The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning–based Stellar Parameters for 21 Million Stars from the First Data Release
Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar parameters. In this study, we present a machine learning appro...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
|
| Series: | The Astrophysical Journal Supplement Series |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/1538-4365/adae86 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850189552826712064 |
|---|---|
| author | Hongrui Gu Zhou Fan Gang Zhao Huang Yang Timothy C. Beers Wei Wang Jie Zheng Jingkun Zhao Chun Li Yuqin Chen Haibo Yuan Haining Li Kefeng Tan Yihan Song Ali Luo Nan Song Yujuan Liu |
| author_facet | Hongrui Gu Zhou Fan Gang Zhao Huang Yang Timothy C. Beers Wei Wang Jie Zheng Jingkun Zhao Chun Li Yuqin Chen Haibo Yuan Haining Li Kefeng Tan Yihan Song Ali Luo Nan Song Yujuan Liu |
| author_sort | Hongrui Gu |
| collection | DOAJ |
| description | Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar parameters. In this study, we present a machine learning approach to derive estimates of stellar parameters, including [Fe/H], log g , and T _eff , based on a combination of medium-band and broadband photometric observations. Our analysis employs data primarily sourced from the Stellar Abundances and Galactic Evolution Survey (SAGES), which aims to observe much of the Northern Hemisphere. We combine the uv -band data from SAGES DR1 with photometric and astrometric data from Gaia EDR3, and apply the random forest method to estimate stellar parameters for approximately 21 million stars. We are able to obtain precisions of 0.09 dex for [Fe/H], 0.12 dex for log g , and 70 K for T _eff . Furthermore, by incorporating Two Micron All Sky Survey and Wide-field Infrared Survey Explorer infrared photometric and Galaxy Evolution Explorer ultraviolet data, we are able to achieve even higher precision estimates for over 2.2 million stars. These results are applicable to both giant and dwarf stars. Building upon this mapping, we construct a foundational data set for research on metal-poor stars, the structure of the Milky Way, and beyond. With the forthcoming release of additional bands from SAGES such DDO51 and H α , this versatile machine learning approach is poised to play an important role in upcoming surveys featuring expanded filter sets. |
| format | Article |
| id | doaj-art-5ea7aeb5a0a04b83ad269be3be452ede |
| institution | OA Journals |
| issn | 0067-0049 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Supplement Series |
| spelling | doaj-art-5ea7aeb5a0a04b83ad269be3be452ede2025-08-20T02:15:34ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492025-01-0127711910.3847/1538-4365/adae86The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning–based Stellar Parameters for 21 Million Stars from the First Data ReleaseHongrui Gu0https://orcid.org/0009-0007-5610-6495Zhou Fan1https://orcid.org/0000-0003-3067-3540Gang Zhao2https://orcid.org/0000-0002-8980-945XHuang Yang3https://orcid.org/0000-0003-3250-2876Timothy C. Beers4https://orcid.org/0000-0003-4573-6233Wei Wang5https://orcid.org/0000-0002-9702-4441Jie Zheng6https://orcid.org/0000-0001-6637-6973Jingkun Zhao7https://orcid.org/0000-0003-2868-8276Chun Li8https://orcid.org/0009-0000-4835-7525Yuqin Chen9https://orcid.org/0000-0002-8442-901XHaibo Yuan10https://orcid.org/0000-0003-2471-2363Haining Li11https://orcid.org/0000-0002-0389-9264Kefeng Tan12https://orcid.org/0000-0003-0173-6397Yihan Song13https://orcid.org/0000-0001-7255-5003Ali Luo14https://orcid.org/0000-0001-7865-2648Nan Song15Yujuan Liu16CAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences , Beijing 100101, People’s Republic of China ; zfan@nao.cas.cn; School of Astronomy and Space Science, University of Chinese Academy of Sciences , Beijing, People’s Republic of ChinaCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences , Beijing 100101, People’s Republic of China ; zfan@nao.cas.cn; School of Astronomy and Space Science, University of Chinese Academy of Sciences , Beijing, People’s Republic of ChinaCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences , Beijing 100101, People’s Republic of China ; zfan@nao.cas.cn; School of Astronomy and Space Science, University of Chinese Academy of Sciences , Beijing, People’s Republic of ChinaCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences , Beijing 100101, People’s Republic of China ; zfan@nao.cas.cn; School of Astronomy and Space Science, University of Chinese Academy of Sciences , Beijing, People’s Republic of ChinaDepartment of Physics and Astronomy, University of Notre Dame , Notre Dame, IN 46556, USA; Joint Institute for Nuclear Astrophysics—Center for the Evolution of the Elements (JINA-CEE) , USACAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences , Beijing 100101, People’s Republic of China ; zfan@nao.cas.cnCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences , Beijing 100101, People’s Republic of China ; zfan@nao.cas.cnCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences , Beijing 100101, People’s Republic of China ; zfan@nao.cas.cnCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences , Beijing 100101, People’s Republic of China ; zfan@nao.cas.cnCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences , Beijing 100101, People’s Republic of China ; zfan@nao.cas.cnDepartment of Astronomy, Beijing Normal University , Beijing 100875, People’s Republic of ChinaCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences , Beijing 100101, People’s Republic of China ; zfan@nao.cas.cnCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences , Beijing 100101, People’s Republic of China ; zfan@nao.cas.cnCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences , Beijing 100101, People’s Republic of China ; zfan@nao.cas.cnCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences , Beijing 100101, People’s Republic of China ; zfan@nao.cas.cnChina Science and Technology Museum , Beijing 100101, People’s Republic of ChinaCAS Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Sciences , Beijing 100101, People’s Republic of China ; zfan@nao.cas.cnStellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar parameters. In this study, we present a machine learning approach to derive estimates of stellar parameters, including [Fe/H], log g , and T _eff , based on a combination of medium-band and broadband photometric observations. Our analysis employs data primarily sourced from the Stellar Abundances and Galactic Evolution Survey (SAGES), which aims to observe much of the Northern Hemisphere. We combine the uv -band data from SAGES DR1 with photometric and astrometric data from Gaia EDR3, and apply the random forest method to estimate stellar parameters for approximately 21 million stars. We are able to obtain precisions of 0.09 dex for [Fe/H], 0.12 dex for log g , and 70 K for T _eff . Furthermore, by incorporating Two Micron All Sky Survey and Wide-field Infrared Survey Explorer infrared photometric and Galaxy Evolution Explorer ultraviolet data, we are able to achieve even higher precision estimates for over 2.2 million stars. These results are applicable to both giant and dwarf stars. Building upon this mapping, we construct a foundational data set for research on metal-poor stars, the structure of the Milky Way, and beyond. With the forthcoming release of additional bands from SAGES such DDO51 and H α , this versatile machine learning approach is poised to play an important role in upcoming surveys featuring expanded filter sets.https://doi.org/10.3847/1538-4365/adae86Stellar abundancesFundamental parameters of starsAstronomy data analysisPhotometry |
| spellingShingle | Hongrui Gu Zhou Fan Gang Zhao Huang Yang Timothy C. Beers Wei Wang Jie Zheng Jingkun Zhao Chun Li Yuqin Chen Haibo Yuan Haining Li Kefeng Tan Yihan Song Ali Luo Nan Song Yujuan Liu The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning–based Stellar Parameters for 21 Million Stars from the First Data Release The Astrophysical Journal Supplement Series Stellar abundances Fundamental parameters of stars Astronomy data analysis Photometry |
| title | The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning–based Stellar Parameters for 21 Million Stars from the First Data Release |
| title_full | The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning–based Stellar Parameters for 21 Million Stars from the First Data Release |
| title_fullStr | The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning–based Stellar Parameters for 21 Million Stars from the First Data Release |
| title_full_unstemmed | The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning–based Stellar Parameters for 21 Million Stars from the First Data Release |
| title_short | The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning–based Stellar Parameters for 21 Million Stars from the First Data Release |
| title_sort | stellar abundances and galactic evolution survey sages ii machine learning based stellar parameters for 21 million stars from the first data release |
| topic | Stellar abundances Fundamental parameters of stars Astronomy data analysis Photometry |
| url | https://doi.org/10.3847/1538-4365/adae86 |
| work_keys_str_mv | AT hongruigu thestellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT zhoufan thestellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT gangzhao thestellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT huangyang thestellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT timothycbeers thestellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT weiwang thestellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT jiezheng thestellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT jingkunzhao thestellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT chunli thestellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT yuqinchen thestellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT haiboyuan thestellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT hainingli thestellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT kefengtan thestellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT yihansong thestellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT aliluo thestellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT nansong thestellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT yujuanliu thestellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT hongruigu stellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT zhoufan stellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT gangzhao stellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT huangyang stellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT timothycbeers stellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT weiwang stellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT jiezheng stellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT jingkunzhao stellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT chunli stellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT yuqinchen stellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT haiboyuan stellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT hainingli stellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT kefengtan stellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT yihansong stellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT aliluo stellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT nansong stellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease AT yujuanliu stellarabundancesandgalacticevolutionsurveysagesiimachinelearningbasedstellarparametersfor21millionstarsfromthefirstdatarelease |