Gravitational wave denoising of binary black hole mergers with deep learning
Gravitational wave denoising of binary black hole mergers with deep learning
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DOI：
10.1016/j.physletb.2019.135081
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发表时间：
2019-01
影响因子：
4.4
通讯作者：
Wei Wei-Wei;E. Huerta
中科院分区：
文献类型：
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作者：
Wei Wei-Wei;E. Huerta
引力波探测需要深入了解引力波信号的物理特性，以及从中提取引力波信号的噪声。理解噪声的统计特性是一项复杂的工作，特别是在现实的检测场景中。在本文中，我们证明了深度学习可以处理引力波数据的非高斯和非平稳性质，并展示了它在对GW150914、GW170104、GW170608和GW170814双黑洞合并产生的引力波信号进行高级LIGO噪声降噪中的应用。为了证明这种方法的准确性，我们计算了由我们的去噪算法产生的时间序列信号与期望描述这些引力波源的数值相对论模板之间的重叠，发现重叠O≥0.99。我们还表明，我们的深度学习算法能够从我们注入到实际先进的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意义。
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 O≳ 0.99. 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.