FliPer: A global measure of power density to estimate surface gravities of main-sequence solar-like stars and red giants
FliPer: A global measure of power density to estimate surface gravities of main-sequence solar-like stars and red giants
复制标题
DOI：
10.1051/0004-6361/201833106
复制
发表时间：
2018-09
影响因子：
6.5
通讯作者：
L. Bugnet;R. Garc'ia;G. Davies;S. Mathur;E. Corsaro;O. Hall;B. Rendle
中科院分区：
文献类型：
--
作者：
L. Bugnet;R. Garc'ia;G. Davies;S. Mathur;E. Corsaro;O. Hall;B. Rendle
星震学利用数以千计的低质量恒星（0.8 M⊙< M < 3 M⊙）的平均全球地震参数和有效温度，提供诸如质量、半径或表面重力等全球恒星参数。该方法已成功地应用于测量由湍流对流激发的声模的恒星。其他方法，如闪烁技术也可以用来确定恒星表面重力，但只适用于log g大于2.5指数。在这项工作中，我们提出了一个新的度量称为FliPer（频谱功率密度中的闪烁，与在时域中计算的标准闪烁测量相反）；它能够在不进行任何地震分析的情况下，对比Kp < 14亮的恒星进行可靠的地表重力测量（0.1 < log g < 4.6 index）。FliPer考虑了在给定频率范围内，以功率密度谱测量的恒星的平均变异性。然而，需要在几个频率范围内计算FliPer值才能更好地表征一颗恒星。利用大量的星震目标，FliPer可以通过机器学习来校准地表重力的行为。这一校正使用随机森林回归量，涵盖了从主序星到亚巨星和红巨星的大范围表面重力，不确定性很小，从0.04到0.1指数。FliPer值可以插入到自动全球地震管道中，既可以给出恒星表面重力的估计，也可以通过检测获得的νmax值中的异常值来评估地震结果的质量。FliPer也只使用奈奎斯特频率太低而无法测量声模特性的长节奏数据来限制主序矮星的表面重力。
Asteroseismology provides global stellar parameters such as masses, radii, or surface gravities using mean global seismic parameters and effective temperature for thousands of low-mass stars (0.8 M⊙ < M < 3 M⊙). This methodology has been successfully applied to stars in which acoustic modes excited by turbulent convection are measured. Other methods such as the Flicker technique can also be used to determine stellar surface gravities, but only works for log g above 2.5 dex. In this work, we present a new metric called FliPer (Flicker in spectral power density, in opposition to the standard Flicker measurement which is computed in the time domain); it is able to extend the range for which reliable surface gravities can be obtained (0.1 < log g < 4.6 dex) without performing any seismic analysis for stars brighter than Kp < 14. FliPer takes into account the average variability of a star measured in the power density spectrum in a given range of frequencies. However, FliPer values calculated on several ranges of frequency are required to better characterize a star. Using a large set of asteroseismic targets it is possible to calibrate the behavior of surface gravity with FliPer through machine learning. This calibration made with a random forest regressor covers a wide range of surface gravities from main-sequence stars to subgiants and red giants, with very small uncertainties from 0.04 to 0.1 dex. FliPer values can be inserted in automatic global seismic pipelines to either give an estimation of the stellar surface gravity or to assess the quality of the seismic results by detecting any outliers in the obtained νmax values. FliPer also constrains the surface gravities of main-sequence dwarfs using only long-cadence data for which the Nyquist frequency is too low to measure the acoustic-mode properties.