Accurate and efficient waveform model for precessing binary black holes
Accurate and efficient waveform model for precessing binary black holes
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DOI：
10.1103/physrevd.108.064059
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发表时间：
2023-06
影响因子：
5
通讯作者：
Hang Yu;Javier Roulet;T. Venumadhav;B. Zackay;M. Zaldarriaga
中科院分区：
文献类型：
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作者：
Hang Yu;Javier Roulet;T. Venumadhav;B. Zackay;M. Zaldarriaga
我们提出了IMRPhenomXODE，一个新的唯象频域波形近似的引力波（GW）信号从旋进的二元黑洞（BBHs）与一般的自旋配置。我们建立在IMRPhenomXPHM [G. Pratten等人，Phys. Rev. D 103，104056（2021），这是GW数据分析中最广泛采用的波形近似之一，包括自旋进动，并引入了两个额外的显着改进。首先，我们采用了一种有效的技术来数值求解（下一个）$^4$-领先阶后牛顿进动方程，这使得我们能够准确地确定轨道角动量$\boldsymbol{\hat{L}}_{\rm N}$的方向的演变，即使在复杂的进动动力学的情况下，如过渡进动。其次，我们用SEOBNRv 4PHM [S. Ossokine等人，物理修订版D 102，044055（2020）]，以捕获由于进动引起的效应，例如与$\boldsymbol{\hat{L}}_{\rm N}$对齐的自旋分量的变化。通过结合这些新的功能，IMRPhenomXODE实现匹配与SEOBNRv 4PHM是优于99%的质量比$q \geq 1/6$和任意自旋配置的大多数BBHs。相比之下，IMRPhenomXPHM和SEOBNRv 4PHM之间的失配经常超过10%的BBH与$q\lesssim 1/2$和大的面内或反对齐的自旋组件。我们的实现在计算上也是高效的，波形评估时间甚至可以短于具有长持续时间和高频率分辨率的BBH信号的IMRPhenomXPHM。IMRPhenomXODE的准确性和效率使其成为GW事件搜索、参数估计分析和推断潜在人口属性的有价值的工具。
We present IMRPhenomXODE, a new phenomenological frequency-domain waveform approximant for gravitational wave (GW) signals from precessing binary black holes (BBHs) with generic spin configurations. We build upon the success of IMRPhenomXPHM [G. Pratten et al., Phys. Rev. D 103, 104056 (2021), which is one of the most widely adopted waveform approximants in GW data analyses that include spin precession, and introduce two additional significant improvements. First, we employ an efficient technique to numerically solve the (next-to)$^4$-leading-order post-Newtonian precession equations, which allows us to accurately determine the evolution of the orientation of the orbital angular momentum $\boldsymbol{\hat{L}}_{\rm N}$ even in cases with complicated precession dynamics, such as transitional precession. Second, we recalibrate the phase of GW modes in the frame coprecessing with $\boldsymbol{\hat{L}}_{\rm N}$ against SEOBNRv4PHM [S. Ossokine et al., Phys. Rev. D 102, 044055 (2020)] to capture effects due to precession such as variations in the spin components aligned with $\boldsymbol{\hat{L}}_{\rm N}$. By incorporating these new features, IMRPhenomXODE achieves matches with SEOBNRv4PHM that are better than 99% for most BBHs with mass ratios $q \geq 1/6$ and with arbitrary spin configurations. In contrast, the mismatch between IMRPhenomXPHM and SEOBNRv4PHM often exceeds 10% for a BBH with $q\lesssim 1/2$ and large in-plane or antialigned spin components. Our implementation is also computationally efficient, with waveform evaluation times that can even be shorter than those of IMRPhenomXPHM for BBH signals with long durations and hence high frequency resolutions. The accuracy and efficiency of IMRPhenomXODE position it as a valuable tool for GW event searches, parameter estimation analyses, and the inference of underlying population properties.