Tracing cosmic voids with fast simulations

Cosmic voids are vast underdense regions in the cosmic web that encode crucial information about structure formation, the composition of the Universe, and its expansion history. Due to their lower density, these regions are less affected by non-linear gravitational dynamics, making them suitable candidates for analysis using semi-analytic methods. We assess the accuracy of the code, a fast tool for generating dark matter halo catalogs based on Lagrangian perturbation theory, in modeling the statistical properties of cosmic voids. We validate this approach by comparing the resulting void statistics measured from to those obtained from N-body simulations. We generate a set of simulations using and assuming a fiducial cosmology and varying the resolution. For a given resolution, the simulations share the same initial conditions between the two codes. Snapshots are saved at multiple redshifts and post-processed using the watershed void finder ̌ide to identify cosmic voids. For each simulation, we measure the following statistics: void size function, void ellipticity function, core density function, and the void radial density profile. We use these statistics to quantify the accuracy of relative to in the context of cosmic voids. We find agreement for all void statistics at better than 2σ between and with no systematic difference in redshift trends. This demonstrates that the code can reliably produce void statistics with high computational efficiency compared to full N-body simulations.