A Parametrized Galaxy Catalog Simulator For Testing Cluster Finding,
  Mass Estimation and Photometric Redshift Estimation in Optical and Near
  Infrared Surveys
  We present a galaxy catalog simulator which turns N-body simulations with
subhalos into multiband photometric mocks. The simulator assigns galaxy
properties to each subhalo to reproduce the observed cluster galaxy halo
occupation distribution, the radial and mass dependent variation in fractions
of blue galaxies, the luminosity functions in clusters and the field, and the
red-sequence in clusters. Moreover, the evolution of these parameters is tuned
to match existing observational constraints. Field galaxies are sampled from
existing multiband photometric surveys using derived galaxy photometric
redshifts. Parametrizing an ensemble of cluster galaxy properties enables us to
create mock catalogs with variations in those properties, which in turn allows
us to quantify the sensitivity of cluster finding to current observational
uncertainties in these properties.
  We present an application of the catalog simulator to characterize the
selection function of a galaxy cluster finder that utilizes the cluster
red-sequence galaxy clustering on the sky, in terms of completeness and
contamination. We estimate systematic uncertainties due to the observational
uncertainties on our simulator parameters in determining the selection function
using five different sets of modified catalogs. Our estimates indicate that
these uncertainties are at the $\le15$% level with current observational
constraints on cluster galaxy populations and their evolution. In addition, we
examine the $B_{gc}$ parameter as an optical mass indicator and measure the
intrinsic scatter of the $B_{gc}$--mass relation to be approximately log normal
with $\sigma_{\log_{10}M}\sim0.25$. Finally, we present tests of a red sequence
overdensity redshift estimator using both simulated and real data, showing that
it delivers redshifts for massive clusters with $\sim$2% accuracy out to
redshifts $z\sim0.5$ with SDSS-like datasets.
