2023/10/13 by L. D. Anderson, Anderson, L. D., Bin Liu +13
Chemistry · Earth and Planetary Sciences · #Atmospheric Ozone and Climate #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Spectroscopy and Chemometric Analyses #Spectroscopy and Laser Applications
paper · pdf · doi:10.48550/arxiv.2310.09076
openalex publication_date 2023/10/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 (SNR), 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 SNR is maximized when all spectra are averaged, for noise and uniform weighting we find that averaging the 35-45% of spectra with the highest SNR results in the highest SNR 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 (GDIGS) hydrogen radio recombination line (RRL) data to determine the ionic abundance ratio, y+, and discuss future applications of the methodology.