基于人工神经网络的双星中子星引力波实时探测
Real-Time Detection of Gravitational Waves from Binary Neutron Stars using Artificial Neural Networks
摘要：
双黑洞合并产生的引力波的突破性发现，以及最近的中子星合并，开启了多信使天体物理学的新时代，并彻底改变了我们对宇宙的理解。人工神经网络等机器学习技术已经改变了许多技术领域，并且在引力波天体物理学中也被证明是成功的，用于探测和表征来自双黑洞的引力波信号。在这里，我们使用深度学习方法来快速识别来自双中子星合并的瞬态引力波信号，这些信号在代表典型引力波探测器数据的噪声时间序列中。具体来说，我们证明了在10万个数据样本上训练的深度卷积神经网络可以快速识别二进制中子星引力波信号，并将它们与噪声和合并黑洞二进制信号区分开来。这些结果表明，人工神经网络的潜力，实时检测引力波信号的双中子星合并，这是至关重要的，及时跟进和详细观察的电磁和天体粒子对应伴随这些重要的瞬态。
英文摘要：
The groundbreaking discoveries of gravitational waves from binary black-hole mergers and, most recently, coalescing neutron stars started a new era of Multi-Messenger Astrophysics and revolutionized our understanding of the Cosmos. Machine learning techniques such as artificial neural networks are already transforming many technological fields and have also proven successful in gravitational-wave astrophysics for detection and characterization of gravitational-wave signals from binary black holes. Here we use a deep-learning approach to rapidly identify transient gravitational-wave signals from binary neutron star mergers in noisy time series representative of typical gravitational-wave detector data. Specifically, we show that a deep convolution neural network trained on 100,000 data samples can rapidly identify binary neutron star gravitational-wave signals and distinguish them from noise and signals from merging black hole binaries. These results demonstrate the potential of artificial neural networks for real-time detection of gravitational-wave signals from binary neutron star mergers, which is critical for a prompt follow-up and detailed observation of the electromagnetic and astro-particle counterparts accompanying these important transients.
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