Metallicities of 20 Million Giant Stars Based on Gaia XP Spectra
We design an uncertainty-aware cost-sensitive neural network (UA-CSNet) to estimate metallicities from dereddened and corrected Gaia BP/RP (XP) spectra for giant stars. This method accounts for both stochastic errors in the input spectra and the imbalanced density distribution in [Fe/H] values. With...
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2025-01-01
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| Series: | The Astrophysical Journal Supplement Series |
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| Online Access: | https://doi.org/10.3847/1538-4365/add5e3 |
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| author | Lin Yang Haibo Yuan Bowen Huang Ruoyi Zhang Timothy C. Beers Kai Xiao Shuai Xu Yang Huang Maosheng Xiang Meng Zhang Jinming Zhang |
| author_facet | Lin Yang Haibo Yuan Bowen Huang Ruoyi Zhang Timothy C. Beers Kai Xiao Shuai Xu Yang Huang Maosheng Xiang Meng Zhang Jinming Zhang |
| author_sort | Lin Yang |
| collection | DOAJ |
| description | We design an uncertainty-aware cost-sensitive neural network (UA-CSNet) to estimate metallicities from dereddened and corrected Gaia BP/RP (XP) spectra for giant stars. This method accounts for both stochastic errors in the input spectra and the imbalanced density distribution in [Fe/H] values. With a specialized architecture and training strategy, the UA-CSNet improves the precision of the predicted metallicities, especially for very metal-poor (VMP; [Fe/H] ≤ −2.0) stars. With the PASTEL catalog as the training sample, our model can estimate metallicities down to [Fe/H] ∼ −4. We compare our estimates with a number of external catalogs and conduct tests using star clusters, finding overall good agreement. We also confirm that our estimates for VMP stars are unaffected by carbon enhancement. Applying the UA-CSNet, we obtain reliable and precise metallicity estimates for approximately 20 million giant stars, including 360,000 VMP stars and 50,000 extremely metal-poor ([Fe/H] ≤ −3.0) stars. The resulting catalog is publicly available via the Chinese Virtual Observatory at doi: 10.12149/101604. This work highlights the potential of low-resolution spectra for metallicity estimation and provides a valuable data set for studying the formation and chemodynamical evolution of our Galaxy. |
| format | Article |
| id | doaj-art-479efc66525b4e47af9225618fce6cb4 |
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| issn | 0067-0049 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
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| series | The Astrophysical Journal Supplement Series |
| spelling | doaj-art-479efc66525b4e47af9225618fce6cb42025-08-20T02:36:06ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492025-01-012791710.3847/1538-4365/add5e3Metallicities of 20 Million Giant Stars Based on Gaia XP SpectraLin Yang0https://orcid.org/0000-0002-9824-0461Haibo Yuan1https://orcid.org/0000-0003-2471-2363Bowen Huang2https://orcid.org/0000-0002-1259-0517Ruoyi Zhang3https://orcid.org/0000-0003-1863-1268Timothy C. Beers4https://orcid.org/0000-0003-4573-6233Kai Xiao5https://orcid.org/0000-0001-8424-1079Shuai Xu6https://orcid.org/0000-0003-3535-504XYang Huang7https://orcid.org/0000-0003-3250-2876Maosheng Xiang8https://orcid.org/0000-0002-5818-8769Meng Zhang9https://orcid.org/0000-0001-9293-131XJinming Zhang10https://orcid.org/0009-0005-7743-6229Department of Cyber Security, Beijing Electronic Science and Technology Institute , Beijing, 100070, People’s Republic of China; College of Artificial Intelligence, Beijing Normal University , No. 19 Xinjiekouwai Street, Haidian District, Beijing, 100875, People’s Republic of ChinaInstitute for Frontiers in Astronomy and Astrophysics, Beijing Normal University , Beijing, 102206, People’s Republic of China ; yuanhb@bnu.edu.cn; School of Physics and Astronomy, Beijing Normal University , Beijing, 100875, People’s Republic of ChinaInstitute for Frontiers in Astronomy and Astrophysics, Beijing Normal University , Beijing, 102206, People’s Republic of China ; yuanhb@bnu.edu.cn; School of Physics and