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Autonomous Gaussian Decomposition

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posted on 2023-05-18, 12:15 authored by Lindner, RR, Vera-Ciro, C, Murray, CE, Stanimirovic, S, Babler, B, Heiles, C, Hennebelle, P, Goss, WM, John DickeyJohn Dickey
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.

Funding

Australian Research Council

History

Publication title

The Astrophysical Journal

Volume

149

Issue

4

Article number

138

Number

138

Pagination

14

ISSN

0004-637X

Department/School

School of Natural Sciences

Publisher

Univ Chicago Press

Place of publication

1427 E 60Th St, Chicago, USA, Il, 60637-2954

Rights statement

Copyright 2015 The American Astronomical Society

Repository Status

  • Open

Socio-economic Objectives

Expanding knowledge in the physical sciences

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    University Of Tasmania

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