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arXiv 2005.06534astro-ph.IMgr-qcphysics.data-anphysics.ins-det
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基于深度学习的引力波数据降噪

Noise Reduction in Gravitational-wave Data via Deep Learning

Rich Ormiston, Tri Nguyen, Michael Coughlin, Rana X. Adhikari, Erik Katsavounidis

摘要:

随着引力波天文学的出现,人们期望有技术来扩展引力波探测器的范围。除了已经探测到的恒星质量黑洞和中子星合并外,还有更多的黑洞在噪音的表面之下,如果噪音足够小,就可以探测到。我们的方法(DeepClean)将机器学习算法应用于引力波探测器数据和来自监测仪器的现场传感器的数据,以减少由于仪器伪影和环境污染而导致的时间序列中的噪声。这个框架是通用的,足以减去线性,非线性和非静态耦合机制。它还可以提供了解目前不被理解为限制检测器灵敏度的机制的手柄。降噪技术的鲁棒性在其有效地去除噪声的能力,没有意外的影响引力波信号也解决了通过软件信号注入和参数估计的恢复信号。结果表明,$\sim 21.6\%$可提高注入信号的最佳信噪比,恢复的参数与注入集一致。我们提出了该算法的性能线性和非线性噪声源,并讨论了它的天体物理搜索的引力波探测器的影响。

英文摘要:

With the advent of gravitational wave astronomy, techniques to extend the reach of gravitational wave detectors are desired. In addition to the stellar-mass black hole and neutron star mergers already detected, many more are below the surface of the noise, available for detection if the noise is reduced enough. Our method (DeepClean) applies machine learning algorithms to gravitational wave detector data and data from on-site sensors monitoring the instrument to reduce the noise in the time-series due to instrumental artifacts and environmental contamination. This framework is generic enough to subtract linear, non-linear, and non-stationary coupling mechanisms. It may also provide handles in learning about the mechanisms which are not currently understood to be limiting detector sensitivities. The robustness of the noise reduction technique in its ability to efficiently remove noise with no unintended effects on gravitational-wave signals is also addressed through software signal injection and parameter estimation of the recovered signal. It is shown that the optimal SNR ratio of the injected signal is enhanced by $\sim 21.6\%$ and the recovered parameters are consistent with the injected set. We present the performance of this algorithm on linear and non-linear noise sources and discuss its impact on astrophysical searches by gravitational wave detectors.

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