  The statistics of shear peaks have been shown to provide valuable
cosmological information beyond the power spectrum, and will be an important
constraint of models of cosmology with the large survey areas provided by
forthcoming astronomical surveys. Surveys include masked areas due to bright
stars, bad pixels etc, which must be accounted for in producing constraints on
cosmology from shear maps. We advocate a forward-modeling approach, where the
impact of masking (and other survey artifacts) are accounted for in the
theoretical prediction of cosmological parameters, rather than removed from
survey data. We use masks based on the Deep Lens Survey, and explore the impact
of up to 37% of the survey area being masked on LSST and DES-scale surveys. By
reconstructing maps of aperture mass, the masking effect is smoothed out,
resulting in up to 14% smaller statistical uncertainties compared to simply
reducing the survey area by the masked area. We show that, even in the presence
of large survey masks, the bias in cosmological parameter estimation produced
in the forward-modeling process is ~1%, dominated by bias caused by limited
simulation volume. We also explore how this potential bias scales with survey
area and find that small survey areas are more significantly impacted by the
differences in cosmological structure in the data and simulated volumes, due to
cosmic variance.
