gwforge: a user-friendly package to generate gravitational-wave mock data

Abstract Next-generation gravitational-wave detectors, with their improved sensitivity and wider frequency bandwidth, will be capable of observing almost every compact binary coalescence signal from epochs before the first stars began to form, increasing the number of detectable binaries to hundreds of thousands annually. This will enable us to observe compact objects through cosmic time, probe extreme matter phenomena, do precision cosmology, study gravity in strong field dynamical regimes and potentially allow observation of fundamental physics beyond the standard model. However, the richer data sets produced by these detectors will pose new computational, physical and astrophysical challenges, necessitating the development of novel algorithms and data analysis strategies. To aid in these efforts, this paper introduces gwforge , a user-friendly, lightweight Python package, to generate mock data for next-generation detectors. gwforge allows users to seamlessly simulate data while abstracting away technical complexities, enabling more efficient testing and development of analysis pipelines. Additionally, the package’s data generation process is optimized using high-throughput systems like HTCondor , significantly speeding up the simulation of large populations of gravitational-wave events. We demonstrate the package’s capabilities through data simulation examples and highlight a few potential applications: performance loss due to foreground noise, bright-siren cosmology and impact of waveform systematics on binary parameter estimation.