Photometric Metallicities of 0.8 Million KiDS Stars

Abstract Accurate determinations of metallicity for large, complete stellar samples are essential for advancing various studies of the Milky Way. In this paper, we present a data-driven algorithm that leverages photometric data from the KiDS and the VIKING surveys to estimate stellar absolute magnitudes, effective temperatures, and metallicities. The algorithm is trained and validated using spectroscopic data from LAMOST, SEGUE, APOGEE, and GALAH, as well as a catalog of very metal-poor stars from the literature, and Gaia EDR3 data. This approach enables us to estimate metallicities, effective temperatures, and g -band absolute magnitudes for approximately 0.8 million stars in the KiDS data set. The photometric metallicity estimates exhibit an uncertainty of around 0.28 dex when compared to spectroscopic studies, within the metallicity range of −2 dex to 0.5 dex. The photometric effective temperature estimates have an uncertainty of around 149 K, while the uncertainty in the absolute magnitudes is approximately 0.36 mag. The metallicity estimates are reliable for values down to about −2 dex. This catalog represents a valuable resource for studying the structure and chemical properties of the Milky Way, offering an extensive data set for future investigations into Galactic formation and evolution.

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Publication Details

Journal
Research in Astronomy and Astrophysics
Published
2025-02-12
DOI
https://doi.org/10.1088/1674-4527/adb55b
Citations
3
Primary Topic
Stellar, planetary, and galactic studies
Type
article
Field-Weighted Citation Impact
3.46

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article

Photometric Metallicities of 0.8 Million KiDS Stars

Bingqiu Chen, Bо Sun, Xiaowei Liu
3 citations
Research in Astronomy and Astrophysics
Stellar, planetary, and galactic studies
3.46
article

Photometric Metallicities of 0.8 Million KiDS Stars

Bingqiu Chen, Bо Sun, Xiaowei Liu
article en
3 citations

Abstract

Abstract Accurate determinations of metallicity for large, complete stellar samples are essential for advancing various studies of the Milky Way. In this paper, we present a data-driven algorithm that leverages photometric data from the KiDS and the VIKING surveys to estimate stellar absolute magnitudes, effective temperatures, and metallicities. The algorithm is trained and validated using spectroscopic data from LAMOST, SEGUE, APOGEE, and GALAH, as well as a catalog of very metal-poor stars from the literature, and Gaia EDR3 data. This approach enables us to estimate metallicities, effective temperatures, and g -band absolute magnitudes for approximately 0.8 million stars in the KiDS data set. The photometric metallicity estimates exhibit an uncertainty of around 0.28 dex when compared to spectroscopic studies, within the metallicity range of −2 dex to 0.5 dex. The photometric effective temperature estimates have an uncertainty of around 149 K, while the uncertainty in the absolute magnitudes is approximately 0.36 mag. The metallicity estimates are reliable for values down to about −2 dex. This catalog represents a valuable resource for studying the structure and chemical properties of the Milky Way, offering an extensive data set for future investigations into Galactic formation and evolution.

Research in Astronomy and AstrophysicsVol. 25(3)
Yunnan University (CN)
U.S. Department of Energy, Alfred P. Sloan Foundation, University of Utah, European Space Agency, Deutsche Forschungsgemeinschaft, National Natural Science Foundation of China, Chinese Academy of Sciences, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Università degli Studi di Padova, Natural Science Foundation of Yunnan Province, National Development and Reform Commission, Office of Science, Australian Astronomical Optics-Macquarie, National Astronomical Observatories, Chinese Academy of Sciences
Openalex Percentile: Top 9%
Stellar, planetary, and galactic studies
3.46
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