论文标题
深层神经网络的空间重力波信号检测和提取
Space-based gravitational wave signal detection and extraction with deep neural network
论文作者
论文摘要
空间引力波（GW）检测器将能够观察到来自当前基于地面检测的来源的信号。因此，建立的信号检测方法（匹配的过滤）将需要一个复杂的模板库，从而导致计算成本在实践中太昂贵。在这里，我们为所有基于空间的GW来源开发了高准确的GW信号检测和提取方法。作为概念的证明，我们表明，基于科学驱动的多阶段自我注意的深神经网络可以识别浸入高斯噪声中的合成信号。我们的方法在识别来自各种来源的信号时表现出超过99％的检测率，信噪比为50，误报率为1％。与目标信号相比，获得至少95％的相似性。我们进一步证明了几种扩展场景的解释性和强大的概括行为。
Space-based gravitational wave (GW) detectors will be able to observe signals from sources that are otherwise nearly impossible from current ground-based detection. Consequently, the well established signal detection method, matched filtering, will require a complex template bank, leading to a computational cost that is too expensive in practice. Here, we develop a high-accuracy GW signal detection and extraction method for all space-based GW sources. As a proof of concept, we show that a science-driven and uniform multi-stage self-attention-based deep neural network can identify synthetic signals that are submerged in Gaussian noise. Our method exhibits a detection rate exceeding 99% in identifying signals from various sources, with the signal-to-noise ratio at 50, at a false alarm rate of 1%. while obtaining at least 95% similarity compared with target signals. We further demonstrate the interpretability and strong generalization behavior for several extended scenarios.
