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 loc...

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Bibliographic Details
Published in:The Astronomical journal 2015-04, Vol.149 (4), p.1-14
Main Authors: Lindner, Robert R., Vera-Ciro, Carlos, Murray, Claire E., Stanimirovi, Sne ana, Babler, Brian, Heiles, Carl, Hennebelle, Patrick, Goss, W. M., Dickey, John
Format: Article
Language:English
Subjects:
ABSORPTION SPECTRA
ABSORPTION SPECTROSCOPY
ALGORITHMS
Arrays
ASTRONOMY
ASTROPHYSICS, COSMOLOGY AND ASTRONOMY
Autonomous
COMPARATIVE EVALUATIONS
DATA ANALYSIS
Decomposition
EFFICIENCY
Gaussian
HYDROGEN
ISM: atoms
ISM: clouds
ISM: lines and bands
line: identification
Manuals
Mathematical models
MATHEMATICAL SOLUTIONS
methods: data analysis
MONTE CARLO METHOD
Monte Carlo methods
NOISE
techniques: spectroscopic
TELESCOPES
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Summary: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 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.
ISSN:0004-6256
1538-3881
1538-3881
DOI:10.1088/0004-6256/149/4/138