Space-based gravitational wave signal detection and extraction with deep neural network

• URL: http://arxiv.org/abs/2207.07414v3
• Date: Wed, 16 Aug 2023 02:17:48 GMT
• Title: Space-based gravitational wave signal detection and extraction with deep neural network
• Authors: Tianyu Zhao, Ruoxi Lyu, He Wang, Zhoujian Cao, Zhixiang Ren
• Abstract summary: Space-based gravitational wave (GW) detectors will be able to observe signals from sources that are otherwise nearly impossible from current ground-based detection. Here, we develop a high-accuracy GW signal detection and extraction method for all space-based GW sources.
• Score: 13.176946557548042
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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