引力波探测的机器学习：预测和最佳消除室女座牛顿噪声的替代维纳滤波
Machine learning for gravitational-wave detection: surrogate Wiener filtering for the prediction and optimized cancellation of Newtonian noise at Virgo
摘要：
在引力波探测器中，消除来自地球重力波动的噪声（也称为牛顿噪声）是一个艰巨的挑战。重力波动是由与环境场有关的密度扰动引起的，例如，地震和声场，其特征在于复杂的空间相关性。这些领域的测量必然提供不完整的信息，问题是如何最佳地利用现有的信息设计的噪声消除系统。在本文中，我们提出了一种机器学习的方法来计算代理模型的维纳滤波器。该模型被用来计算地震检波器阵列的最佳配置为不同数量的传感器，这是缺失的神经网络抵消系统的设计重点。优化结果表明，有效的噪声消除可以实现，即使对于复杂的地震场与相对较少的地震计，只要他们部署在最佳配置。在这里提出的形式中，优化方法可以应用于位于地面的所有当前和未来的引力波探测器，并且还可以对未来的地下探测器进行微小修改。
英文摘要：
The cancellation of noise from terrestrial gravity fluctuations, also known as Newtonian noise (NN), in gravitational-wave detectors is a formidable challenge. Gravity fluctuations result from density perturbations associated with environmental fields, e.g., seismic and acoustic fields, which are characterized by complex spatial correlations. Measurements of these fields necessarily provide incomplete information, and the question is how to make optimal use of available information for the design of a noise-cancellation system. In this paper, we present a machine-learning approach to calculate a surrogate model of a Wiener filter. The model is used to calculate optimal configurations of seismometer arrays for a varying number of sensors, which is the missing keystone for the design of NN cancellation systems. The optimization results indicate that efficient noise cancellation can be achieved even for complex seismic fields with relatively few seismometers provided that they are deployed in optimal configurations. In the form presented here, the optimization method can be applied to all current and future gravitational-wave detectors located at the surface and with minor modifications also to future underground detectors.
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