Wavenumber

Physics

Perturbation (astronomy)

Mathematics

Truncation error

Truncation (statistics)

Bispectrum

Magnetic field

Statistical physics

Statistics

Initialization

Crossover

Spectral density

Artificial intelligence

Perturbation theory (quantum mechanics)

Programming language

Computer science

Quantum mechanics

Applied mathematics

Classical mechanics

Equipartition theorem

Monthly Notices of the Royal Astronomical Society

Inaccuracies in the initial conditions for cosmological N-body simulations could easily be the largest source of systematic error in predicting the non-linear large-scale structure. From the theory side, initial conditions are usually provided by using low-order truncations of the displacement field from Lagrangian perturbation theory, with the first and second-order approximations being the most common ones. Here we investigate the improvement brought by using initial conditions based on third-order Lagrangian perturbation theory (3LPT). We show that with 3LPT, truncation errors are vastly suppressed, thereby opening the portal to initializing simulations accurately as late as z=12 (for the resolution we consider). We analyse the competing effects of perturbative truncation and particle discreteness on various summary statistics. Discreteness errors are essentially decaying modes and thus get strongly amplified for earlier initialization times. We show that late starting times with 3LPT provide the most accurate configuration, which we find to coincide with the continuum fluid limit within 1 per cent for the power- and bispectrum at z=0 up to the particle Nyquist wave number of our simulations (k ~ 3h/Mpc). In conclusion, to suppress non-fluid artefacts, we recommend initializing simulations as late as possible with 3LPT. We make our 3LPT initial condition generator publicly available.

Accurate initial conditions for cosmological <i>N</i>-body simulations: minimizing truncation and discreteness errors

Accurate initial conditions for cosmological N-body simulations: minimizing truncation and discreteness errors