利用深度学习实现未来引力波天文台的引力波混合信号分离
Gravitational Wave Mixture Separation for Future Gravitational Wave Observatories Utilizing Deep Learning
AI总结：
针对未来引力波天文台中重叠信号易被误判的问题，本文借鉴语音分离思想，利用深度学习模型有效分离重叠引力波信号，以消除参数估计偏差。
摘要：
未来的引力波天文台，如爱因斯坦望远镜（ET），预计将探测到引力波信号，其中一些信号很可能相互重叠。这种重叠可能导致将多个信号误识别为单一引力波事件，从而可能使混合引力波的估计参数产生偏差。在本文中，我们借鉴语音分离的概念，将其应用于重叠引力波的信号分离以解决这一问题。我们证明，深度学习模型能够有效分离重叠的引力波信号。所提出的方法可能有助于消除此类信号在参数估计中的偏差。
英文摘要：
Future GW observatories, such as the Einstein Telescope (ET), are expected to detect gravitational wave signals, some of which are likely to overlap with each other. This overlap may lead to misidentification as a single GW event, potentially biasing the estimated parameters of mixture GWs. In this paper, we adapt the concept of speech separation to address this issue by applying it to signal separation of overlapping GWs. We show that deep learning models can effectively separate overlapping GW signals. The proposed method may aid in eliminating biases in parameter estimation for such signals.
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