Numerical relativity waveform surrogate model for generically precessing binary black hole mergers
Numerical relativity waveform surrogate model for generically precessing binary black hole mergers
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DOI：
10.1103/physrevd.96.024058
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发表时间：
2017-05
影响因子：
5
通讯作者：
J. Blackman;Scott E. Field;M. Scheel;C. Galley;C. Ott;M. Boyle;Lawrence E. Kidder;H. Pfeiffer;B. Szil'agyi
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文献类型：
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作者：
J. Blackman;Scott E. Field;M. Scheel;C. Galley;C. Ott;M. Boyle;Lawrence E. Kidder;H. Pfeiffer;B. Szil'agyi
通用的非偏心双星黑洞 (BBH) 系统会发射引力波 (GW)，引力波由七个内在参数完整描述：黑洞自旋矢量及其质量比。通过数值求解爱因斯坦方程来模拟 BBH 聚结的计算成本很高，需要数天到数月的计算资源来计算一组参数值。由于许多不同的源参数通常需要对引力波进行理论预测，因此快速而准确的模型至关重要。我们提出了第一个来自 BBH 合并的 GW 代理模型，包括内在非偏心参数空间的所有七个维度。我们称之为 NRSur7dq2 的替代模型是根据 744 次数值相对论模拟的结果构建的。 NRSur7dq2 涵盖高达 0.8 的自旋幅度和高达 2 的质量比，包括所有 l≤4 模式，在合并之前开始约 20 个轨道，并且可以在 ∼50 ms 内进行评估。我们发现最大的 NRSur7dq2 误差与数值相对论模拟中的最大误差相当，并且比其他波形模型的误差小一个数量级以上。我们的模型，以及更广泛的这里开发的方法，将使以前使用高精度波形无法进行的研究成为可能，例如参数推断和引力波观测的广义相对论测试。
A generic, noneccentric binary black hole (BBH) system emits gravitational waves (GWs) that are completely described by seven intrinsic parameters: the black hole spin vectors and the ratio of their masses. Simulating a BBH coalescence by solving Einstein’s equations numerically is computationally expensive, requiring days to months of computing resources for a single set of parameter values. Since theoretical predictions of the GWs are often needed for many different source parameters, a fast and accurate model is essential. We present the first surrogate model for GWs from the coalescence of BBHs including all seven dimensions of the intrinsic noneccentric parameter space. The surrogate model, which we call NRSur7dq2, is built from the results of 744 numerical relativity simulations. NRSur7dq2 covers spin magnitudes up to 0.8 and mass ratios up to 2, includes all l≤4 modes, begins about 20 orbits before merger, and can be evaluated in ∼50 ms. We find the largest NRSur7dq2 errors to be comparable to the largest errors in the numerical relativity simulations, and more than an order of magnitude smaller than the errors of other waveform models. Our model, and more broadly the methods developed here, will enable studies that were not previously possible when using highly accurate waveforms, such as parameter inference and tests of general relativity with GW observations.