Detection and parameter estimation of gravitational waves from binary neutron-star mergers in real LIGO data using deep learning
Detection and parameter estimation of gravitational waves from binary neutron-star mergers in real LIGO data using deep learning
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DOI：
10.1016/j.physletb.2021.136161
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发表时间：
2020-12
影响因子：
4.4
通讯作者：
P. Krastev;K. Gill;V. Villar;E. Berger
中科院分区：
文献类型：
--
作者：
P. Krastev;K. Gill;V. Villar;E. Berger
来自紧凑型二元合并的引力波的实时检测和参数估计的关键挑战之一是传统匹配滤波和贝叶斯推理方法的计算成本。特别是，将这些方法应用于引力波探测器可用的完整信号参数空间和/或实时参数估计在计算上是令人望而却步的。另一方面，快速检测和推断对于及时跟踪伴随重要瞬变的电磁和天体粒子对应物（例如双中子星和黑洞中子星合并）至关重要。训练深度神经网络来识别特定信号并学习引力波信号及其参数之间映射的计算有效表示，可以快速可靠地完成检测和推理，并且具有高灵敏度和准确性。在这项工作中，我们应用深度学习方法来快速识别和表征 realLIGO 数据中双中子星合并产生的瞬态引力波信号。我们首次证明人工神经网络可以快速检测和表征真实 LIGO 数据中的双中子星引力波信号，并将其与来自合并黑洞双星的噪声和信号区分开来。我们通过证明我们的深度学习框架正确分类了引力波瞬态目录 GWTC-1 [Abbott 等人，2017] 中的所有引力波事件来说明这一关键结果。 （2019）[4]]。这些结果强调了在机器学习方法中使用真实引力波探测器数据的重要性，并代表着实现引力波实时探测和推断的一步。
One of the key challenges of real-time detection and parameter estimation of gravitational waves from compact binary mergers is the computational cost of conventional matched-filtering and Bayesian inference approaches. In particular, the application of these methods to the full signal parameter space available to the gravitational-wave detectors, and/or real-time parameter estimation is computationally prohibitive. On the other hand, rapid detection and inference are critical for prompt follow-up of the electromagnetic and astro-particle counterparts accompanying important transients, such as binary neutron-star and black-hole neutron-star mergers. Training deep neural networks to identify specific signals and learn a computationally efficient representation of the mapping between gravitational-wave signals and their parameters allows both detection and inference to be done quickly and reliably, with high sensitivity and accuracy. In this work we apply a deep-learning approach to rapidly identify and characterize transient gravitational-wave signals from binary neutron-star mergers inrealLIGO data. We show for the first time that artificial neural networks can promptly detect and characterize binary neutron star gravitational-wave signals inrealLIGO data, and distinguish them from noise and signals from coalescing black-hole binaries. We illustrate this key result by demonstrating that our deep-learning framework classifies correctly all gravitational-wave events from the Gravitational-Wave Transient Catalog, GWTC-1 [Abbott et al. (2019) [4]]. These results emphasize the importance of using realistic gravitational-wave detector data in machine learning approaches, and represent a step towards achieving real-time detection and inference of gravitational waves.