The past two decades have seen a major expansion in the availability, size, and precision of time-domain data sets in astronomy. Owing to their unique combination of flexibility, mathematical simplicity, and comparative robustness, Gaussian processes (GPs) have emerged recently as the solution of choice to model stochastic signals in such data sets. In this review, we provide a brief introduction to the emergence of GPs in astronomy, present the underlying mathematical theory, and give practical advice considering the key modeling choices involved in GP regression. We then review applications of GPs to time-domain data sets in the astrophysical literature so far, from exoplanets to active galactic nuclei, showcasing the power and flexibility of the method. We provide worked examples using simulated data, with links to the source code; discuss the problem of computational cost and scalability; and give a snapshot of the current ecosystem of open-source GP software packages. In summary: ▪GP regression is a conceptually simple but statistically principled and powerful tool for the analysis of astronomical time series.▪It is already widely used in some subfields, such as exoplanets, and gaining traction in many others, such as optical transients.▪Driven by further algorithmic and conceptual advances, we expect that GPs will continue to be an important tool for robust and interpretable time-domain astronomy for many years to come.

中文翻译：

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天文时间序列的高斯过程回归


在过去的二十年里，天文学中时域数据集的可用性、大小和精度有了重大扩展。由于高斯过程 （GP） 将灵活性、数学简单性和相对稳健性独特地结合在一起，最近成为在此类数据集中对随机信号进行建模的首选解决方案。在这篇综述中，我们简要介绍了天文学中 GP 的出现，介绍了基本的数学理论，并考虑了 GP 回归中涉及的关键建模选择给出了实用建议。然后，我们回顾了迄今为止天体物理学文献中 GP 在时域数据集中的应用，从系外行星到活跃的星系核，展示了该方法的强大功能和灵活性。我们提供使用模拟数据的工作示例，并带有源代码的链接;讨论计算成本和可扩展性问题;并简要介绍了当前开源 GP 软件包的生态系统。总而言之：▪GP 回归是一种概念简单但具有统计原则且强大的天文时间序列分析工具，▪它已经广泛用于一些子领域，例如系外行星，并在许多其他领域（例如光学瞬态）中获得关注。▪在算法和概念进一步进步的推动下，我们预计 GP 将在未来许多年继续成为稳健和可解释的时域天文学的重要工具。