Fusing numerical relativity and deep learning to detect higher-order multipole waveforms from eccentric binary black hole mergers
Fusing numerical relativity and deep learning to detect higher-order multipole waveforms from eccentric binary black hole mergers
复制标题
DOI：
10.1103/physrevd.100.044025
复制
发表时间：
2018-07
影响因子：
5
通讯作者：
A. Rebei;E. Huerta;Si-Yang Wang;Sarah Habib;R. Haas;Daniel Johnson;D. George
中科院分区：
文献类型：
--
作者：
A. Rebei;E. Huerta;Si-Yang Wang;Sarah Habib;R. Haas;Daniel Johnson;D. George
我们确定了使高阶波形多极$(\ell，\，|m|)=\{(2，\，2)，\，(2，\，1)，\，(3，\，3)，\，(3，\，2)，\，(3，\，2)，\，(3，\，1)，\，(4，\，4)，\，(4，\，3)，\，(4，\，3)，\，(4，\，3)，\，2)，(4，1)$用于偏心双星黑洞合并的引力波探测。我们使用数值相对论波形进行这项研究，这些波形描述了质量比为$1\leq\q\leq 10$的非自旋黑洞双星，合并前15周的轨道偏心率高达$e_0=0.18$。对于恒星质量、非对称质量比、二元黑洞合并，并假设LIGO的零失谐高功率组态，我们发现在参数空间中以$\ell=|m|=2$波形模拟的黑洞合并的信噪比为零的区域，加入$(\ell，|m|)$模式能够观察到信噪比在30\-45%范围内的信噪比范围为$\ell=|m|=2$数值相对论波形的最佳定向二元黑洞合并的信噪比.在确定了引力波探测中$(\ell，\，|m|)$模重要的参数空间后，我们构造了描述这些天体物理激励场景的波形信号，并用深度学习算法证明了这些拓扑复杂的信号可以在真实的LIGO噪声中被检测和表征。
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.