Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave (GW) detector data. Examples include techniques for improving the sensitivity of Advanced Laser Interferometer GW Observatory and Advanced Virgo GW searches, methods for fast measurements of the astrophysical parameters of GW sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future GW detectors.

机器学习已经成为解决天体物理学问题的一种流行而强大的方法。我们回顾了机器学习技术在地面重力波（GW）探测器数据分析中的应用。示例包括用于提高高级激光干涉仪GW天文台和高级处女座GW搜索灵敏度的技术，用于快速测量GW源天体参数的方法以及用于减少和表征非天体探测器噪声的算法。这些应用程序演示了如何利用机器学习技术来增强当前和将来的GW检测器可能实现的科学。