GWSim: Python package for creating mock GW samples for different astrophysical populations and cosmological models of binary black holes

Precision cosmology with gravitational wave (GW) sources requires a deeper understanding of the interplay between GW source population and cosmological parameters governing the dynamics of the Universe. With the swift increase in GW detections, it is necessary to develop a tool for exploring many aspects of cosmology and fundamental physics; this tools allows to simulate GW mock samples for several populations and cosmological models. We have developed a new code called GWSim, which allows us to make a large sample of GW mock events from a broad range of configurations, while varying the cosmology, the merger rate, and the GW source parameters (i.e. mass and spin distributions in particular) for a given network of GW detectors. A large sample of simulated mock GW events will be useful for improving our understanding of the statistical properties of the distribution of GW sources, as long as it is detectable for a given detector noise and an astrophysical and cosmological model. It will also be useful to compare simulated samples with the observed distribution of the GW sources from data and infer the underlying population of the GW source parameters and cosmology. We restricted the cosmology to spatially flat universes, including models with varying dark energy equation of state. The GWSim code provides each mock event with a position in the sky and a redshift; these values can be those of random host galaxies coming from an isotropic and homogeneous simulated Universe or a user-supplied galaxy catalog. We used realistic detector configurations of the LIGO and Virgo network of detectors to demonstrate the performance of this code for the latest observation runs and the upcoming observation run.