论文标题
使用高斯过程回归网络对恒星活动进行建模
Modelling stellar activity with Gaussian process regression networks
论文作者
论文摘要
已知恒星光球活性限制了超极行星的检测和表征。特别是，对阳光恒星周围的类似地球行星的研究需要数据分析方法，该方法可以准确地模拟影响径向速度（RV）测量值的恒星活性现象。高斯工艺回归网络（GPRNS）为分析同时分析提供了一种原则的方法，将贝叶斯神经网络的结构特性与高斯过程的非参数灵活性相结合。使用竖琴-N太阳能光谱观测，包括三年的时间，我们证明了该框架能够共同对RV数据和传统的恒星活动指标进行联合建模。尽管我们仅考虑最简单的GPRN配置，但我们能够至少与先前发布的方法一样准确地描述太阳能RV数据的行为。我们确认RV和恒星活动时间序列之间的相关性在几天的分离时达到最大值，并在时间序列中找到了与太阳活动最小值有关的时间序列中非平稳行为的证据。
Stellar photospheric activity is known to limit the detection and characterisation of extra-solar planets. In particular, the study of Earth-like planets around Sun-like stars requires data analysis methods that can accurately model the stellar activity phenomena affecting radial velocity (RV) measurements. Gaussian Process Regression Networks (GPRNs) offer a principled approach to the analysis of simultaneous time-series, combining the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian Processes. Using HARPS-N solar spectroscopic observations encompassing three years, we demonstrate that this framework is capable of jointly modelling RV data and traditional stellar activity indicators. Although we consider only the simplest GPRN configuration, we are able to describe the behaviour of solar RV data at least as accurately as previously published methods. We confirm the correlation between the RV and stellar activity time series reaches a maximum at separations of a few days, and find evidence of non-stationary behaviour in the time series, associated with an approaching solar activity minimum.
