The detection of terrestrial planets by radial velocity and photometry is hindered by the presence of stellar signals. Those are often modeled as stationary Gaussian processes, whose kernels are based on qualitative considerations, which do not fully leverage the existing physical understanding of stars. Our aim is to build a formalism which allows to transfer the knowledge of stellar activity into practical data analysis methods. In particular, we aim at obtaining kernels with physical parameters. This has two purposes: better modelling signals of stellar origin to find smaller exoplanets, and extracting information about the star from the statistical properties of the data. We consider several observational channels such as photometry, radial velocity, activity indicators, and build a model called FENRIR to represent their stochastic variations due to stellar surface inhomogeneities. We compute analytically the covariance of this multi-channel stochastic process, and implement it in the S+LEAF framework to reduce the cost of likelihood evaluations from $O(N^3)$ to $O(N)$. We also compute analytically higher order cumulants of our FENRIR model, which quantify its non-Gaussianity. We obtain a fast Gaussian process framework with physical parameters, which we apply to the HARPS-N and SORCE observations of the Sun, and constrain a solar inclination compatible with the viewing geometry. We then discuss the application of our formalism to granulation. We exhibit non-Gaussianity in solar HARPS radial velocities, and argue that information is lost when stellar activity signals are assumed to be Gaussian. We finally discuss the origin of phase shifts between RVs and indicators, and how to build relevant activity indicators. We provide an open-source implementation of the FENRIR Gaussian process model with a Python interface.
翻译:探测径向速度和光变的地球类行星受到天体信号的干扰。这些信号经常被建模为基于定性考虑的平稳高斯过程,这样做并不能充分利用已经存在的有关星体的物理理解。我们的目标是建立一个形式化的框架,使得能够将星体活动的知识转换为实际的数据分析方法。特别是,我们的目标是得到具有物理参数的核函数。这样做的目的有两个:一是更好地建模由于星体表面不均匀而引起的星体信号以寻找更小的外星行星,二是通过数据的统计属性从星体信号中提取信息。我们考虑多种观测通道,如光变、径向速度、活动指标等,构建一个称为FENRIR的模型来表示它们由于星体表面不均匀而产生的随机变化。我们计算了这个多通道随机过程的协方差,并将其实现在S+LEAF框架中,将似然函数的计算成本从$O(N^3)$减少到$O(N)$。我们还计算了我们的FENRIR模型的高阶累积,用以衡量它的非高斯性。我们得到了一个具有物理参数的快速高斯过程框架,将其应用于HARPS-N和SORCE对太阳的观测,并对太阳倾角进行约束。然后我们讨论了我们的形式化框架在颗粒运动中的应用。我们在太阳HARPS径向速度中发现了非高斯性,并认为当星体活动信号被假定为高斯信号时信息将丢失。最后,我们讨论了径向速度和指标之间的相位偏移的起源以及如何构建相关的活动指标。我们为FENRIR高斯过程模型提供了Python接口的开源实现。