Combination of cluster number counts and two-point correlations:
  Validation on Mock Dark Energy Survey
  We present a method of combining cluster abundances and large-scale two-point
correlations, namely galaxy clustering, galaxy--cluster cross-correlations,
cluster auto-correlations, and cluster lensing. This data vector yields
comparable cosmological constraints to traditional analyses that rely on
small-scale cluster lensing for mass calibration. We use cosmological survey
simulations designed to resemble the Dark Energy Survey Year One (DES-Y1) data
to validate the analytical covariance matrix and the parameter inferences. The
posterior distribution from the analysis of simulations is statistically
consistent with the absence of systematic biases detectable at the precision of
the DES Y1 experiment. We compare the $\chi^2$ values in simulations to their
expectation and find no significant difference. The robustness of our results
against a variety of systematic effects is verified using a simulated
likelihood analysis of a Dark Energy Survey Year 1-like data vectors. This work
presents the first-ever end-to-end validation of a cluster abundance
cosmological analysis on galaxy catalog-level simulations.
