes. The agreement is even better in redshift space, where the clustering in EAGLE is recovered to within our statistical uncertainties for all masses and separations. Additionally, we analyse the dependence of galaxy clustering on properties other than halo mass, i.e. the assembly bias. We demonstrate that assembly bias alters the clustering in EAGLE by $25\%$ and that $V_{\rm relax}$ captures its effect to within $15\%$. We trace the small but systematic difference in the predicted clustering of SHAM and EAGLE galaxies to the failure of a fundamental assumption of SHAM: for the same $V_{\rm relax}$, central and satellite subhaloes do not host statistically the same galaxies independently of the host halo mass.
J. Chaves-Montero, R. Angulo, J. Schaye, et. al.
Thu, 9 Jul 15
44/50
Comments: 20 pages, 14 figures, submitted to MNRAS
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