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We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of Gaussian components in the data, and also their locations, widths, and amplitudes. We test AGD and find that it produces results comparable to human-derived solutions on 21 cm absorption spectra from the 21 cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey. We use AGD with Monte Carlo methods to derive the H ɪ line completeness as a function of peak optical depth and velocity width for the 21-SPONGE data, and also show that the results of AGD are stable against varying observational noise intensity. The autonomy and computational efficiency of the method over traditional manual Gaussian fits allow for truly unbiased comparisons between observations and simulations, and for the ability to scale up and interpret the very large data volumes from the upcoming Square Kilometer Array and pathfinder telescopes.

Item Type:	Refereed Article
Keywords:	Gaussian fitting, galactic structure, radio spectroscopy, ISM: atoms, ISM: clouds, ISM: lines and bands, line: identification, methods: data analysis, techniques: spectroscopic
Research Division:	Physical Sciences
Research Group:	Astronomical and Space Sciences
Research Field:	Galactic Astronomy
Objective Division:	Expanding Knowledge
Objective Group:	Expanding Knowledge
Objective Field:	Expanding Knowledge in the Physical Sciences
UTAS Author:	Dickey, J (Professor John Dickey)
ID Code:	102296
Year Published:	2015
Funding Support:	Australian Research Council (DP110104101)
Web of Science® Times Cited:	10
Deposited By:	Mathematics and Physics
Deposited On:	2015-08-11
Last Modified:	2017-11-06
Downloads:	196 View Download Statistics