Methods for Averaging Spectral Line Data

Abstract

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.