# Forecasting Super-Sample Covariance in Future Weak Lensing Surveys with SuperSCRAM

## TL;DR

This paper introduces a formalism and a public code, SuperSCRAM, to forecast and mitigate super-sample covariance effects in future weak lensing surveys, improving parameter constraint accuracy.

## Contribution

It presents a Fisher matrix-based approach and a practical implementation for estimating and reducing super-sample covariance in cosmological surveys.

## Key findings

- Super-sample covariance can significantly inflate error volumes in parameter space.
- Mitigation strategies can reduce super-sample covariance effects by more than half.
- Application to WFIRST and LSST data demonstrates practical effectiveness.

## Abstract

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.