The ideal spectral averaging method depends on one’s science goals and the available information about one’s data. Including low-quality data in the average can decrease the signal-to-noise ratio (S/N), which may necessitate an optimization method or a consideration of different weighting schemes. Here, we explore a variety of spectral averaging methods. We investigate the use of three weighting schemes during averaging: weighting by the signal divided by the variance (“intensity-noise weighting”), weighting by the inverse of the variance (“noise weighting”), and uniform weighting. Whereas for intensity-noise weighting the S/N is maximized when all spectra are averaged, for noise and uniform weighting we find that averaging the 35%–45% of spectra with the highest S/N results in the highest S/N average spectrum. With this intensity cutoff, the average spectrum with noise or uniform weighting has ∼95% of the intensity of the spectrum created from intensity-noise weighting. We apply our spectral averaging methods to GBT Diffuse Ionized Gas hydrogen radio recombination line data to determine the ionic abundance ratio, y ⁺, and discuss future applications of the methodology.

中文翻译：

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平均谱线数据的方法


理想的光谱平均方法取决于一个人的科学目标和有关数据的可用信息。在平均值中包含低质量数据会降低信噪比 (S/N)，这可能需要优化方法或考虑不同的加权方案。在这里，我们探索各种光谱平均方法。我们研究了平均期间三种加权方案的使用：通过信号除以方差进行加权（“强度噪声加权”）、通过方差的倒数进行加权（“噪声加权”）和均匀加权。对于强度噪声加权，当对所有光谱进行平均时，S/N 最大化，而对于噪声和均匀加权，我们发现对具有最高 S/N 的 35%–45% 光谱进行平均会得到最高 S/N 平均光谱。通过这种强度截止，具有噪声或均匀加权的平均频谱具有由强度噪声加权创建的频谱强度的~95%。我们将光谱平均方法应用于 GBT 扩散电离气体氢无线电复合线数据，以确定离子丰度比 y ⁺ ，并讨论该方法的未来应用。