starry_process：恒星光变曲线的可解释高斯过程
starry_process: Interpretable Gaussian processes for stellar light curves
摘要：
在这篇文章中，我们提出了starry_process代码，它实现了一个可解释的高斯过程（GP），用于模拟恒星光变曲线的变化。当暗星斑旋转进出视野时，从遥远恒星接收到的总通量将随着时间的推移而变化。因此，未解析的通量时间序列编码有关恒星表面特征的空间结构的信息。starry_process软件包允许人们容易地对由于星点引起的通量变化进行建模，无论人们是否有兴趣了解这些星点的特性，还是当它被视为讨厌的信号时，对恒星变化进行边缘化。这里实现的GP和典型的GP用于模拟恒星的变化之间的主要区别是我们的GP的明确依赖于恒星的物理性质，如其周期，倾角和临边暗化系数，并对属性的斑点，如它们的半径和纬度分布。此代码是Luger，Foreman-Mackey和Hedges（2021）中开发的可解释GP算法的Python实现。
英文摘要：
In this note we present the starry_process code, which implements an interpretable Gaussian process (GP) for modeling variability in stellar light curves. As dark starspots rotate in and out of view, the total flux received from a distant star will change over time. Unresolved flux time series therefore encode information about the spatial structure of features on the stellar surface. The starry_process software package allows one to easily model the flux variability due to starspots, whether one is interested in understanding the properties of these spots or marginalizing over the stellar variability when it is treated as a nuisance signal. The main difference between the GP implemented here and typical GPs used to model stellar variability is the explicit dependence of our GP on physical properties of the star, such as its period, inclination, and limb darkening coefficients, and on properties of the spots, such as their radius and latitude distributions. This code is the Python implementation of the interpretable GP algorithm developed in Luger, Foreman-Mackey, and Hedges (2021).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
