Gwbench: a novel Fisher information package for gravitational-wave benchmarking

TL;DR

gwbench is a Python package that uses Fisher information to efficiently benchmark gravitational-wave detectors and waveforms, enabling rapid estimation of signal-to-noise ratios and measurement errors without heavy computational costs.

Contribution

The paper introduces gwbench, a new Python tool that simplifies and accelerates gravitational-wave benchmarking using Fisher information, including detector effects and waveform derivatives.

Findings

Provides fast estimation of SNR and errors for gravitational waves.

Includes Earth's rotation effects on detector sensitivities.

Access to waveform models from the LSC Algorithm Library.

Abstract

We present a new Python package, gwbench, implementing the well-established Fisher information formalism as a fast and straightforward tool for the purpose of gravitational-wave benchmarking, i.e. the estimation of signal-to-noise ratios and measurement errors of gravitational waves observed by a network of detectors. Such an infrastructure is necessary due to the high computational cost of Bayesian parameter estimation methods which renders them less effective for the scientific assessment of gravitational waveforms, detectors, and networks of detectors, especially when determining their effects on large populations of gravitational-wave sources spread throughout the universe. gwbench further gives quick access to detector locations and sensitivities, while including the effects of Earth's rotation on the latter, as well as waveform models and their derivatives, while giving access to the host of waveforms available in the LSC Algorithm Library. With the provided functionality, gwbench is relevant for a wide variety of applications in gravitational-wave astronomy such as waveform modeling, detector development, cosmology, and tests of general relativity.