The use of Gaussian processes (GPs) as models for astronomical time series datasets has recently become almost ubiquitous, given their ease of use and flexibility. GPs excel in particular at marginalization over the stellar signal in cases where the variability due to starspots rotating in and out of view is treated as a nuisance, such as in exoplanet transit modeling. However, these effective models are less useful in cases where the starspot signal is of primary interest since it is not obvious how the parameters of the GP model are related to the physical properties of interest, such as the size, contrast, and latitudinal distribution of the spots. Instead, it is common practice to explicitly model the effect of individual starspots on the light curve and attempt to infer their properties via optimization or posterior inference. Unfortunately, this process is degenerate, ill-posed, and often computationally intractable when applied to stars with more than a few spots and/or to ensembles of many light curves. In this paper, we derive a closed-form expression for the mean and covariance of a Gaussian process model that describes the light curve of a rotating, evolving stellar surface conditioned on a given distribution of starspot sizes, contrasts, and latitudes. We demonstrate that this model is correctly calibrated, allowing one to robustly infer physical parameters of interest from one or more stellar light curves, including the typical radii and the mean and variance of the latitude distribution of starspots. Our GP has far-ranging implications for understanding the variability and magnetic activity of stars from both light curves and radial velocity (RV) measurements, as well as for robustly modeling correlated noise in both transiting and RV exoplanet searches. Our implementation is efficient, user-friendly, and open source, available as the Python package starry-process.
Using Astrometry to Break Degeneracies in Stellar Surface Mapping
Michigan Institute for Data Science, University of Michigan（密歇根大学数据科学研究所）；Department of Astronomy, University of Michigan（密歇根大学天文学系）；Department of Electrical Engineering and Computer Science, University of Michigan（密歇根大学电气与计算机工程系）
Mapping Stellar Heterogeneities with the Nautilus Space Observatory
Michigan State University（密歇根州立大学）；Space Telescope Science Institute（太空望远镜科学研究所）；Massachusetts Institute of Technology（麻省理工学院）；Max Planck Institute for Solar System Research（马克斯·普朗克太阳系统研究所）；The University of Arizona（亚利桑那大学）；NASA ICAR/NExSS（美国国家航空航天局 ICAR/NExSS）
A new treatment of telluric and stellar features for medium resolution spectroscopy and molecular mapping. Application to the abundance determination on Beta Pic b
Mapping the Distant and Metal-Poor Milky Way with SDSS-V
Department of Astronomy & Astrophysics, University of Chicago（芝加哥大学天文与天体物理系）；Kavli Institute for Cosmological Physics, University of Chicago（芝加哥大学卡弗里宇宙物理学研究所）；NSF-Simons AI Institute for the Sky (SkAI)（NSF-Simons 天空人工智能研究所 (SkAI)）；Max-Planck-Institut für Astronomie（马克斯·普朗克天文学研究所）；Department of Astronomy, Columbia University（哥伦比亚大学天文学系）；Department of Astronomy, Boston University（波士顿大学天文学系）；Instituto de Astrofísica, Departamento de Física y Astronomía, Facultad de Ciencias Exactas, Universidad Andres Bello（安第斯贝尔托大学精确科学学院物理与天文学系天体物理研究所）；LIRA
Stars with Photometrically Young Gaia Luminosities Around the Solar System (SPYGLASS) I: Mapping Young Stellar Structures and their Star Formation Histories
The Open Cluster Chemical Abundances and Mapping Survey: IX. Measuring the Effects of Stellar Diffusion in the Open Clusters NGC 752 and Ruprecht 147 using APOGEE
Universidade Federal de Sergipe（塞尔希培联邦大学）；Texas Christian University（德克萨斯基督教大学）；Maunakea Spectroscopic Explorer, Canada-France-Hawaii-Telescope（莫纳克天文光谱仪，加拿大法国夏威夷望远镜）；Steward Observatory, University of Arizona（亚利桑那大学斯图尔德天文台）；Observatório Nacional/MCTIC（国家天文台/巴西科技与创新部）；University of Florida（佛罗里达大学）
