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 deep neural network can identify synthetic signals that are submerged in Gaussian noise. Our method has more than 99% accuracy for signal detection of various sources 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:天基引力波探测器将能够观测从目前地面探测几乎不可能得到的源发出的信号。 因此,完善的信号探测方法、匹配的过滤方法将需要复杂的模板库,导致计算成本过高,在实践中成本太高。在这里,我们为所有天基GW来源开发了高精确度的GW信号探测和提取方法。作为概念的证明,我们证明科学驱动和统一的多阶段多级深层神经网络可以识别被高斯噪音淹没的合成信号。我们的方法对各种源的信号探测具有99%的准确性,而与目标信号相比则至少达到95%的相似性。我们进一步展示了若干扩展情景的可解释性和强烈的概括行为。