GWSim: Python package for creating mock GW samples for different astrophysical populations and cosmological models of binary black holes
Christos Karathanasis, Benoit Revenu, Suvodip Mukherjee, Federico, Stachurski

TL;DR
GWSim is a Python tool that generates large, customizable mock gravitational wave samples across various cosmological models, aiding in understanding GW source populations and cosmological parameters.
Contribution
It introduces GWSim, a versatile code for simulating GW events with adjustable astrophysical and cosmological parameters, enhancing analysis capabilities for current and future GW observations.
Findings
Demonstrated GWSim's performance with LIGO and Virgo configurations.
Generated large mock GW samples for different cosmological models.
Enabled comparison between simulated and observed GW source distributions.
Abstract
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…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Radio Astronomy Observations and Technology · Cosmology and Gravitation Theories
