High precision ringdown modeling: Multimode fits and BMS frames
High precision ringdown modeling: Multimode fits and BMS frames
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DOI：
10.1103/physrevd.105.104015
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发表时间：
2021-10
影响因子：
5
通讯作者：
L. M. Zertuche;Keefe Mitman;N. Khera;L. Stein;M. Boyle;N. Deppe;F. H'ebert;D. Iozzo;Lawrence E. Kidder;Jordan Moxon;H. Pfeiffer;M. Scheel;S. Teukolsky;William Throwe;Nils L. Vu
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文献类型：
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作者：
L. M. Zertuche;Keefe Mitman;N. Khera;L. Stein;M. Boyle;N. Deppe;F. H'ebert;D. Iozzo;Lawrence E. Kidder;Jordan Moxon;H. Pfeiffer;M. Scheel;S. Teukolsky;William Throwe;Nils L. Vu
准正态模（QNM）建模是表征残余黑洞，研究强引力和测试GR的宝贵工具。直到最近，QNM研究才开始关注数值相对论（NR）应变波形的多模拟合。随着GW天文台变得更加敏感，他们将能够解决高阶模式。因此，多模QNM拟合将是至关重要的，反过来需要更彻底地处理在$\mathscr{I}^+$的渐近框架。这项工作的第一个主要成果是一种方法，用于系统地拟合QNM模型包含许多模式的数值波形产生的柯西特征提取（CCE），提取技术，这是已知的解决记忆效应。我们选择的模式来建模的基础上，他们的功率贡献的数字和模型波形之间的残差。我们表明，所有模式的应变失配改善了一个因素的$\sim10^5$时，使用多模拟合，而不是只拟合的$（2，\pm2，n）$模式。我们最重要的结果解决了一个关键点，已被忽视的QNM文献：匹配的Bondi-van der Burg-Metzner-Sachs（BMS）框架的数值波形的QNM模型的重要性。我们表明，通过映射的数值波形$-$表现出记忆效应$-$的BMS框架被称为超级休息帧，有一个改善的全模式应变失配相比，使用的应变波形的BMS框架是不固定的。此外，我们发现，通过映射CCE波形的超级休息帧，我们可以获得所有模式的失配，平均而言，一个因素的$\sim4$优于使用公开的外推波形。我们说明了这些建模增强的有效性，通过将它们应用到NR产生的波形家族，并将我们的结果与以前的QNM研究进行比较。
Quasi-normal mode (QNM) modeling is an invaluable tool for characterizing remnant black holes, studying strong gravity, and testing GR. Only recently have QNM studies begun to focus on multimode fitting to numerical relativity (NR) strain waveforms. As GW observatories become even more sensitive they will be able to resolve higher-order modes. Consequently, multimode QNM fits will be critically important, and in turn require a more thorough treatment of the asymptotic frame at $\mathscr{I}^+$. The first main result of this work is a method for systematically fitting a QNM model containing many modes to a numerical waveform produced using Cauchy-characteristic extraction (CCE), an extraction technique which is known to resolve memory effects. We choose the modes to model based on their power contribution to the residual between numerical and model waveforms. We show that the all-mode strain mismatch improves by a factor of $\sim10^5$ when using multimode fitting as opposed to only fitting the $(2,\pm2,n)$ modes. Our most significant result addresses a critical point that has been overlooked in the QNM literature: the importance of matching the Bondi-van der Burg-Metzner-Sachs (BMS) frame of the numerical waveform to that of the QNM model. We show that by mapping the numerical waveforms$-$which exhibit the memory effect$-$to a BMS frame known as the super rest frame, there is an improvement of $\sim10^5$ in the all-mode strain mismatch compared to using a strain waveform whose BMS frame is not fixed. Furthermore, we find that by mapping CCE waveforms to the super rest frame, we can obtain all-mode mismatches that are, on average, a factor of $\sim4$ better than using the publicly-available extrapolated waveforms. We illustrate the effectiveness of these modeling enhancements by applying them to families of waveforms produced by NR and comparing our results to previous QNM studies.