Identifying physical structures in our Galaxy with Gaussian Mixture Models: An unsupervised machine learning technique

TL;DR

This paper demonstrates that Gaussian Mixture Models can effectively identify coherent physical structures in the interstellar medium using spatially and spectrally resolved data, validated on multiple galactic regions.

Contribution

It introduces a step-by-step methodology for applying GMM to astrophysical data and validates its effectiveness in identifying physical structures in the ISM.

Findings

GMM identified 6 structures in NGC 1977

GMM identified 4 structures in RCW 120

GMM identified 5 structures in RCW 49

Abstract

We explore the potential of the Gaussian Mixture Model (GMM), an unsupervised machine learning method, to identify coherent physical structures in the ISM. 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 [CII] 158 $\mu$m mapping observations from the SOFIA telescope. We find that the models identified 6, 4 and 5 velocity coherent physical structures in NGC 1977, RCW 120 and RCW 49, respectively, which are validated by analysing the observed spectra towards 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.