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：精密径向速度测量仍是探测和表征系外行星的关键工具。虽然仪器精度持续提升，但恒星活动仍是获取低于1-2 m/s精度可靠测量的障碍。通过模拟与实测数据，我们研究了深度神经网络方法在产生无恒星活动干扰的多普勒测量结果方面的能力。以两颗已知恒星（波江座ε与显微镜座AU）为案例研究，这两颗恒星均存在明显的恒星活动诱导的径向速度变化信号。使用starsim代码生成可观测量的合成数据（输入）及对应的径向速度信号（标签），并用于训练深度神经网络算法。我们确定了由卷积层与全连接层组成的适合该任务的结构。研究的指标包括平均谱线参数（宽度、等分线、对比度）及多波段测光数据。研究表明，不依赖径向速度的方法能大幅消除已知物理效应（如黑子、自转及对流蓝移）引起的虚假多普勒变化。我们确定了具有最强预测能力的活动指标组合。当应用于实际观测时，虽然校正与观测变化趋势基本吻合，但发现噪声抑制效果不如模拟结果，这可能是由于模拟物理过程的精度不足。我们证明，对于已知的物理效应，基于模型驱动的机器学习方法足以消除恒星活动引起的多普勒信号污染。目前已知数十种与恒星活动相关的可观测量，但其反演潜力尚未被充分发掘，这表明引入更多指标、更完善的模型以及优化采样策略的观测，将显著提升我们的趋势检测能力。