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arXiv 1807.09787gr-qcastro-ph.HEphysics.comp-ph
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融合数值相对论和深度学习来检测偏心二元黑洞合并的高阶多极波形

Fusing numerical relativity and deep learning to detect higher-order multipole waveforms from eccentric binary black hole mergers

Adam Rebei, E. A. Huerta, Sibo Wang, Sarah Habib, Roland Haas, Daniel Johnson, Daniel George

备注：
17 pages, 11 figures, 1 appendix, accepted to Phys. Rev. D
发表信息：
Phys. Rev. D 100, 044025 (2019)
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摘要：

我们确定的质量比，偏心率和二进制的倾角，最大限度地提高了高阶波形多极$(\ell, \, |m|)= \{(2,\,2),\, (2,\,1),\, (3,\,3),\, (3,\,2), \, (3,\,1),\, (4,\,4),\, (4,\,3),\, (4,\,2),\,(4,\,1)\}$的引力波探测偏心双黑洞合并的贡献。我们进行这项研究，使用数值相对论波形，描述非旋转的黑洞二进制的质量比$1\leq q \leq 10$，轨道偏心率高达$e_0=0.18$ 15个周期前合并。对于恒星质量，非对称质量比，二元黑洞合并，并假设LIGO的零失谐高功率配置，我们发现，在参数空间的区域中，$\ell=|m|=2$波形建模的黑洞合并具有消失的信噪比，包括$(\ell, \, |m|)$模式使得能够以在30%到45%的信噪比范围内的信噪比来观测这些源。用$\ell=|m|=2$数值相对论波形模拟最佳取向的二元黑洞合并的噪声比。在确定了$(\ell, \, |m|)$模式对引力波探测很重要的参数空间后，我们构建了描述这些天体物理激励场景的波形信号，并证明了这些拓扑复杂的信号可以用深度学习算法在真实的LIGO噪声中检测和表征。

英文摘要：

We determine the mass-ratio, eccentricity and binary inclination angles that maximize the contribution of the higher-order waveform multipoles $(\ell, \, |m|)= \{(2,\,2),\, (2,\,1),\, (3,\,3),\, (3,\,2), \, (3,\,1),\, (4,\,4),\, (4,\,3),\, (4,\,2),\,(4,\,1)\}$ for the gravitational wave detection of eccentric binary black hole mergers. We carry out this study using numerical relativity waveforms that describe non-spinning black hole binaries with mass-ratios $1\leq q \leq 10$, and orbital eccentricities as high as $e_0=0.18$ fifteen cycles before merger. For stellar-mass, asymmetric mass-ratio, binary black hole mergers, and assuming LIGO's Zero Detuned High Power configuration, we find that in regions of parameter space where black hole mergers modeled with $\ell=|m|=2$ waveforms have vanishing signal-to-noise ratios, the inclusion of $(\ell, \, |m|)$ modes enables the observation of these sources with signal-to-noise ratios that range between 30\% to 45\% the signal-to-noise ratio of optimally oriented binary black hole mergers modeled with $\ell=|m|=2$ numerical relativity waveforms. Having determined the parameter space where $(\ell, \, |m|)$ modes are important for gravitational wave detection, we construct waveform signals that describe these astrophysically motivate scenarios, and demonstrate that these topologically complex signals can be detected and characterized in real LIGO noise with deep learning algorithms.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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