论文标题
用量化的神经网络进行深度学习，以预测偏心紧凑型二元合并的重力波
Deep Learning with Quantized Neural Networks for Gravitational Wave Forecasting of Eccentric Compact Binary Coalescence
论文作者
论文摘要
我们介绍了对二进制中子星，中子星 - 黑洞系统和二进制黑洞合并的深度学习预测的第一个应用，这些二进制恒星和二进制黑洞合并跨越了偏心范围E <= 0.9。我们训练描述这些天体物理种群的神经网络，然后通过在\ texttt {引力波开放科学中心}的高级LIGO噪声中注入模拟的偏心信号来测试其性能：1）量化了在二进制组件合并之前识别这些信号的速度； 2）量化一旦确定重力波的时间，神经网络如何准确地估计合并的时间； 3）估计从早期检测到合并的这些事件的时间依赖性天空定位。我们的发现表明，深度学习可以在合并之前从几秒钟（用于二进制黑洞）（对于二进制中子恒星）中识别出偏心信号。我们的神经网络的量化版本可实现4倍的模型大小，最高2.5倍的推理速度。这些新型算法可用于促进密集的恒星环境中紧凑型二进制的时间敏感的多通信天体物理学观察。
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 LIGO noise available at the \texttt{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 4x reduction in model size, and up to 2.5x inference speed up. These novel algorithms may be used to facilitate time-sensitive multi-messenger astrophysics observations of compact binaries in dense stellar environments.
