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April 21, 2026The Astrophysical Journal Supplement SeriesOpen Access

A Catalog of High-confidence Very Metal-poor Stars from DESI Based on a Deep Learning Method

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Authors

CYC. H. YangG林Guanhong 观泓 Lin 林L谈Lei 磊 Tan 谈

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Overview

A novel deep learning framework identifies very metal-poor stars, highlighting significant findings for Galactic studies.

Key Points

  • The aim is to identify very metal-poor stars effectively from large datasets generated by DESI.
  • Developed a dual-model deep learning framework combining 1D-ResNet classifier and regression model.
  • Utilized transfer learning with high-quality labels from APOGEE and Large Sky Area Telescope.
  • Optimized classification for DESI spectra with a specific temperature cut to ensure accuracy.
  • Achieved a classification accuracy of 97.87% and an rms error of 0.093 dex in metallicity predictions.
  • Constructed a catalog of 2569 high-confidence VMP candidates, including 1377 new contenders.
  • Validated the reliability of metallicity estimates through internal consistency checks and external cross-matching.

Cite This Study

Yang et al. (2026) studied this question.

synapsesocial.com/papers/69e7132bcb99343efc98cf3ehttps://doi.org/10.3847/1538-4365/ae578a
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