Astronomy, Beijing Normal University , Beijing, 100875, People’s Republic of ChinaInstitute for Frontiers in Astronomy and Astrophysics, Beijing Normal University , Beijing, 102206, People’s Republic of China ; yuanhb@bnu.edu.cn; School of Physics and Astronomy, Beijing Normal University , Beijing, 100875, 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) , USASchool of Astronomy and Space Science, University of Chinese Academy of Sciences , Beijing, 100049, People’s Republic of ChinaInstitute for Frontiers in Astronomy and Astrophysics, Beijing Normal University , Beijing, 102206, People’s Republic of China ; yuanhb@bnu.edu.cn; School of Physics and Astronomy, Beijing Normal University , Beijing, 100875, People’s Republic of ChinaInstitute for Frontiers in Astronomy and Astrophysics, Beijing Normal University , Beijing, 102206, People’s Republic of China ; yuanhb@bnu.edu.cn; School of Astronomy and Space Science, University of Chinese Academy of Sciences , Beijing, 100049, People’s Republic of ChinaInstitute for Frontiers in Astronomy and Astrophysics, Beijing Normal University , Beijing, 102206, People’s Republic of China ; yuanhb@bnu.edu.cn; National Astronomical Observatories, Chinese Academy of Sciences , 20A Datun Road, Chaoyang District, Beijing, People’s Republic of ChinaNational Astronomical Observatories, Chinese Academy of Sciences , 20A Datun Road, Chaoyang District, Beijing, People’s Republic of ChinaInstitute for Frontiers in Astronomy and Astrophysics, Beijing Normal University , Beijing, 102206, People’s Republic of China ; yuanhb@bnu.edu.cn; School of Physics and Astronomy, Beijing Normal University , Beijing, 100875, People’s Republic of ChinaWe design an uncertainty-aware cost-sensitive neural network (UA-CSNet) to estimate metallicities from dereddened and corrected Gaia BP/RP (XP) spectra for giant stars. This method accounts for both stochastic errors in the input spectra and the imbalanced density distribution in [Fe/H] values. With a specialized architecture and training strategy, the UA-CSNet improves the precision of the predicted metallicities, especially for very metal-poor (VMP; [Fe/H] ≤ −2.0) stars. With the PASTEL catalog as the training sample, our model can estimate metallicities down to [Fe/H] ∼ −4. We compare our estimates with a number of external catalogs and conduct tests using star clusters, finding overall good agreement. We also confirm that our estimates for VMP stars are unaffected by carbon enhancement. Applying the UA-CSNet, we obtain reliable and precise metallicity estimates for approximately 20 million giant stars, including 360,000 VMP stars and 50,000 extremely metal-poor ([Fe/H] ≤ −3.0) stars. The resulting catalog is publicly available via the Chinese Virtual Observatory at doi: 10.12149/101604. This work highlights the potential of low-resolution spectra for metallicity estimation and provides a valuable data set for studying the formation and chemodynamical evolution of our Galaxy.https://doi.org/10.3847/1538-4365/add5e3Fundamental parameters of starsMetallicityAstronomy data analysisSpectroscopy |
| spellingShingle | Lin Yang Haibo Yuan Bowen Huang Ruoyi Zhang Timothy C. Beers Kai Xiao Shuai Xu Yang Huang Maosheng Xiang Meng Zhang Jinming Zhang Metallicities of 20 Million Giant Stars Based on Gaia XP Spectra The Astrophysical Journal Supplement Series Fundamental parameters of stars Metallicity Astronomy data analysis Spectroscopy |
| title | Metallicities of 20 Million Giant Stars Based on Gaia XP Spectra |
| title_full | Metallicities of 20 Million Giant Stars Based on Gaia XP Spectra |
| title_fullStr | Metallicities of 20 Million Giant Stars Based on Gaia XP Spectra |
| title_full_unstemmed | Metallicities of 20 Million Giant Stars Based on Gaia XP Spectra |
| title_short | Metallicities of 20 Million Giant Stars Based on Gaia XP Spectra |
| title_sort | metallicities of 20 million giant stars based on gaia xp spectra |
| topic | Fundamental parameters of stars Metallicity Astronomy data analysis Spectroscopy |
| url | https://doi.org/10.3847/1538-4365/add5e3 |
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