An optimal survey geometry of weak lensing survey: minimizing super-sample covariance
Ryuichi Takahashi, Shunji Soma, Masahiro Takada, Issha Kayo

TL;DR
This paper identifies optimal survey geometries for weak lensing to minimize super-sample covariance, significantly improving measurement precision and effective area coverage.
Contribution
It proposes specific survey geometries, including elongated and disconnected patch configurations, to reduce super-sample covariance effects in weak lensing surveys.
Findings
Elongated rectangular surveys reduce super-sample covariance by a factor of 2.
Distributing patches with ~15 degrees separation maximizes area coverage and signal-to-noise ratio.
Optimal geometries enable wider multipole coverage and improved measurement precision.
Abstract
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 -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…
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