In the future, the third-generation gravitational wave (GW) detectors, exemplified by the Einstein Telescope (ET), will be operational. Many binary neutron star (BNS) coalescence events are expected to be observed. There are many machine learning works on binary black hole data analysis published, but works on BNSs are fewer. This paper explores the extraction of BNS waveforms from ET. Drawing inspiration from summed parallel infinite impulse response’s matched filtering approach, we introduce a novel framework based on leveraging deep learning for BNS waveform extraction. By integrating denoised outputs of time-delayed strain, we can reconstruct the embedded BNS waveform. We have established three distinct BNS GW denoising models, each tailored to address the early inspiral, later inspiral, and merger phases of BNS GWs, respectively. To further regulate the waveform shape, we propose an amplitude regularity model which takes the denoised output as input and output the regulated waveform. The experiments conducted on test data demonstrate the efficacy of the denoising models and the amplitude regularity model, as well as the overall waveform construction method. To the best of our knowledge, this marks the first instance of deep learning being utilized for the task of BNS waveform extraction. We also introduce a method for detecting BNS GWs that relies on waveform extraction. Our detection results demonstrate that this proposed method can accurately identify all GW signals with a SNR exceeding 20, without any false triggers. Furthermore, we believe that this GW extraction method also holds great promise for various other applications in BNS GW data processing, including localization and parameter estimation.

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• General relativity
• Gravitational wave detection
• Gravitational waves