Ray Tracing Simulations of Weak Lensing by Large-Scale Structure

We investigate weak lensing by large-scale structure using ray tracing through N-body simulations. Photon trajectories are followed through high resolution simulations of structure formation to make simulated maps of shear and convergence on the sky. Tests with varying numerical parameters are used to calibrate the accuracy of computed lensing statistics on angular scales from about 1 arcminute to a few degrees. Various aspects of the weak lensing approximation are also tested. For fields a few degrees on a side the shear power spectrum is almost entirely in the nonlinear regime and agrees well with nonlinear analytical predictions. Sampling fluctuations in power spectrum estimates are investigated by comparing several ray tracing realizations of a given model. For survey areas smaller than a degree on a side the main source of scatter is nonlinear coupling to modes larger than the survey. We develop a method which uses this effect to estimate the mass density parameter Omega from the scatter in power spectrum estimates for subregions of a larger survey. We show that the power spectrum can be measured accurately from realistically noisy data on scales corresponding to 1-10 Mpc/h. Non-Gaussian features in the one point distribution function of the weak lensing convergence (reconstructed from the shear) are also sensitive to Omega. We suggest several techniques for estimating Omega in the presence of noise and compare their statistical power, robustness and simplicity. With realistic noise Omega can be determined to within 0.1-0.2 from a deep survey of several square degrees.Comment: 59 pages, 22 figures included. Matches version accepted for Ap

The Discrete radon transform: A more efficient approach to image reconstruction

The Radon transform and its inversion are the mathematical keys that enable tomography. Radon transforms are defined for continuous objects with continuous projections at all angles in [0,π). In practice, however, we pre-filter discrete projections take