KMS National Astronomical Observatories, CAS
| Predicting Supermassive Black Hole Mass with Machine Learning Methods | |
| He, Yi1,2; Guo, Qi1,2; Shao, Shi1 | |
| 2022-08-01 | |
| Source Publication | RESEARCH IN ASTRONOMY AND ASTROPHYSICS
 |
| ISSN | 1674-4527 |
| Volume | 22Issue:8Pages:9 |
| Abstract | 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. |
| Keyword | (galaxies:) quasars: supermassive black holes galaxies: evolution methods: data analysis |
| Funding Organization | 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 |
| DOI | 10.1088/1674-4527/ac777f |
| WOS Keyword | DIGITAL SKY SURVEY ; ACTIVE GALACTIC NUCLEI ; RADIUS-LUMINOSITY RELATIONSHIP ; STAR-FORMING GALAXIES ; DATA RELEASE ; COSMOLOGICAL SIMULATIONS ; ELLIPTIC GALAXIES ; H-ALPHA ; AGN ; COEVOLUTION |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | 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 |
| WOS Research Area | Astronomy & Astrophysics |
| WOS Subject | Astronomy & Astrophysics |
| WOS ID | WOS:000832500200001 |
| Publisher | NATL ASTRONOMICAL OBSERVATORIES, CHIN ACAD SCIENCES |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.bao.ac.cn/handle/114a11/87908 |
| Collection | 中国科学院国家天文台 |
| Corresponding Author | Guo, Qi |
| Affiliation | 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 |
| Recommended Citation 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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