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Predicting Supermassive Black Hole Mass with Machine Learning Methods

文献类型：期刊论文

作者He, Yi1,2; Guo, Qi1,2; Shao, Shi1
刊名RESEARCH IN ASTRONOMY AND ASTROPHYSICS
出版日期2022-08-01
卷号22期号:8页码:9
关键词(galaxies:) quasars: supermassive black holes galaxies: evolution methods: data analysis
ISSN号1674-4527
DOI10.1088/1674-4527/ac777f
英文摘要It is crucial to measure the mass of supermassive black holes (SMBHs) in understanding the co-evolution between the SMBHs and their host galaxies. Previous methods usually require spectral data which are expensive to obtain. We use the AGN catalog from the Sloan Digital Sky Survey project Data Release 7 (DR7) to investigate the correlations between SMBH mass and their host galaxy properties. We apply the machine learning algorithms, such as Lasso regression, to establish the correlation between the SMBH mass and various photometric properties of their host galaxies. We find an empirical formula that can predict the SMBH mass according to galaxy luminosity, colors, surface brightness, and concentration. The root-mean-square error is 0.5 dex, comparable to the intrinsic scatter in SMBH mass measurements. The 1 sigma scatter in the relation between the SMBH mass and the combined galaxy properties relation is 0.48 dex, smaller than the scatter in the SMBH mass versus galaxy stellar mass relation. This relation could be used to study the SMBH mass function and the AGN duty cycles in the future.
WOS关键词DIGITAL SKY SURVEY ; ACTIVE GALACTIC NUCLEI ; RADIUS-LUMINOSITY RELATIONSHIP ; STAR-FORMING GALAXIES ; DATA RELEASE ; COSMOLOGICAL SIMULATIONS ; ELLIPTIC GALAXIES ; H-ALPHA ; AGN ; COEVOLUTION
资助项目National Key Research and Development of China[2018YFA0404503] ; NSFC[12033008] ; NSFC[11988101] ; K.C.Wong Education Foundation ; China.Manned Space Project[CMS-CSST-2021-A03]
WOS研究方向Astronomy & Astrophysics
语种英语
WOS记录号WOS:000832500200001
出版者NATL ASTRONOMICAL OBSERVATORIES, CHIN ACAD SCIENCES
资助机构National Key Research and Development of China ; National Key Research and Development of China ; NSFC ; NSFC ; K.C.Wong Education Foundation ; K.C.Wong Education Foundation ; China.Manned Space Project ; China.Manned Space Project ; National Key Research and Development of China ; National Key Research and Development of China ; NSFC ; NSFC ; K.C.Wong Education Foundation ; K.C.Wong Education Foundation ; China.Manned Space Project ; China.Manned Space Project ; National Key Research and Development of China ; National Key Research and Development of China ; NSFC ; NSFC ; K.C.Wong Education Foundation ; K.C.Wong Education Foundation ; China.Manned Space Project ; China.Manned Space Project ; National Key Research and Development of China ; National Key Research and Development of China ; NSFC ; NSFC ; K.C.Wong Education Foundation ; K.C.Wong Education Foundation ; China.Manned Space Project ; China.Manned Space Project
源URL[http://ir.bao.ac.cn/handle/114a11/87907]  
专题中国科学院国家天文台
通讯作者Guo, Qi
作者单位1.Chinese Acad Sci, Natl Astron Observ, Key Lab Computat Astrophys, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Sch Astron & Space Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 He, Yi,Guo, Qi,Shao, Shi. Predicting Supermassive Black Hole Mass with Machine Learning Methods[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2022,22(8):9.
APA He, Yi,Guo, Qi,&Shao, Shi.(2022).Predicting Supermassive Black Hole Mass with Machine Learning Methods.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,22(8),9.
MLA He, Yi,et al."Predicting Supermassive Black Hole Mass with Machine Learning Methods".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 22.8(2022):9.

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来源：国家天文台

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