发表机构
School of Earth and Space Exploration, Arizona State University; Department of Astronomy & Astrophysics, University of Chicago; Instituto de Astrofísica de Andalucía (IAA-CSIC)（亚利桑那州立大学地球与空间探索学院; 芝加哥大学天文学与天体物理学系; 安达卢西亚天体物理研究所（CSIC））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过三个案例展示留一交叉验证（LOO-CV）在径向速度分析中识别数据影响、模型选择及噪声处理方面的作用，增强可解释性，并提供了实用指南与在线教程。
AI 中文摘要
径向速度（RV）观测正达到越来越高的精度，使得针对低振幅信号的探测成为可能。评估在我们能力极限下获得的结果，需要理解数据、模型与推断之间的关系。留一交叉验证（LOO-CV）已成为天文数据分析中用于可解释模型批评的日益流行的工具。在本工作中，我们通过三个案例研究展示了LOO-CV如何为系外行星系统的RV分析提供洞察。首先，我们研究了从CARMENES径向速度数据推断出的HD 119130 b异常高质量，该质量在后续使用其他仪器的观测后被显著下调。我们表明，这一高质量主要由两个单独的测量值驱动，移除这些测量值显著降低了数据集之间的张力。其次，我们将LOO-CV用作GJ 4276系统的模型选择工具。在此，LOO-CV倾向于偏心单行星模型而非双行星模型，并确定了后续观测最能区分这两种模型的轨道相位。最后，我们将LOO-CV扩展到具有相关噪声的模型，将其应用于GJ 357系统，该系统的径向速度使用高斯过程建模。我们发现，对于贝叶斯证据适度支持的一个第四行星信号，没有预测性支持，这加强了对文献中先前报告的三行星架构的解释。我们提供了在RV分析中实施LOO-CV的实用指南，以及一个在线教程。随着未来RV巡天推动行星探测和表征的极限，此类方法将变得更加重要。
英文摘要
Radial velocity (RV) observations are reaching increasingly high precision, enabling the targeting of low-amplitude signals. Assessing results obtained at the limits of our capabilities requires understanding the relationship between data, models and inference. Leave-one-out cross-validation (LOO-CV) has become an increasingly popular tool for interpretable model criticism in astronomical data analysis. In this work, we demonstrate how LOO-CV can provide insight into the RV analysis of exoplanetary systems through three case studies. We first investigate the anomalously high mass of HD 119130 b inferred from CARMENES RVs, later revised significantly downward after follow-up observations with other instruments. We show that this high mass was strongly driven by two individual measurements, whose removal significantly reduces the tension between data sets. Next, we use LOO-CV as a model selection tool for the GJ 4276 system. Here, LOO-CV prefers an eccentric single-planet model over a two-planet model, and identifies the orbital phases at which follow-up observations would best discriminate between them. Finally, we extend LOO-CV to models with correlated noise, applying it to the GJ 357 system, whose RVs are modeled with a Gaussian process. We find no predictive support for a fourth planetary signal that is moderately favored by the Bayesian evidence, strengthening the interpretation of a three-planet architecture previously reported in the literature. We provide practical guidance for implementing LOO-CV in RV analysis, together with an online tutorial. Such methods will become ever more important as future RV surveys push the limits of planet detection and characterization.
CommentsAccepted for publication in The Astronomical Journal. We include an online tutorial to apply LOO-CV to RV data sets. The tutorial is hosted on GitHub at https://github.com/mcnixon/rv_loo_cv_tutorial and preserved on Zenodo at https://zenodo.org/records/22779652
