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arXiv 2105.03073gr-qcastro-ph.IM
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使用深度学习从探测器数据中提取二元黑洞引力波信号

Extraction of Binary Black Hole Gravitational Wave Signals from Detector Data Using Deep Learning

Chayan Chatterjee, Linqing Wen, Foivos Diakogiannis, Kevin Vinsen

摘要:

准确提取探测到的引力波信号波形对于验证探测和探测产生引力波的源背后的天体物理是必不可少的。然而,这在现实场景中可能是困难的,其中由现有GW检测器检测到的信号可能被非平稳和非高斯噪声污染。虽然现有的波形提取方法的性能是最佳的,他们不够快,在线应用,这是重要的多信使天文学。在本文中,我们证明了由卷积神经网络和双向长短期记忆组件组成的深度学习架构可用于在几毫秒内从现实噪声中提取二进制黑洞(BBH)GW波形。我们已经系统地测试了我们的网络注入GW信号,组件质量均匀分布在10至80太阳质量的范围内,高斯噪声和LIGO探测器噪声。我们发现,我们的模型可以提取GW波形重叠超过0.95与纯数值相对论模板的信号与信噪比(SNR)大于6,也是强大的干扰毛刺。然后,我们将我们的模型应用于从第一次(O1)和第二次(O2)观察运行中检测到的所有十个BBH事件,获得所有十个提取的BBH波形与相应的纯模板的大于0.97的重叠。我们讨论了我们的结果的含义和未来的应用GW本地化和质量估计。

英文摘要:

Accurate extractions of the detected gravitational wave (GW) signal waveforms are essential to validate a detection and to probe the astrophysics behind the sources producing the GWs. This however could be difficult in realistic scenarios where the signals detected by existing GW detectors could be contaminated with non-stationary and non-Gaussian noise. While the performance of existing waveform extraction methods are optimal, they are not fast enough for online application, which is important for multi-messenger astronomy. In this paper, we demonstrate that a deep learning architecture consisting of Convolutional Neural Network and bidirectional Long Short-Term Memory components can be used to extract binary black hole (BBH) GW waveforms from realistic noise in a few milli-seconds. We have tested our network systematically on injected GW signals, with component masses uniformly distributed in the range of 10 to 80 solar masses, on Gaussian noise and LIGO detector noise. We find that our model can extract GW waveforms with overlaps of more than 0.95 with pure Numerical Relativity templates for signals with signal-to-noise ratio (SNR) greater than six, and is also robust against interfering glitches. We then apply our model to all ten detected BBH events from the first (O1) and second (O2) observation runs, obtaining greater than 0.97 overlaps for all ten extracted BBH waveforms with the corresponding pure templates. We discuss the implication of our result and its future applications to GW localization and mass estimation.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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