GAUSSPY+: A fully automated Gaussian decomposition package for emission line spectra
Journal article, 2019

Our understanding of the dynamics of the interstellar medium is informed by the study of the detailed velocity structure of emission line observations. One approach to study the velocity structure is to decompose the spectra into individual velocity components; this leads to a description of the data set that is significantly reduced in complexity. However, this decomposition requires full automation lest it become prohibitive for large data sets, such as Galactic plane surveys. We developed GAUSSPY+, a fully automated Gaussian decomposition package that can be applied to emission line data sets, especially large surveys of HI and isotopologues of CO. We built our package upon the existing GAUSSPY algorithm and significantly improved its performance for noisy data. New functionalities of GAUSSPY+ include: (i) automated preparatory steps, such as an accurate noise estimation, which can also be used as stand-alone applications; (ii) an improved fitting routine; (iii) an automated spatial refitting routine that can add spatial coherence to the decomposition results by refitting spectra based on neighbouring fit solutions. We thoroughly tested the performance of GAUSSPY+ on synthetic spectra and a test field from the Galactic Ring Survey. We found that GAUSSPY+ can deal with cases of complex emission and even low to moderate signal-to-noise values.

methods: data analysis

radio lines: general

ISM: lines and bands

ISM: kinematics and dynamics


M. Riener

Max Planck Society

Heidelberg University

Jouni Kainulainen

Chalmers, Space, Earth and Environment, Astronomy and Plasmaphysics

Max Planck Society

J. D. Henshaw

Max Planck Society

Jan Orkisz

Chalmers, Space, Earth and Environment, Astronomy and Plasmaphysics

C. E. Murray

Johns Hopkins University

H. Beuther

Max Planck Society

Astronomy and Astrophysics

0004-6361 (ISSN) 1432-0746 (eISSN)

Vol. 628 A78

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Swedish Research Council (VR) (2017-03864), 2018-01-01 -- 2020-12-31.

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Subject Categories

Probability Theory and Statistics

Signal Processing

Medical Image Processing



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