标题： 基于深度学习的双黑洞合并引力波去噪 显示英文标题

标题： Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning

摘要： 引力波探测需要深入了解引力波信号的物理属性以及从中提取出的噪声属性。理解噪声的统计特性是一项复杂的任务，尤其是在现实的探测场景下。本文展示了深度学习能够处理引力波数据的非高斯性和非平稳性，并展示了其在从高级LIGO噪声中去噪双黑洞合并产生的引力波信号（GW150914、GW170104、GW170608 和 GW170814）中的应用。为了展示该方法的准确性，我们计算了去噪算法生成的时间序列信号与预期描述这些引力波源的数值相对论模板之间的重叠度，发现重叠度为${\cal{O}}\gtrsim0.99$。我们还表明，我们的深度学习算法能够从注入真实高级LIGO数据中的数值相对论信号中去除噪声异常。我们讨论了这些结果对引力波信号表征的影响。 显示英文摘要

摘要： Gravitational wave detection requires an in-depth understanding of the physical properties of gravitational wave signals, and the noise from which they are extracted. Understanding the statistical properties of noise is a complex endeavor, particularly in realistic detection scenarios. In this article we demonstrate that deep learning can handle the non-Gaussian and non-stationary nature of gravitational wave data, and showcase its application to denoise the gravitational wave signals generated by the binary black hole mergers GW150914, GW170104, GW170608 and GW170814 from advanced LIGO noise. To exhibit the accuracy of this methodology, we compute the overlap between the time-series signals produced by our denoising algorithm, and the numerical relativity templates that are expected to describe these gravitational wave sources, finding overlaps ${\cal{O}}\gtrsim0.99$. We also show that our deep learning algorithm is capable of removing noise anomalies from numerical relativity signals that we inject in real advanced LIGO data. We discuss the implications of these results for the characterization of gravitational wave signals.