Autonomous Gaussian Decomposition
Abstract
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 21cm absorption spectra from the 21cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey. We use AGD with Monte Carlo methods to derive the HI 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.
Cite
@article{arxiv.1409.2840,
title = {Autonomous Gaussian Decomposition},
author = {Robert R. Lindner and Carlos Vera-Ciro and Claire E. Murray and Snežana Stanimirović and Brian L. Babler and Carl Heiles and Patrick Hennebelle and W. M. Goss and John Dickey},
journal= {arXiv preprint arXiv:1409.2840},
year = {2015}
}
Comments
12 pages, 8 figures, submitted to AJ