论文标题
GWBENCH：用于重力波基准测试的新型Fisher信息包
Gwbench: a novel Fisher information package for gravitational-wave benchmarking
论文作者
论文摘要
我们提出了一个新的Python软件包GWBENCH，该软件包实施了良好的Fisher Information形式主义，作为一种快速而直接的工具，以实现重力波基准测试，即信噪比的估计和测量值的估计，由检测器网络观察到的重力波。由于贝叶斯参数估计方法的高计算成本，因此需要这种基础设施，这使得它们对重力波形，探测器和探测器网络的科学评估的有效性降低，尤其是在确定它们对遍布整个宇宙的大量重力波源的影响时。 GWBENCH进一步可以快速访问检测器位置和敏感性，同时包括地球旋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算法库中可用的波形主机。通过提供的功能，GWBENCH与重力波天文学中的多种应用有关，例如波形建模，探测器发展，宇宙学和一般相对性测试。
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.
