Identifying Physical Structures in Our Galaxy with Gaussian Mixture Models: An Unsupervised Machine Learning Technique
The Astrophysical Journal, vol. 958, pp. 136
Abstract
Abstract We explore the potential of the Gaussian mixture model (GMM), an unsupervised machine-learning method, to identify coherent physical structures in the interstellar medium. The implementation we present can be used on any kind of spatially and spectrally resolved data set. We provide a step-by-step guide to use these models on different sources and data sets. Following the guide, we run the models on NGC 1977, RCW 120, and RCW 49 using the [C ii] 158 μm mapping observations from the SOFIA telescope. We find that the models identified six, four, and five velocity coherent physical structures in NGC 1977, RCW 120, and RCW 49, respectively, which are validated by analyzing the observed spectra toward these structures and by comparison to earlier findings. In this work we demonstrate that GMM is a powerful tool that can better automate the process of spatial and spectral analysis to interpret mapping observations.
Authors 17
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M. Tiwari corresponding
Max Planck Institute for Radio Astronomy · University of Maryland, College Park
Affiliation as printed
Max Planck Institute for Radioastronomy, Auf dem Hügel, D-53121 Bonn, Germany
University of Maryland, Department of Astronomy, College Park, MD 20742-2421, USA
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Leiden University · Leiden Observatory
Affiliation as printed
Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, The Netherlands
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Affiliation as printed
I. Physik. Institut, University of Cologne, Zülpicher Str. 77, D-50937 Cologne, Germany
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Affiliation as printed
SOFIA Science Center, USRA, NASA Ames Research Center, M.S. N232-12, Moffett Field, CA 94035, USA
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Courant Institute of Mathematical Sciences · New York University
Affiliation as printed
Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
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Affiliation as printed
I. Physik. Institut, University of Cologne, Zülpicher Str. 77, D-50937 Cologne, Germany
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Affiliation as printed
I. Physik. Institut, University of Cologne, Zülpicher Str. 77, D-50937 Cologne, Germany
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Affiliation as printed
I. Physik. Institut, University of Cologne, Zülpicher Str. 77, D-50937 Cologne, Germany
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University of Maryland, College Park
Affiliation as printed
University of Maryland, Department of Astronomy, College Park, MD 20742-2421, USA
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Affiliation as printed
SOFIA Science Center, USRA, NASA Ames Research Center, M.S. N232-12, Moffett Field, CA 94035, USA
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Leiden University · Instituto de Física Fundamental · Leiden Observatory
Affiliation as printed
Instituto de Física Fundamental, CSIC, Calle Serrano 121-123, E-28006 Madrid, Spain
Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, The Netherlands
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University of Maryland, College Park
Affiliation as printed
University of Maryland, Department of Astronomy, College Park, MD 20742-2421, USA
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Affiliation as printed
I. Physik. Institut, University of Cologne, Zülpicher Str. 77, D-50937 Cologne, Germany
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Affiliation as printed
I. Physik. Institut, University of Cologne, Zülpicher Str. 77, D-50937 Cologne, Germany
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Affiliation as printed
I. Physik. Institut, University of Cologne, Zülpicher Str. 77, D-50937 Cologne, Germany
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University of Maryland, College Park
Affiliation as printed
University of Maryland, Department of Astronomy, College Park, MD 20742-2421, USA
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Leiden University · Leiden Observatory · University of Maryland, College Park
Affiliation as printed
Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, The Netherlands
University of Maryland, Department of Astronomy, College Park, MD 20742-2421, USA
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