A machine learning approach to galactic emission-line region classification

Abstract

Diagnostic 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. Show more