AI 中文总结
该研究利用1171组ESPRESSO观测数据训练机器学习模型，从RM时间序列中重建参考径向速度趋势，虽能恢复已知周期性但未完全消除活动变异性，需更大数据集评估其缓解活动诱导径向速度信号的潜力。
AI 中文摘要
罗斯特-麦克劳克林（RM）效应会因凌日行星遮挡旋转恒星表面的不同区域，导致线轮廓畸变，从而产生视向径向速度（RV）偏移。由于可通过凌日外观测估算基础轨道径向速度趋势，RM序列为研究流量诱导的径向速度变化提供了可控实验环境。我们汇编了13个目标在21个RM观测夜期间获取的1171组ESPRESSO观测数据，并训练机器学习模型，从观测到的径向速度、线轮廓诊断参数和活动指标中重建参考径向速度趋势。预测性能在不同恒星间差异显著，且取决于RM信号强度以及目标恒星与训练样本的相似性。将该方法应用于类太阳恒星观测和比邻星（Proxima Centauri）观测时，虽恢复了已知周期性，但未完全消除活动诱导的变异性。需更大规模、更多样化的数据集来评估该方法用于缓解活动诱导径向速度信号的潜力。
英文摘要
The Rossiter-McLaughlin (RM) effect produces apparent radial velocity (RV) shifts through line-profile distortions caused by a transiting planet blocking different regions of the rotating stellar surface. Because the underlying orbital RV trend can be estimated from out-of-transit observations, RM sequences provide a controlled laboratory for studying flux-induced RV variations. We compiled a sample of 1171 ESPRESSO observations of 13 targets obtained during 21 RM observing nights and trained machine-learning models to reconstruct a reference RV trend from observed RVs, line-profile diagnostics, and activity indicators. Predictive performance varied substantially among stars and depended on both the strength of the RM signal and the similarity of the target star to the training sample. Applications to Sun-as-a-star observations and Proxima Centauri recovered known periodicities but did not fully remove the activity-induced variability. Larger and more diverse datasets will be required to assess the potential of this approach for mitigating activity-induced RV signals.
CommentsPublished in RNAAS
