An optimal survey geometry of weak lensing survey: minimizing
  super-sample covariance
  Upcoming wide-area weak lensing surveys are expensive both in time and cost
and require an optimal survey design in order to attain maximum scientific
returns from a fixed amount of available telescope time. The super-sample
covariance (SSC), which arises from unobservable modes that are larger than the
survey size, significantly degrades the statistical precision of weak lensing
power spectrum measurement even for a wide-area survey. Using the 1000 mock
realizations of the log-normal model, which approximates the weak lensing field
for a $\Lambda$-dominated cold dark matter model, we study an optimal survey
geometry to minimize the impact of SSC contamination. For a continuous survey
geometry with a fixed survey area, a more elongated geometry such as a
rectangular shape of 1:400 side-length ratio reduces the SSC effect and allows
for a factor 2 improvement in the cumulative signal-to-noise ratio ($S/N$) of
power spectrum measurement up to $\ell_{\rm max}\simeq $ a few $10^3$, compared
to compact geometries such as squares or circles. When we allow the survey
geometry to be disconnected but with a fixed total area, assuming $1\times 1$
sq. degrees patches as the fundamental building blocks of survey footprints,
the best strategy is to locate the patches with $\sim 15$ degrees separation.
This separation angle corresponds to the scale at which the two-point
correlation function has a negative minimum. The best configuration allows for
a factor 100 gain in the effective area coverage as well as a factor 2.5
improvement in the $S/N$ at high multipoles, yielding a much wider coverage of
multipoles than in the compact geometry.
