Optimizing simulation parameters for weak lensing analyses involving non-Gaussian observables

We performed a series of numerical experiments to quantify the sensitivity of the predictions for weak lensing statistics obtained in ray-tracing dark matter (DM)-only simulations, to two hyper-parameters that influence the accuracy as well as the computational cost of the predictions: the thickness of the lens planes used to build past light cones and the mass resolution of the underlying DM simulation. The statistics considered are the power spectrum (PS) and a series of non-Gaussian observables, including the one-point probability density function, lensing peaks, and Minkowski functionals. Counterintuitively, we find that using thin lens planes (< 60 h−1 Mpc on a 240 h−1 Mpc simulation box) suppresses the PS over a broad range of scales beyond what would be acceptable for a survey comparable to the Large Synoptic Survey Telescope (LSST). A mass resolution of 7.2 × 1011 h−1 M⊙ per DM particle (or 2563 particles in a (240 h−1 Mpc)3 box) is sufficient to extract information using the PS and non-Gaussian statistics from weak lensing data at angular scales down to 1' with LSST-like levels of shape noise.