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Cluster number counts can constrain the properties of dark energy if and only if the evolution in the relationship between observable quantities and the cluster mass can be calibrated. Next generation surveys with ~10000 clusters will have sufficient statistics to enable some degree of self-calibration. The excess variance of counts due to the clustering of clusters provides such an opportunity and can be measured from the survey without additional observational cost. It can minimize the degradation in dark energy constraints due to an unknown power law evolution in the mass-observable relation improving constraints on the dark energy equation of state by a factor of 2 or more to sigma(w)=0.06 for a deep 4000 deg2 survey.
In recent years, the amplitude of matter fluctuations inferred from low-redshift probes has been found to be generally lower than the value derived from CMB observations in the $Lambda$CDM model. This tension has been exemplified by Sunyaev-Zeldovich
Non-linear bias measurements require a great level of control of potential systematic effects in galaxy redshift surveys. Our goal is to demonstrate the viability of using Counts-in-Cells (CiC), a statistical measure of the galaxy distribution, as a
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
The center determination of a galaxy cluster from an optical cluster finding algorithm can be offset from theoretical prescriptions or $N$-body definitions of its host halo center. These offsets impact the recovered cluster statistics, affecting both
(Abridged) Combining cosmic shear power spectra and cluster counts is powerful to improve cosmological parameter constraints and/or test inherent systematics. However they probe the same cosmic mass density field, if the two are drawn from the same s