恒星振荡时间序列自相关中大分离的检测
On detecting the large separation in the autocorrelation of stellar oscillation times series
摘要：
CoRoT和开普勒空间飞行任务进行的观测提供了大量的小行星地震数据。他们的分析需要一个有效的程序，首先确定恒星是否可靠地显示出类似太阳的振荡，其次测量所谓的大分离，第三估计可以从傅立叶频谱中检索到的小行星地震信息。本文提出了一种基于地震傅里叶谱自相关的方法。我们一直在寻找能够预测地震时间序列自相关分析的输出的标准。首先，考虑到白噪声的贡献，适当地缩放自相关。然后，我们使用零假设H0检验来评估自相关分析的可靠性。进行基于太阳能和CoRoT时间序列的计算，以量化作为自相关信号的幅度的函数的性能。我们提出了一个自动化的大分离，其可靠性量化的H0测试的测定。我们应用这种方法来分析一个大型的红巨星观测的CoRoT。我们估计开普勒任务的测光时间序列的预期性能。最后，我们证明，该方法使得有可能区分l=0从l=1模式。包络自相关函数已被证明是非常强大的噪声asteroseisic数据的大分离的确定，因为它使我们能够量化不同的测量性能的精度：平均大分离，大分离的变化与频率，小分离和程度的识别。
英文摘要：
The observations carried out by the space missions CoRoT and Kepler provide a large set of asteroseismic data. Their analysis requires an efficient procedure first to determine if the star is reliably showing solar-like oscillations, second to measure the so-called large separation, third to estimate the asteroseismic information that can be retrieved from the Fourier spectrum. We develop in this paper a procedure, based on the autocorrelation of the seismic Fourier spectrum. We have searched for criteria able to predict the output that one can expect from the analysis by autocorrelation of a seismic time series. First, the autocorrelation is properly scaled for taking into account the contribution of white noise. Then, we use the null hypothesis H0 test to assess the reliability of the autocorrelation analysis. Calculations based on solar and CoRoT times series are performed in order to quantify the performance as a function of the amplitude of the autocorrelation signal. We propose an automated determination of the large separation, whose reliability is quantified by the H0 test. We apply this method to analyze a large set of red giants observed by CoRoT. We estimate the expected performance for photometric time series of the Kepler mission. Finally, we demonstrate that the method makes it possible to distinguish l=0 from l=1 modes. The envelope autocorrelation function has proven to be very powerful for the determination of the large separation in noisy asteroseismic data, since it enables us to quantify the precision of the performance of different measurements: mean large separation, variation of the large separation with frequency, small separation and degree identification.
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