A machine learning approach to galactic emission-line region classification

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dc.contributor.authorRhea, Carter L.
dc.contributor.authorRousseau-Nepton, Laurie
dc.contributor.authorMoumen, Ismael
dc.contributor.authorPrunet, Simon
dc.contributor.authorHlavacek-Larrondo, Julie
dc.contributor.authorGrasha, Kathryn
dc.contributor.authorRobert, Carmelle
dc.contributor.authorMorisset, Christophe
dc.contributor.authorStasinska, Grazyna
dc.contributor.authorVale-Asari, Natalia
dc.contributor.authorGiroux, Justine
dc.contributor.authorMcleod, Anna
dc.contributor.authorGendron-Marsolais, Marie-Lou
dc.contributor.authorWang, Junfeng
dc.contributor.authorLyman, Joe
dc.contributor.authorChemin, Laurent
dc.date.accessioned2025-03-21T17:56:30Z
dc.date.available2025-03-21T17:56:30Z
dc.date.issued2023
dc.descriptionIndexación: Scopus
dc.descriptionThe authors would like to thank the Canada–France–Hawaii Telescope (CFHT) which is operated by the National Research Council (NRC) of Canada, the Institut National des Sciences de l’Univers of the Centre National de la Recherche Scientifique (CNRS) of France, and the University of Hawaii. The observations at the CFHT were performed with care and respect from the summit of Maunakea which is a significant cultural and historic site. CLR acknowledges financial support from the physics department of the Université de Montréal, the MITACS scholarship program, and the IVADO doctoral excellence scholarship. JH-L acknowledges support from NSERC via the Discovery grant program, as well as the Canada Research Chair program. NVA acknowledges the support of the Royal Society and the Newton Fund via the award of a Royal Society–Newton Advanced Fellowship (grant NAF\R1\180403), and of Fundação de Amparo à Pesquisa e Inovação de Santa Catarina (FAPESC) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). CR is grateful to the Fonds de recherche du Québec – Nature et Technologies (FRQNT), for SIGNALS team financial support, and to the Natural Sciences and Engineering Research Council of Canada (NSERC) KG is supported by the Australian Research Council through the Discovery Early Career Researcher Award (DECRA) Fellowship DE220100766 funded by the Australian Government. K.G. is supported by the Australian Research Council Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D), through project number CE170100013. The research of L. Chemin is funded by the Fondecyt Regular project 1210992 from Agencia Nacional de Investigacion y Desarrollo de Chile.
dc.description.abstractDiagnostic diagrams of emission-line ratios have been used e xtensiv ely to categorize extragalactic emission regions; ho we ver, 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 abo v e giv en three ke y 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 impro v e 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. © 2023 The Author(s).
dc.description.urihttps://academic-oup-com.recursosbiblioteca.unab.cl/rasti/article/2/1/345/7209183?login=true&token=eyJhbGciOiJub25lIn0.eyJleHAiOjE3NDUxNzA5NTAsImp0aSI6ImZjYzdlMWJiLTRlMmItNDUwNS04YjVmLTMyMjQwYmUxZDMzZSJ9.
dc.identifier.citationRAS Techniques and Instruments. Volume 2, Issue 1, Pages 345 - 359. 1 January 2023
dc.identifier.doi10.1093/rasti/rzad023
dc.identifier.issn2752-8200
dc.identifier.urihttps://repositorio.unab.cl/handle/ria/63842
dc.language.isoen
dc.publisherOxford University Press
dc.rights.licenseAttribution 4.0 International Deed (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectData Methods
dc.subjectGalactic H ii regions
dc.subjectMachine Learning
dc.subjectPlanetary Nebulae
dc.subjectSupernova Remnants
dc.titleA machine learning approach to galactic emission-line region classification
dc.typeArtículo
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