Kepler Data Analysis: Non-Gaussian Noise and Fourier Gaussian Process Analysis of Stellar Variability
Kepler Data Analysis: Non-Gaussian Noise and Fourier Gaussian Process Analysis of Stellar Variability
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DOI：
10.3847/1538-3881/ab8460
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发表时间：
2020-05-01
影响因子：
5.3
通讯作者：
Seljak, Uros
中科院分区：
文献类型：
--
作者：
Robnik, Jakob;Seljak, Uros
我们开发了一个统计分析模型的开普勒恒星流量数据中存在的行星凌日，非高斯噪声，恒星的变化。我们首先开发了存在异常值的开普勒噪声概率分布模型，这使得噪声概率分布非高斯。我们开发了一个信号似然分析的基础上，这种概率分布，在其中，我们建模的信号作为一个总和的星星的变化和行星凌日。我们认为，这些组件需要一起建模，如果要从数据中提取最佳信号。对于恒星变率模型，我们开发了一个最佳的高斯过程分析使用基于傅立叶的维纳滤波器的方法，其中的功率谱是非参数的，从数据中学习。我们开发高维优化的目标函数，我们共同优化所有的模型参数，包括数千个星星变异模式，和行星过境参数。我们将该方法应用于开普勒-90数据，并表明它提供了一个更好的匹配恒星的变化比现有的方法，并鲁棒地处理噪声离群值。因此，行星半径具有比现有方法（包括样条曲线和celerite）给出的更高的值。
We develop a statistical analysis model of Kepler stellar flux data in the presence of planet transits, non-Gaussian noise, and stellar variability. We first develop a model for the Kepler noise probability distribution in the presence of outliers, which make the noise probability distribution non-Gaussian. We develop a signal likelihood analysis based on this probability distribution, in which we model the signal as a sum of the star variability and planetary transits. We argue that these components need to be modeled together if optimal signal is to be extracted from the data. For the stellar variability model we develop an optimal Gaussian process analysis using a Fourier-based Wiener filter approach, where the power spectrum is non-parametric and learned from the data. We develop high dimensional optimization of the objective function, where we jointly optimize all the model parameters, including thousands of star variability modes, and planet transit parameters. We apply the method to Kepler-90 data and show that it gives a better match to the stellar variability than the existing methods, and robustly handles noise outliers. As a consequence, the planet radii have a higher value than what the existing methods give, including splines and celerite.