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arXiv · 2310.09076

Methods for Averaging Spectral Line Data

L. D. Anderson·B. Liu·Dana S. Balser·T. M. Bania·L. M. Haffner·Dylan J. Linville·Matteo Luisi·Trey V. Wenger
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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 (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.

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L. D. Anderson, B. Liu, Dana S. Balser, T. M. Bania, L. M. Haffner, Dylan J. Linville, Matteo Luisi, Trey V. Wenger. 2023-10-13. Methods for Averaging Spectral Line Data. https://arxiv.org/abs/2310.09076

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