Total-variation methods for gravitational-wave denoising: Performance tests on Advanced LIGO data
Total-variation methods for gravitational-wave denoising: Performance tests on Advanced LIGO data
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DOI：
10.1103/physrevd.98.084013
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发表时间：
2018-06
影响因子：
5
通讯作者：
A. Torres-Forn'e;E. Cuoco;A. Marquina;J. Font;J. Ib'anez
中科院分区：
文献类型：
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作者：
A. Torres-Forn'e;E. Cuoco;A. Marquina;J. Font;J. Ib'anez
通过将核心坍缩超新星和双黑洞并合的数值相对性波形注入到先进LIGO首次观测数据中，我们评估了在真实噪声条件下引力波信号降噪的全变分方法。这项工作是我们以前的调查的延伸，其中只使用高斯噪声。由于结果的质量取决于模型的正则化参数，因此我们执行启发式搜索以产生最佳结果的值。我们讨论了基于最优值、均值或多个值选择该参数的各种方法，并比较了这些选择的去噪结果。此外，我们还提出了一种机器学习的方法，通过自动搜索来获得该方法的拉格朗日乘子。我们的结果进一步证明了全变分方法在引力波天文学领域作为一种消除噪声的工具是有用的。
We assess total-variation methods to denoise gravitational-wave signals in real noise conditions by injecting numerical-relativity waveforms from core-collapse supernovae and binary black hole mergers in data from the first observing run of Advanced LIGO. This work is an extension of our previous investigation in which only Gaussian noise was used. Since the quality of the results depends on the regularization parameter of the model, we perform a heuristic search for the value that produces the best results. We discuss various approaches for the selection of this parameter, based on the optimal, mean, or multiple values, and compare the results of the denoising upon these choices. Moreover, we also present a machine-learning-informed approach to obtain the Lagrange multiplier of the method through an automatic search. Our results provide further evidence that total-variation methods can be useful in the field of gravitational-wave astronomy as a tool to remove noise.