Modelling galaxy clustering: halo occupation distribution versus subhalo
  matching
  We model the luminosity-dependent projected and redshift-space two-point
correlation functions (2PCFs) of the Sloan Digital Sky Survey (SDSS) DR7 Main
galaxy sample, using the halo occupation distribution (HOD) model and the
subhalo abundance matching (SHAM) model and its extension. All the models are
built on the same high-resolution $N$-body simulations. We find that the HOD
model generally provides the best performance in reproducing the clustering
measurements in both projected and redshift spaces. The SHAM model with the
same halo-galaxy relation for central and satellite galaxies (or distinct
haloes and subhaloes), when including scatters, has a best-fitting
$\chi^2/\rm{dof}$ around $2$--$3$. We therefore extend the SHAM model to the
subhalo clustering and abundance matching (SCAM) by allowing the central and
satellite galaxies to have different galaxy--halo relations. We infer the
corresponding halo/subhalo parameters by jointly fitting the galaxy 2PCFs and
abundances and consider subhaloes selected based on three properties, the mass
$M_{\rm acc}$ at the time of accretion, the maximum circular velocity $V_{\rm
acc}$ at the time of accretion, and the peak maximum circular velocity $V_{\rm
peak}$ over the history of the subhaloes. The three subhalo models work well
for luminous galaxy samples (with luminosity above $L_*$). For low-luminosity
samples, the $V_{\rm acc}$ model stands out in reproducing the data, with the
$V_{\rm peak}$ model slightly worse, while the $M_{\rm acc}$ model fails to fit
the data. We discuss the implications of the modeling results.
