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.

翻译：空间引力波探测器能够观测到当前地面探测器几乎无法探测的源信号。因此，成熟的信号检测方法——匹配滤波——将需要复杂的模板库，导致实际计算成本过高。本文提出了一种面向所有空间引力波源的高精度信号检测与提取方法。作为概念验证，我们证明了一种基于科学驱动且统一的、多阶段自注意力深度神经网络，可识别淹没在高斯噪声中的合成信号。在信噪比为50、虚警率为1%的条件下，我们的方法对多种源信号的检测率超过99%，同时与目标信号的相似度至少达到95%。我们进一步展示了该方法在若干扩展场景下的可解释性和强泛化能力。