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Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star-Quasar-Galaxy Catalog

文献类型：期刊论文

作者Feng HC(封海成)14,15,16,17; Li, Rui13; Napolitano, Nicola R.10,11,12; Li SS(李莎莎)14,15,16,17; Bai JM(白金明)14,15,16,17; Dong, Yue9; Li, Ran7,8; Liu HT(刘洪涛)15,16,17; Lu KX(卢开兴)15,16,17; Pan, Zhi-Wei5,6
刊名ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES
出版日期2025-07-01
卷号279期号:1
ISSN号0067-0049
DOI10.3847/1538-4365/adde5a
产权排序第1完成单位
文献子类Article
英文摘要We present a novel multimodal neural network (MNN) for classifying astronomical sources in multiband ground-based observations, from optical to near-infrared (NIR), to separate sources in stars, galaxies, and quasars. Our approach combines a convolutional neural network branch for learning morphological features from r-band images with an artificial neural network branch for extracting spectral energy distribution (SED) information. Specifically, we have used nine-band optical (ugri) and NIR (ZYHJKs) data from the Kilo-Degree Survey (KiDS) Data Release 5. The two branches of the network are concatenated and feed into fully connected layers for final classification. We train the network on a spectroscopically confirmed sample from the Sloan Digital Sky Survey crossmatched with KiDS. The trained model achieves 98.76% overall accuracy on an independent testing data set, with F1-scores exceeding 95% for each class. Raising the output probability threshold, we obtain higher purity at the cost of lower completeness. We have also validated the network using external catalogs crossmatched with KiDS, correctly classifying 99.74% of a pure star sample selected from Gaia parallaxes and proper motions, and 99.74% of an external galaxy sample from the Galaxy and Mass Assembly survey, adjusted for low-redshift contamination. We apply the trained network to 27,335,836 KiDS DR5 sources with r <= 23 mag to generate a new classification catalog. This MNN successfully leverages both morphological and SED information to enable efficient and robust classification of stars, quasars, and galaxies in large photometric surveys.
学科主题天文学 ; 星系与宇宙学
URL标识查看原文
出版地TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
WOS关键词DIGITAL SKY SURVEY ; DATA RELEASE ; ENERGY-DISTRIBUTIONS ; QSO CLASSIFICATION ; SELECTION ; REDSHIFT ; PARAMETERS ; SCIENCE ; SEARCH ; MATTER
资助项目MOST divided by National Natural Science Foundation of China (NSFC)https://doi.org/10.13039/501100001809[2021YFA1600404]; National Key R&D Program of China[12203096]; National Key R&D Program of China[12303022]; National Key R&D Program of China[12203050]; National Key R&D Program of China[12373018]; National Key R&D Program of China[11991051]; National Key R&D Program of China[12203041]; National Natural Science Foundation of China[202301AT070358]; National Natural Science Foundation of China[202301AT070339]; Yunnan Fundamental Research Projects; Yunnan Postdoctoral Research Foundation; Special Research Assistant Funding Project of Chinese Academy of Sciences[CMS-CSST-2021-A01]; China Manned Space Project[2024JJ2040]; Hunan Outstanding Youth Science Foundation[12150710511]; NSFC, Research Fund for Excellent International Scholars
WOS研究方向Astronomy & Astrophysics
语种英语
WOS记录号WOS:001523832500001
出版者IOP Publishing Ltd
资助机构MOST divided by National Natural Science Foundation of China (NSFC)https://doi.org/10.13039/501100001809[2021YFA1600404] ; National Key R&D Program of China[12203096, 12303022, 12203050, 12373018, 11991051, 12203041] ; National Natural Science Foundation of China[202301AT070358, 202301AT070339] ; Yunnan Fundamental Research Projects ; Yunnan Postdoctoral Research Foundation ; Special Research Assistant Funding Project of Chinese Academy of Sciences[CMS-CSST-2021-A01] ; China Manned Space Project[2024JJ2040] ; Hunan Outstanding Youth Science Foundation[12150710511] ; NSFC, Research Fund for Excellent International Scholars
版本出版稿
源URL[http://ir.ynao.ac.cn/handle/114a53/28405]  
专题云南天文台_丽江天文观测站（南方基地）
云南天文台_中国科学院天体结构与演化重点实验室
星系类星体研究组
通讯作者Feng HC(封海成)
作者单位1.South-Western Institute for Astronomy Research, Yunnan University, Kunming 650500, People’s Republic of China
2.Department of Physics, School of Physics and Electronics, Hunan Normal University, Changsha 410081, People’s Republic of China;
3.Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, People’s Republic of China;
4.INAF—Osservatorio Astronomico di Padova, via dell’Osservatorio 5, 35122 Padova, Italy;
5.Kavli Institute for Astronomy and Astrophysics, Peking University, Beijing 100871, People’s Republic of China;
6.Department of Astronomy, School of Physics, Peking University, Beijing 100871, People’s Republic of China;
7.National Astronomical Observatories, Chinese Academy of Sciences, 20A Datun Road, Chaoyang District, Beijing 100012, People’s Republic of China;
8.University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China;
9.School of Mathematics and Physics, Xi’an Jiaotong-Liverpool University, 111 Renai Road, Suzhou, 215123, People’s Republic of China;
10.INAF—Osservatorio Astronomico di Capodimonte, Salita Moiariello 16, 80131—Napoli, Italy;
推荐引用方式
GB/T 7714		 Feng HC,Li, Rui,Napolitano, Nicola R.,et al. Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star-Quasar-Galaxy Catalog[J]. ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,2025,279(1).
APA 封海成.,Li, Rui.,Napolitano, Nicola R..,李莎莎.,白金明.,...&Zhang, Yang-Wei.(2025).Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star-Quasar-Galaxy Catalog.ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,279(1).
MLA 封海成,et al."Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star-Quasar-Galaxy Catalog".ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES 279.1(2025).

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来源：云南天文台

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