Precision radial velocity (RV) measurements continue to be a key tool to detect and characterise extrasolar planets. While instrumental precision keeps improving, stellar activity remains a barrier to obtain reliable measurements below 1-2 m/s accuracy. Using simulations and real data, we investigate the capabilities of a Deep Neural Network approach to produce activity free Doppler measurements of stars. As case studies we use observations of two known stars (Eps Eridani and AUMicroscopii), both with clear signals of activity induced RV variability. Synthetic data using the starsim code are generated for the observables (inputs) and the resulting RV signal (labels), and used to train a Deep Neural Network algorithm. We identify an architecture consisting of convolutional and fully connected layers that is adequate to the task. The indices investigated are mean line-profile parameters (width, bisector, contrast) and multi-band photometry. We demonstrate that the RV-independent approach can drastically reduce spurious Doppler variability from known physical effects such as spots, rotation and convective blueshift. We identify the combinations of activity indices with most predictive power. When applied to real observations, we observe a good match of the correction with the observed variability, but we also find that the noise reduction is not as good as in the simulations, probably due to the lack of detail in the simulated physics. We demonstrate that a model-driven machine learning approach is sufficient to clean Doppler signals from activity induced variability for well known physical effects. There are dozens of known activity related observables whose inversion power remains unexplored indicating that the use of additional indicators, more complete models, and more observations with optimised sampling strategies can lead to significant improvements in our detrending capabilities.
翻译:精确辐射速度( RV) 测量继续是探测和描述异常行星的关键工具。 虽然工具精确度不断提高, 恒星活动仍然是获得1-2 m/s准确度以下的可靠测量的障碍。 我们使用模拟和真实数据, 调查深神经网络方法的能力, 以生成无活动的多普勒星测量。 作为案例研究, 我们使用两种已知恒星( Eps Eridani 和 Aumicrocopii) 的观测, 两者都有活动清晰信号, 导致RV变异。 使用恒星代码生成的合成数据, 用于观测( 投入) 和由此产生的 RV 信号( 标签), 并用于训练深神经网络的算法 。 我们确定一个结构结构, 由革命性和完全连接的层组成, 足以完成任务。 所调查的指数是线谱参数( 边、 双区、 对比) 和多波段光度测量。 我们证明, RV 依赖方法可以大幅降低已知的多普勒变变量变异性, 从已知的物理效果( 如点、 旋转和相变换动) 等的物理信号信号信号信号信号信号信号信号信号信号, 我们也可以算算算算算算出一个清晰的精细的精细的模型, 。