Gaussian Process Regression for Astronomical Time Series
Gaussian Process Regression for Astronomical Time Series
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DOI：
10.1146/annurev-astro-052920-103508
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发表时间：
2022-09
影响因子：
33.3
通讯作者：
S. Aigrain;D. Foreman-Mackey
中科院分区：
文献类型：
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作者：
S. Aigrain;D. Foreman-Mackey
在过去的二十年里，天文学时域数据集的可用性、规模和精度都有了很大的发展。由于其独特的组合的灵活性，数学简单性和相对的鲁棒性，高斯过程（GP）最近出现作为选择的解决方案来模拟随机信号在这样的数据集。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，并考虑到GP回归中涉及的关键建模选择，给出了实用的建议。然后，我们回顾应用GPS到时域数据集的天体物理文献，到目前为止，从系外行星到活动星系核，展示了该方法的功能和灵活性。我们提供了使用模拟数据的工作示例，并提供了源代码的链接;讨论了计算成本和可扩展性的问题;并给出了当前开源GP软件包生态系统的快照。总之：RIGGP回归是一个概念简单，但统计原则和强大的工具，用于分析天文时间序列。它已经被广泛用于一些子领域，如系外行星，并在许多其他领域获得牵引力，如光学瞬变。在进一步的算法和概念进步的推动下，我们预计GPS将继续成为未来许多年强大和可解释的时域天文学的重要工具。天文学和天体物理学年度评论第61卷的预计最终在线出版日期为2023年8月。请访问http://www.annualreviews.org/page/journal/pubdates了解修订后的估计数。
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. Expected final online publication date for the Annual Review of Astronomy and Astrophysics, Volume 61 is August 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.