论文标题
Carmenes搜索M矮人周围的系外行星:基准在高精度径向速度测量中的活动的影响
The CARMENES search for exoplanets around M dwarfs: Benchmarking the impact of activity in high-precision radial velocity measurements
论文作者
论文摘要
当前使用径向速度(RV)技术的当前系外行星调查是针对M矮人的,因为任何可居住区的陆地质量行星都将在较短的时期引起高RV和轨道,而不是对于更大的恒星。主要的警告之一是,矮人表现出从无效到非常活跃的广泛活性水平,这可能会在线曲线中诱导不对称性,从而诱导不对称性,从而诱发虚假的RV测量。我们的目标是使用普通添加期卡门的观测值和近红外观测值对恒星活动对高精度RV测量的影响进行基准测试。我们使用新开发的低分辨率多普勒成像技术来确定八个观测时期的光中心或点诱导的RV组件。我们证实了独立测量的光中心和色度指数之间具有统计学意义且强的相关性,这是RVS波长的振幅变化的量度。我们还发现,几个活动指数与RV的圆形闭环关系,用于仅涵盖几个旋转周期的数据子集。我们还研究了大相位间隙在活性指标期刊周期图中的含义。最后,通过删除点诱导的RV组件,我们将行星质量的灵敏度提高至少三个。我们得出的结论是,对于活跃的M恒星,常规观察策略是识别和消除相关噪声源的最有效方法。
Current exoplanet surveys using the radial velocity (RV) technique are targeting M dwarfs because any habitable zone terrestrial-mass planets will induce a high RV and orbit on shorter periods than for more massive stars. One of the main caveats is that M dwarfs show a wide range of activity levels from inactive to very active, which can induce an asymmetry in the line profiles and, consequently, a spurious RV measurement. We aim to benchmark the impact of stellar activity on high-precision RV measurements using regular-cadence CARMENES visible and near-infrared observations of the active M3.5 dwarf EV Lac. We used the newly developed technique of low-resolution Doppler imaging to determine the centre-of-light, or spot-induced RV component, for eight observational epochs. We confirm a statistically significant and strong correlation between the independently measured centre-of-light and the chromatic index, which is a measure of the amplitude variation with wavelength of the RVs. We also find circular closed-loop relations of several activity indices with RV for a subset of data that covers only several rotation periods. We also investigate the implications of large phase gaps in the periodograms of activity indicators. Finally, by removing the spot-induced RV component we improve the planet-mass sensitivity by a factor of at least three. We conclude that for active M stars, a regular-cadence observing strategy is the most efficient way to identify and eliminate sources of correlated noise.