Modeling Redshift-Space Clustering with Abundance Matching
  We explore the degrees of freedom required to jointly fit projected and
redshift-space clustering of galaxies selected in three bins of stellar mass
from the Sloan Digital Sky Survey Main Galaxy Sample (SDSS MGS) using a subhalo
abundance matching (SHAM) model. We employ emulators for relevant clustering
statistics in order to facilitate our analysis, leading to large speed gains
with minimal loss of accuracy. We are able to simultaneously fit the projected
and redshift-space clustering of the two most massive galaxy samples that we
consider with just two free parameters: scatter in stellar mass at fixed SHAM
proxy and the dependence of the SHAM proxy on dark matter halo concentration.
We find some evidence for models that include velocity bias, but including
orphan galaxies improves our fits to the lower mass samples significantly. We
also model the clustering signals of specific star formation rate (SSFR)
selected samples using conditional abundance matching (CAM). We obtain
acceptable fits to projected and redshift-space clustering as a function of
SSFR and stellar mass using two CAM variants, although the fits are worse than
for stellar mass-selected samples alone. By incorporating non-unity
correlations between the CAM proxy and SSFR we are able to resolve previously
identified discrepancies between CAM predictions and SDSS observations of the
environmental dependence of quenching for isolated central galaxies.
