确定加权功率谱的一种新的有效方法：通过分析BiSON数据探测低频太阳p模
A new efficient method for determining weighted power spectra: detection of low-frequency solar p-modes by analysis of BiSON data
摘要：
我们提出了一种新的和高效的算法，用于计算的功率谱均匀间隔的数据相结合的降噪优势的加权拟合的快速傅立叶变换（FFT）的计算优势。我们将这种方法应用于伯明翰太阳振荡网络（BiSON）获得的太阳p模式振荡的10年数据集，从而揭示了三个新的低频模式。这些是l=2、n=5和n=7模式以及l=3、n=7模式。在l=2，n=5模式的情况下，这被认为是在文献中第一次这样识别这种模式。该方法所需的统计权重来自于实际数据和仪器性能的复杂模拟的组合。权重的变化主要是由于各种BiSON仪器的噪声特性的差异，这些特性随时间的变化以及台站和太阳之间视线速度的变化。应当注意，加权数据集将比未加权数据集具有更多的时间依赖性信号，因此，其频谱将更容易受到混叠的影响。
英文摘要：
We present a new and highly efficient algorithm for computing a power spectrum made from evenly spaced data which combines the noise-reducing advantages of the weighted fit with the computational advantages of the Fast Fourier Transform (FFT). We apply this method to a 10-year data set of the solar p-mode oscillations obtained by the Birmingham Solar Oscillations Network (BiSON) and thereby uncover three new low-frequency modes. These are the l=2, n=5 and n=7 modes and the l=3, n=7 mode. In the case of the l=2, n=5 modes, this is believed to be the first such identification of this mode in the literature. The statistical weights needed for the method are derived from a combination of the real data and a sophisticated simulation of the instrument performance. Variations in the weights are due mainly to the differences in the noise characteristics of the various BiSON instruments, the change in those characteristics over time and the changing line-of-sight velocity between the stations and the Sun. It should be noted that a weighted data set will have a more time-dependent signal than an unweighted set and that, consequently, its frequency spectrum will be more susceptible to aliasing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
