标题： 使用深度学习重建LIGO中的二进制黑洞谐波 显示英文标题

标题： Reconstruction of binary black hole harmonics in LIGO using deep learning

摘要： 在LIGO和Virgo干涉仪中，来自合并致密双星的引力波信号主要通过基于模板的匹配滤波方法进行检测。虽然这种方法在平稳和高斯数据场景下是最优的，但其灵敏度经常受到探测器中非平稳噪声瞬态的影响。此外，目前大多数搜索没有考虑黑洞自旋进动和高阶波形谐波的影响，仅专注于主导的一阶四极模式。这种限制影响了我们对有趣天体物理源的搜索，例如中等质量黑洞双星和分层合并。在这里，我们首次展示了深度学习可以用于具有高阶模式的进动双黑洞信号的准确波形重建。这种方法也可以适应为快速触发生成算法，以增强在线搜索。我们的模型在第三轮观测的真实LIGO噪声中的模拟注入上进行了测试，与注入信号具有高度重叠。这种准确性在本研究中选择的广泛范围的黑洞质量和自旋配置中是一致的。当应用于真实的引力波事件时，我们的重建结果与相干波突发（无模型）和LALInference（有模型）分析得到的结果之间的重叠在0.85到0.98之间。这些结果表明，深度学习是分析多样化致密双星目录信号的强大工具。 显示英文摘要

摘要： Gravitational wave signals from coalescing compact binaries in the LIGO and Virgo interferometers are primarily detected by the template based matched filtering method. While this method is optimal for stationary and Gaussian data scenarios, its sensitivity is often affected by non stationary noise transients in the detectors. Moreover, most of the current searches do not account for the effects of precession of black hole spins and higher order waveform harmonics, focusing solely on the leading order quadrupolar modes. This limitation impacts our search for interesting astrophysical sources, such as intermediate mass black hole binaries and hierarchical mergers. Here we show for the first time that deep learning can be used for accurate waveform reconstruction of precessing binary black hole signals with higher order modes. This approach can also be adapted into a rapid trigger generation algorithm to enhance online searches. Our model, tested on simulated injections in real LIGO noise from the third observing run achieved high-degree of overlap with injected signals. This accuracy was consistent across a wide range of black hole masses and spin configurations chosen for this study. When applied to real gravitational wave events, our reconstructions achieved between 0.85 and 0.98 overlaps with those obtained by Coherent WaveBurst (unmodeled) and LALInference (modeled) analyses. These results suggest that deep learning is a potent tool for analyzing signals from a diverse catalog of compact binaries.