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

Abstract

We performed a series of numerical experiments to quantify the sensitivity of the predictions for weak lensing statistics obtained in raytracing 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 and a series of non-Gaussian observables, including the one-point probability density function, lensing peaks, and Minkowski functionals. Counter-intuitively, we find that using thin lens planes ($<60h^{-1}$Mpc on a $240h^{-1}$Mpc simulation box) suppresses the power spectrum over a broad range of scales beyond what would be acceptable for an LSST-type survey. A mass resolution of $7.2\times 10^{11}{h}^{-1}{M}_{\odot }$ per DM particle (or 256${}^3$ particles in a ($240h^{-1}$Mpc)${}^3$ box) is sufficient to extract information using the power spectrum and non-Gaussian statistics from weak lensing data at angular scales down to 1 arcmin with LSST-like levels of shape noise.

arXiv comment17 pages, 10 figures, accepted to ApJ

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