Stellar Occultations in the Era of Data Mining and Modern Regression Models: Using Gaussian Processes to Analyze Light Curves and Improve Predictions

IF 3.8 Q2 ASTRONOMY & ASTROPHYSICS
Bastian Knieling, Karsten Schindler, Amanda A. Sickafoose, Michael J. Person, Stephen E. Levine and Alfred Krabbe
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Abstract

Gaussian process (GP) regression is a nonparametric Bayesian approach that has been used successfully in various astronomical domains, especially in time-domain astronomy. The most common applications are the smoothing of data for interpolation and the detection of periodicities. The ability to create unbiased data-driven models without a predefined physical model can be a major advantage over conventional regression methods. Prior knowledge can be included by setting boundary conditions or constraining hyperparameter values, while unknown hyperparameters are optimized during the conditioning of the model. We have adapted and transformed previous approaches of GP regression and introduce three new applications for this regression method, especially in the context of stellar occultations: the modeling of occultation light curves, the correction of public JPL ephemerides of minor planets based on publicly available image data of the Zwicky Transient Facility, and the detection of natural satellites. We used data from observations of stellar occultations to validate the models and achieved promising results in all cases, and thus we confirmed the flexibility of GP regression models. Considering various existing use cases in addition to our novel applications, GP regression can be used to model diverse data sets addressing a wide range of problems. The accuracy of the model depends on the input data and on the set boundary conditions. Generally, high-quality data allow the usage of loose boundary conditions, while low-quality data require more restrictive boundary conditions to avoid overfitting.
数据挖掘和现代回归模型时代的恒星掩星:利用高斯过程分析光变曲线并改进预测
高斯过程(GP)回归是一种非参数贝叶斯方法,已成功应用于各种天文领域,尤其是时域天文学。最常见的应用是平滑数据以进行插值和检测周期性。与传统回归方法相比,该方法的一大优势是能够在没有预定义物理模型的情况下创建无偏的数据驱动模型。通过设置边界条件或限制超参数值,可以将先验知识纳入其中,而未知超参数则在模型调节过程中进行优化。我们对以前的 GP 回归方法进行了调整和改造,并为这种回归方法引入了三个新的应用领域,尤其是在恒星掩星方面:掩星光曲线建模、根据公开的兹威基瞬变设施图像数据修正 JPL 小行星星历表,以及探测天然卫星。我们使用恒星掩星观测数据来验证模型,在所有情况下都取得了令人满意的结果,从而证实了 GP 回归模型的灵活性。考虑到现有的各种应用案例以及我们的新应用,GP 回归可用于对各种数据集进行建模,以解决广泛的问题。模型的准确性取决于输入数据和设定的边界条件。一般来说,高质量数据允许使用宽松的边界条件,而低质量数据则需要更严格的边界条件以避免过拟合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
The Planetary Science Journal
The Planetary Science Journal Earth and Planetary Sciences-Geophysics
CiteScore
5.20
自引率
0.00%
发文量
249
审稿时长
15 weeks
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