Machine-learning approaches for classifying star-forming galaxies and active galactic nuclei from MIGHTEE-detected radio sources in the COSMOS field
文献类型：期刊论文
| 作者 | Silima, Walter2,3; An FX(安芳霞)2,4,5; Vaccari, Mattia1,2,3; Hussein, Eslam A2; Randriamampandry, S6 |
| 刊名 | MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
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| 出版日期 | 2025-11 |
| 卷号 | 544期号:1页码:799-814 |
| 关键词 | methods: observational software: machine learning galaxies: evolution galaxies: formation radio continuum: galaxies |
| ISSN号 | 0035-8711 |
| DOI | 10.1093/mnras/staf1698 |
| 产权排序 | 第3完成单位 |
| 文献子类 | Article |
| 英文摘要 | Radio synchrotron emission originates from both massive star formation and black hole accretion, two processes that drive galaxy evolution. Efficient classification of sources dominated by either process is therefore essential for fully exploiting deep, wide-field extragalactic radio continuum surveys. In this study, we implement, optimize, and compare five widely used supervised machine-learning (ML) algorithms to classify radio sources detected in the MeerKAT International GHz Tiered Extragalactic Exploration (MIGHTEE)-COSMOS survey as star-forming galaxies (SFGs) and active galactic nuclei (AGNs). Training and test sets are constructed from conventionally classified MIGHTEE-COSMOS sources, and 18 physical parameters of the MIGHTEE-detected sources are evaluated as input features. As anticipated, our feature analyses rank the five parameters used in conventional classification as the most effective: the infrared-radio correlation parameter (q(IR)), the optical compactness morphology parameter (class_star), stellar mass, and two combined mid-infrared colours. By optimizing the ML models with these selected features and testing classifiers across various feature combinations, we find that model performance generally improves as additional features are incorporated. Overall, all five algorithms yield an F1-score (the harmonic mean of precision and recall) >90 per cent even when trained on only 20 per cent of the data set. Among them, the distance-based k-nearest neighbours classifier demonstrates the highest accuracy and stability, establishing it as a robust and effective method for classifying SFGs and AGNs in upcoming large radio continuum surveys. |
| 学科主题 | 天文学 ; 星系与宇宙学 ; 计算机科学技术 ; 人工智能 |
| URL标识 | 查看原文 |
| 出版地 | GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND |
| WOS关键词 | LESS-THAN 6 ; EVOLUTION ; EMISSION |
| 资助项目 | National Natural Science Foundation of China[12303016]; National Research Foundation[150551]; National Research Foundation[SRUG22031677]; Natural Science Foundation of Jiangsu Province[BK20242115] |
| WOS研究方向 | Astronomy & Astrophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:001607419700001 |
| 出版者 | OXFORD UNIV PRESS |
| 资助机构 | National Natural Science Foundation of China[12303016] ; National Research Foundation[150551, SRUG22031677] ; Natural Science Foundation of Jiangsu Province[BK20242115] |
| 版本 | 出版稿 |
| 源URL | [http://ir.ynao.ac.cn/handle/114a53/28696]  |
| 专题 | 星系类星体研究组 |
| 通讯作者 | An FX(安芳霞) |
| 作者单位 | 1.INAF – Istituto di Radioastronomia, via Gobetti 101, I-40129 Bologna, Italy; 2.Inter-University Institute for Data Intensive Astronomy (IDIA), Department of Physics and Astronomy, University of the Western Cape, 7535 Bellville, Cape Town, South Africa; 3.Inter-University Institute for Data Intensive Astronomy (IDIA), Department of Astronomy, University of Cape Town, 7701 Rondebosch, Cape Town, South Africa; 4.Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, P. R. China; 5.Purple Mountain Observatory, Chinese Academy of Sciences, 10 Yuanhua Road, Qixia District, Nanjing 210023, P. R. China; 6.A&A, Department of Physics, Faculty of Sciences, University of Antananarivo, P.O. Box 906, Antananarivo 101, Madagascar |
| 推荐引用方式 GB/T 7714 | Silima, Walter,An FX,Vaccari, Mattia,et al. Machine-learning approaches for classifying star-forming galaxies and active galactic nuclei from MIGHTEE-detected radio sources in the COSMOS field[J]. MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,2025,544(1):799-814. |
| APA | Silima, Walter,安芳霞,Vaccari, Mattia,Hussein, Eslam A,&Randriamampandry, S.(2025).Machine-learning approaches for classifying star-forming galaxies and active galactic nuclei from MIGHTEE-detected radio sources in the COSMOS field.MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY,544(1),799-814. |
| MLA | Silima, Walter,et al."Machine-learning approaches for classifying star-forming galaxies and active galactic nuclei from MIGHTEE-detected radio sources in the COSMOS field".MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 544.1(2025):799-814. |
入库方式： OAI收割
来源：云南天文台
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