  We investigate the inclusion of clustering maps in a weak lensing Minkowski
functional (MF) analysis of DES-like and LSST-like simulations to constrain
cosmological parameters. The standard 3x2pt approach to lensing and clustering
data uses two-point correlations as its primary statistic; MFs, morphological
statistics describing the shape of matter fields, provide additional
information for non-Gaussian fields. Previous analyses have studied MFs of
lensing convergence maps; in this project we explore their simultaneous
application to clustering maps. We employ a simplified linear galaxy bias
model, and using a lognormal curved sky measurement and Monte Carlo Markov
Chain (MCMC) sampling process for parameter inference, we find that MFs do not
yield any information in the $\Omega_{\rm m}$ -- $\sigma_8$ plane not already
generated by a 3x2pt analysis. However, we expect that MFs should improve
constraining power when nonlinear baryonic and other small-scale effects are
taken into account. As with a 3x2pt analysis, we find a significant improvement
to constraints when adding clustering data to MF-only and MF$+C_\ell$ shear
measurements, and strongly recommend future higher order statistics be measured
from both convergence and clustering maps.
