Cosmological Hydrodynamic Simulations with Suppressed Variance in the Lyα Forest Power Spectrum

Abstract

Abstract We test a method to reduce unwanted sample variance when predicting Lyα forest power spectra from cosmological hydrodynamical simulations. Sample variance arises due to sparse sampling of modes on large scales and propagates to small scales through nonlinear gravitational evolution. To tackle this, we generate initial conditions in which the density perturbation amplitudes are fixed to the ensemble average power spectrum—and are generated in pairs with exactly opposite phases. We run 50 such simulations (25 pairs) and compare their performance against 50 standard simulations by measuring the Lyα 1D and 3D power spectra at redshifts z = 2, 3, and 4. Both ensembles use periodic boxes of 40 h − 1 Mpc containing 5123 particles each of dark matter and gas. As a typical example of improvement, for wavenumbers k = 0.25 h Mpc − 1 at z = 3, we find estimates of the 1D and 3D power spectra converge 34 and 12 times faster in a paired–fixed ensemble compared with a standard ensemble. We conclude that, by reducing the computational time required to achieve fixed accuracy on predicted power spectra, the method frees up resources for exploration of varying thermal and cosmological parameters—ultimately allowing the improved precision and accuracy of statistical inference.