Dusty Stellar Source Classification by Implementing Machine Learning Methods Based on Spectroscopic Observations in the Magellanic Clouds

The Astrophysical Journal, vol. 986, pp. 168

Abstract

Abstract Dusty stellar point sources are a significant stage in stellar evolution and contribute to the metal enrichment of galaxies. These objects can be classified using photometric and spectroscopic observations using color–magnitude diagrams and infrared excesses in spectral energy distributions. We have employed supervised machine learning spectral classification to categorize dusty stellar point sources, including young stellar objects (YSOs) and evolved stars comprising oxygen- and carbon-rich asymptotic giant branch stars (AGBs), red supergiants (RSGs), and post-AGB stars (PAGBs) in the Large and Small Magellanic Clouds, based on spectroscopic labeled data derived from the Surveying the Agents of Galaxy Evolution (SAGE) project, which involved 12 multiwavelength filters and 618 stellar objects. Despite dealing with missing values and uncertainties in the SAGE spectral data sets, we achieved accurate classifications of these sources. To address the challenge of working with small and imbalanced spectral catalogs, we utilized the Synthetic Minority Oversampling Technique (SMOTE), which generates synthetic data points. Subsequently, among all the models applied before and after data augmentation, the probabilistic random forest (PRF) classifier, a tuned random forest, demonstrated the highest total accuracy, reaching 89% based on the recall metric in categorizing dusty stellar sources. In this study, using the SMOTE technique does not improve the accuracy of the best model for the CAGB, PAGB, and RSG classes; it stays at 100%, 100%, and 88%, respectively. However, there are variations in the OAGB and YSO classes. Accordingly, we collected photometrically labeled data with properties similar to the training data set and classified them using the top four PRF models with an accuracy of more than 87%. We also collected multiwavelength data from several studies to classify them using our consensus model, which integrates the four top models to present common labels as the final prediction.

Authors 10

1. Sepideh Ghaziasgar corresponding

   Institute for Research in Fundamental Sciences

   Affiliation as printed

   School of Astronomy, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5531, Tehran, Iran

2. Mahdi Abdollahi corresponding

   Institute for Research in Fundamental Sciences

   Affiliation as printed

   School of Astronomy, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5531, Tehran, Iran

3. Atefeh Javadi corresponding

   Institute for Research in Fundamental Sciences

   Affiliation as printed

   School of Astronomy, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5531, Tehran, Iran

4. Jacco Th. van Loon corresponding

   Keele University

   Affiliation as printed

   Lennard-Jones Laboratories, Keele University, Keele, ST5 5BG, UK

5. Iain McDonald corresponding

   University of Manchester

   Affiliation as printed

   Jodrell Bank Centre for Astrophysics, Alan Turing Building, University of Manchester, Manchester, M13 9PL, UK

6. Joana M. Oliveira corresponding

   Keele University

   Affiliation as printed

   Lennard-Jones Laboratories, Keele University, Keele, ST5 5BG, UK

7. Amirhossein Masoudnezhad corresponding

   Sharif University of Technology

   Affiliation as printed

   Department of Physics, Sharif University of Technology, P.O. Box 11155-9161, Tehran, Iran

8. Habib G. Khosroshahi corresponding

   Institute for Research in Fundamental Sciences

   Affiliation as printed

   School of Astronomy, Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5531, Tehran, Iran

9. Bernard H. Foing corresponding Aachen Leiden Observatory

   Leiden University · Leiden Observatory

   Affiliation as printed

   Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, The Netherlands

10. Fatemeh Fazel Hesar corresponding Aachen Leiden Institute of Advanced Computer Science

    Leiden University

    Affiliation as printed

    Leiden Institute of Advanced Computer Science, P.O. Box 9504, 2300 RA Leiden, The Netherlands