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.

我们提出了一个新的 Python 包 gwbench，它实现了完善的 Fisher 信息形式作为一种快速而直接的工具，用于引力波基准测试，即估计信噪比和观测到的引力波的测量误差探测器网络。由于贝叶斯参数估计方法的计算成本很高，这使得它们对引力波形、探测器和探测器网络的科学评估效率较低，特别是在确定它们对大量引力波源传播的影响时，这种基础设施是必要的整个宇宙。gwbench 进一步提供了对探测器位置和灵敏度的快速访问，同时包括地球自转对后者的影响，以及波形模型及其衍生物，同时可以访问 LSC 算法库中可用的大量波形。凭借所提供的功能，gwbench 与引力波天文学中的各种应用相关，例如波形建模、探测器开发、宇宙学和广义相对论测试。