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Multi-layer Perceptron for Predicting Galaxy Parameters (MLP-GaP): Stellar Masses and Star Formation Rates

文献类型：期刊论文

作者Guo, Xiaotong2; Fang, Guanwen2; Feng HC(封海成)1; Zhang, Rui2
刊名RESEARCH IN ASTRONOMY AND ASTROPHYSICS
出版日期2024-12-01
卷号24期号:12
关键词methods: data analysis galaxies: fundamental parameters galaxies: star formation
ISSN号1674-4527
DOI10.1088/1674-4527/ad95d7
产权排序第2完成单位
文献子类Article
英文摘要The large-scale imaging survey will produce massive photometric data in multi-bands for billions of galaxies. Defining strategies to quickly and efficiently extract useful physical information from this data is mandatory. Among the stellar population parameters for galaxies, their stellar masses and star formation rates (SFRs) are the most fundamental. We develop a novel tool, Multi-Layer Perceptron for Predicting Galaxy Parameters (MLP-GaP), that uses a machine learning (ML) algorithm to accurately and efficiently derive the stellar masses and SFRs from multi-band catalogs. We first adopt a mock data set generated by the Code Investigating GALaxy Emission (CIGALE) for training and testing data sets. Subsequently, we used a multi-layer perceptron model to build MLP-GaP and effectively trained it with the training data set. The results of the test performed on the mock data set show that MLP-GaP can accurately predict the reference values. Besides MLP-GaP has a significantly faster processing speed than CIGALE. To demonstrate the science-readiness of the MLP-GaP, we also apply it to a real data sample and compare the stellar masses and SFRs with CIGALE. Overall, the predicted values of MLP-GaP show a very good consistency with the estimated values derived from spectral energy distribution fitting. Therefore, the capability of MLP-GaP to rapidly and accurately predict stellar masses and SFRs makes it particularly well-suited for analyzing huge amounts of galaxies in the era of large sky surveys.
学科主题天文学 ; 星系与宇宙学 ; 恒星与银河系
URL标识查看原文
出版地20A DATUN RD, CHAOYANG, BEIJING, 100101, PEOPLES R CHINA
WOS关键词CONVOLUTIONAL NEURAL-NETWORKS ; DATA RELEASE ; AUTOMATIC CLASSIFICATION ; PHOTOMETRIC REDSHIFTS ; POPULATION SYNTHESIS ; MAIN-SEQUENCE ; EVOLUTION ; ULTRAVIOLET ; EXTINCTION ; COSMOLOGY
资助项目National Nature Science Foundation of China[12303017]; National Nature Science Foundation of China[12203096]; Anhui Provincial Natural Science Foundation[2308085QA33]; China Manned Space Project
WOS研究方向Astronomy & Astrophysics
语种英语
WOS记录号WOS:001376598800001
出版者NATL ASTRONOMICAL OBSERVATORIES, CHIN ACAD SCIENCES
资助机构National Nature Science Foundation of China[12303017, 12203096] ; Anhui Provincial Natural Science Foundation[2308085QA33] ; China Manned Space Project
版本出版稿
源URL[http://ir.ynao.ac.cn/handle/114a53/27852]  
专题云南天文台_丽江天文观测站（南方基地）
作者单位1.Yunnan Observatories, Chinese Academy of Sciences, Kunming 650011, China
2.Institute of Astronomy and Astrophysics, Anqing Normal University, Anqing 246133, China;
推荐引用方式
GB/T 7714		 Guo, Xiaotong,Fang, Guanwen,Feng HC,et al. Multi-layer Perceptron for Predicting Galaxy Parameters (MLP-GaP): Stellar Masses and Star Formation Rates[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2024,24(12).
APA Guo, Xiaotong,Fang, Guanwen,封海成,&Zhang, Rui.(2024).Multi-layer Perceptron for Predicting Galaxy Parameters (MLP-GaP): Stellar Masses and Star Formation Rates.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,24(12).
MLA Guo, Xiaotong,et al."Multi-layer Perceptron for Predicting Galaxy Parameters (MLP-GaP): Stellar Masses and Star Formation Rates".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 24.12(2024).

入库方式： OAI收割

来源：云南天文台

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