  Developing accurate analysis techniques to combine various probes of
cosmology is essential to tighten constraints on cosmological parameters and to
check for inconsistencies in our model of the Universe. In this paper we
develop a joint analysis framework for six different second-order statistics
calculated from three tracers of the dark matter density field, namely galaxy
position, shear, and magnification. We extend a data compression scheme
developed in the context of shear-shear statistics (the so-called COSEBIs) to
the other five second-order statistics, thereby significantly reducing the
number of data points in the joint data vector. We use CosmoLike, a newly
developed software framework for joint likelihood analyses, to forecast
parameter constraints for the Dark Energy Survey (DES). The simulated MCMCs
cover a five dimensional cosmological parameter space comparing the information
content of the individual probes to several combined probes (CP) data vectors.
Given the significant correlations of these second-order statistics we model
all cross terms in the covariance matrix; furthermore we go beyond the Gaussian
covariance approximation and use the halo model to include higher order
correlations of the density field. We find that adding magnification
information (including cross probes with shear and clustering) noticeably
increases the information content and that the correct modeling of the
covariance (i.e., accounting for non-Gaussianity and cross terms) is essential
for accurate likelihood contours from the CP data vector. We also identify
several nulltests based on the degeneracy of magnification and shear statistics
which can be used to quantify the contamination of data sets by astrophysical
systematics and/or calibration issues.
