AUTONOMOUS GAUSSIAN DECOMPOSITION
- Department of Astronomy, University of Wisconsin, 475 North Charter Street, Madison, WI 53706 (United States)
- Radio Astronomy Lab, UC Berkeley, 601 Campbell Hall, Berkeley, CA 94720 (United States)
- Laboratoire AIM, Paris-Saclay, CEA/IRFU/SAp-CNRS-Université Paris Diderot, F-91191 Gif-sur Yvette Cedex (France)
- National Radio Astronomy Observatory, P.O. Box O, 1003 Lopezville, Socorro, NM 87801 (United States)
- University of Tasmania, School of Maths and Physics, Private Bag 37, Hobart, TAS 7001 (Australia)
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.
- OSTI ID:
- 22520241
- Journal Information:
- Astronomical Journal (Online), Vol. 149, Issue 4; Other Information: Country of input: International Atomic Energy Agency (IAEA); ISSN 1538-3881
- Country of Publication:
- United States
- Language:
- English
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