A machine learning approach to galactic emission-line region classification

Rhea, Carter L; Rousseau-Nepton, Laurie; Moumen, Ismael; Prunet, Simon; Hlavacek-Larrondo, Julie; Grasha, Kathryn; Robert, Carmelle; Morisset, Christophe; Stasinska, Grazyna; Vale-Asari, Natalia; Giroux, Justine; McLeod, Anna; Gendron-Marsolais, Marie-Lou; Wang, Junfeng; Lyman, Joe; Chemin, Laurent

Abstract

Diagnostic diagrams of emission-line ratios have been used extensively to categorize extragalactic emission regions; however, these diagnostics are occasionally at odds with each other due to differing definitions. In this work, we study the applicability of supervised machine-learning techniques to systematically classify emission-line regions from the ratios of certain emission lines. Using the Million Mexican Model database, which contains information from grids of photoionization models using cloudy, and from shock models, we develop training and test sets of emission line fluxes for three key diagnostic ratios. The sets are created for three classifications: classic H ii regions, planetary nebulae, and supernova remnants. We train a neural network to classify a region as one of the three classes defined above given three key line ratios that are present both in the SITELLE and MUSE instruments’ band-passes: [O iii]λ5007/H β, [N ii]λ6583/H α, ([S ii]λ6717+[S ii]λ6731)/H α. We also tested the impact of the addition of the [O ii]λ3726, 3729/[O iii]λ5007 line ratio when available for the classification. A maximum luminosity limit is introduced to improve the classification of the planetary nebulae. Furthermore, the network is applied to SITELLE observations of a prominent field of M33. We discuss where the network succeeds and why it fails in certain cases. Our results provide a framework for the use of machine learning as a tool for the classification of extragalactic emission regions. Further work is needed to build more comprehensive training sets and adapt the method to additional observational constraints.

Citation

Rhea, C. L., Rousseau-Nepton, L., Moumen, I., Prunet, S., Hlavacek-Larrondo, J., Grasha, K., …Chemin, L. (2023). A machine learning approach to galactic emission-line region classification. RAS Techniques and Instruments, 2(1), 345-359. https://doi.org/10.1093/rasti/rzad023

• Journal Article Type: Article
• Acceptance Date: Jun 21, 2023
• Online Publication Date: Jun 27, 2023
• Publication Date: 2023-01
• Deposit Date: Mar 19, 2024
• Publicly Available Date: Mar 19, 2024
• Journal: RAS Techniques and Instruments
• Print ISSN: 2752-8200
• Publisher: Oxford University Press
• Peer Reviewed: Peer Reviewed
• Volume: 2
• Issue: 1
• Pages: 345-359
• DOI: https://doi.org/10.1093/rasti/rzad023
• Keywords: Data Methods, Planetary Nebulae, Galactic H ii regions, Machine Learning, Supernova Remnants
• Public URL: https://durham-repository.worktribe.com/output/1948990