Deep Learning with Quantized Neural Networks for Gravitational-wave Forecasting of Eccentric Compact Binary Coalescence
Deep Learning with Quantized Neural Networks for Gravitational-wave Forecasting of Eccentric Compact Binary Coalescence
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DOI：
10.3847/1538-4357/ac1121
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Wei Wei-Wei;E. Huerta;Mengshen Yun;N. Loutrel;Md Arif Shaikh;Prayush Kumar;R. Haas;V. Kindratenko
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文献类型：
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作者：
Wei Wei-Wei;E. Huerta;Mengshen Yun;N. Loutrel;Md Arif Shaikh;Prayush Kumar;R. Haas;V. Kindratenko
我们提出了深度学习预测在双中子星、中子星-黑洞系统和双黑洞合并中的第一个应用，这些系统的偏心率范围e ≤ 0.9。我们训练描述这些天体物理群体的神经网络，然后通过在引力波开放科学中心提供的先进激光干涉仪引力波天文台（LIGO）噪声中注入模拟偏心信号来测试它们的性能，以（1）量化神经网络在二元成分合并之前识别这些信号的速度;（2）量化神经网络在识别引力波后估计合并时间的准确程度;（3）估计这些事件从早期检测到合并的时间依赖性天空定位。我们的研究结果表明，深度学习可以识别合并前几秒（对于双黑洞）到几十秒（对于双中子星）的偏心信号。我们的神经网络的量化版本实现了模型大小的4倍减少，以及高达2.5倍的推理加速。这些新的算法可用于促进时间敏感的多信使天体物理观测致密双星在稠密的恒星环境。
We present the first application of deep learning forecasting for binary neutron stars, neutron star–black hole systems, and binary black hole mergers that span an eccentricity range e ≤ 0.9. We train neural networks that describe these astrophysical populations, and then test their performance by injecting simulated eccentric signals in advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) noise available at the Gravitational Wave Open Science Center to (1) quantify how fast neural networks identify these signals before the binary components merge; (2) quantify how accurately neural networks estimate the time to merger once gravitational waves are identified; and (3) estimate the time-dependent sky localization of these events from early detection to merger. Our findings show that deep learning can identify eccentric signals from a few seconds (for binary black holes) up to tens of seconds (for binary neutron stars) prior to merger. A quantized version of our neural networks achieves 4× reduction in model size, and up to 2.5× inference speedup. These novel algorithms may be used to facilitate time-sensitive multimessenger astrophysics observations of compact binaries in dense stellar environments.