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

TL;DR

The paper introduces gwforge, a Python package designed to generate mock gravitational-wave data for next-generation detectors, facilitating research and development in data analysis, astrophysics, and fundamental physics.

Contribution

gwforge is a new, user-friendly Python tool that simplifies the creation of simulated gravitational-wave data for advanced detector studies.

Findings

Demonstrates data simulation capabilities with example applications

Highlights potential research uses like noise analysis and cosmology

Shows impact of waveform systematics on parameter estimation

Abstract

The next-generation gravitational-wave detectors, with their improved sensitivity and wider frequency bandwidth, will be capable of observing almost every compact binary 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. 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.