2014/09/09 by Robert R. Lindner, Carlos Vera-Ciro, Claire E. Murray +7 · 85 citations
Physics and Astronomy · #Astronomy and Astrophysical Research #Galaxies: Formation, Evolution, Phenomena #Gaussian #Gaussian function #Gaussian process #Line (geometry) #Monte Carlo method #Noise (video) #Pathfinder #Radio Astronomy Observations and Technology #Spectral line #astro-ph.GA #astro-ph.IM
paper · pdf · doi:10.1088/0004-6256/149/4/138
published in The Astronomical Journal 149(4), 138 (Institute of Physics) · 12 pages, 8 figures, submitted to AJ
arxiv created 2014/09/09 · openalex publication_date 2015/03/23 · arxiv updated 2015/06/22 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/06
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 i 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.