论文标题
天鹅：来自光度光曲线的恒星表面重力的数据驱动的推断
The Swan: Data-Driven Inference of Stellar Surface Gravities for Cool Stars from Photometric Light Curves
论文作者
论文摘要
众所周知，恒星光曲线可以编码物理恒星特性。需要精确，自动化和计算廉价的方法来从光曲线中得出物理参数，以应对从开普勒和苔丝等空间任务中大量涌入这些数据的大量涌入。在这里，我们提出了一种新方法，我们称之为天鹅，这是一种快速，可推广和有效的方法，用于从开普勒光曲线中使用局部线性回归来推导主序列，次级和红色巨星的恒星表面重力（$ \ log g $），该恒星使用局部线性回归，对Kepler Long Cadence long Cadence Power Spectra的完整频率含量。 With this inexpensive data-driven approach, we recover $\log g$ to a precision of $\sim$0.02 dex for 13,822 stars with seismic $\log g$ values between 0.2-4.4 dex, and $\sim$0.11 dex for 4,646 stars with Gaia derived $\log g$ values between 2.3-4.6 dex.我们进一步开发了一个信号到噪声指标，发现在许多很酷的主要序列星（$ t _ {\ text {eff}} $ $ $ \ lysesim $ 5500 k）中，很难检测到颗粒化，尤其是k矮人。通过将我们的$ \ log g $测量与Gaia Radii相结合，我们以4,646个子级和主序列星级得出了经验质量，中位精度为$ \ sim $ 7％。最后，我们证明我们的方法可用于将$ \ log g $恢复到27天的苔丝基线的类似平均绝对偏差精度。我们的方法可以很容易地应用于光度法时间序列观测值，以将恒星表面重力推断为在进化状态的高精度。
Stellar light curves are well known to encode physical stellar properties. Precise, automated and computationally inexpensive methods to derive physical parameters from light curves are needed to cope with the large influx of these data from space-based missions such as Kepler and TESS. Here we present a new methodology which we call The Swan, a fast, generalizable and effective approach for deriving stellar surface gravity ($\log g$) for main sequence, subgiant and red giant stars from Kepler light curves using local linear regression on the full frequency content of Kepler long cadence power spectra. With this inexpensive data-driven approach, we recover $\log g$ to a precision of $\sim$0.02 dex for 13,822 stars with seismic $\log g$ values between 0.2-4.4 dex, and $\sim$0.11 dex for 4,646 stars with Gaia derived $\log g$ values between 2.3-4.6 dex. We further develop a signal-to-noise metric and find that granulation is difficult to detect in many cool main sequence stars ($T_{\text{eff}}$ $\lesssim$ 5500 K), in particular K dwarfs. By combining our $\log g$ measurements with Gaia radii, we derive empirical masses for 4,646 subgiant and main sequence stars with a median precision of $\sim$7%. Finally, we demonstrate that our method can be used to recover $\log g$ to a similar mean absolute deviation precision for TESS-baseline of 27 days. Our methodology can be readily applied to photometric time-series observations to infer stellar surface gravities to high precision across evolutionary states.
