Forecasting Super-Sample Covariance in Future Weak Lensing Surveys with
  SuperSCRAM
  The observable universe contains density perturbations on scales larger than
any finite volume survey. Perturbations on scales larger than a survey can
measure degrade its power to constrain cosmological parameters. The dependence
of survey observables such as the weak lensing power spectrum on these
long-wavelength modes results in super-sample covariance. Accurately
forecasting parameter constraints for future surveys requires accurately
accounting for the super-sample effects. If super-sample covariance is in fact
a major component of the survey error budget, it may be necessary to
investigate mitigation strategies that constrain the specific realization of
the long-wavelength modes. We present a Fisher matrix based formalism for
approximating the magnitude of super-sample covariance and the effectiveness of
mitigation strategies for realistic survey geometries. We implement our
formalism in the public code SuperSCRAM: Super-Sample Covariance Reduction and
Mitigation. We illustrate SuperSCRAM with an example application, where the
modes contributing to super-sample covariance in the WFIRST weak lensing survey
are constrained by the low-redshift galaxy number counts in the wider LSST
footprint. We find that super-sample covariance increases the volume of the
error ellipsoid in 7D cosmological parameter space by a factor of 4.5 relative
to Gaussian statistical errors only, but our simple mitigation strategy more
than halves the contamination, to a factor of 2.0.
