تقرير
A Machine Learning Approach to Galactic Emission-Line Region Classification
| العنوان: | A Machine Learning Approach to Galactic Emission-Line Region Classification |
|---|---|
| المؤلفون: | Rhea, Carter Lee, Rousseau-Nepton, Laurie, Moumen, Ismael, Prunet, Simon, Hlavacek-Larrondo, Julie, Grasha, Kathryn, Roberts, Carmelle, Morisset, Christophe, Stasinska, Grazyna, Vale-Asari, Natalia, Giroux, Justine, McLeod, Anna, Gendron-Marsolais, Marie-Lou, Wang, Junfeng, Lyman, Joe, Chemin, Laurent |
| سنة النشر: | 2023 |
| المجموعة: | Astrophysics |
| مصطلحات موضوعية: | Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics |
| الوصف: | 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 \texttt{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 \hii{} 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: [{\sc O\,iii}]$\lambda5007$/H$\beta$, [{\sc N\,ii}]$\lambda6583$/H$\alpha$, ([{\sc S\,ii}]$\lambda6717$+[{\sc S\,ii}]$\lambda6731$)/H$\alpha$. We also tested the impact of the addition of the [{\sc O\,ii}]$\lambda3726,3729$/[{\sc O\,iii}]$\lambda5007$ 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. Comment: 17 pages; 17 figures; Accepted to RASTI |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2306.11545 |
| رقم الأكسشن: | edsarx.2306.11545 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |