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Peer-reviewedCC BY 4.0The Astrophysical Journal Supplement Series

Model-driven Stellar Spectroscopy with the Total Differential Spectral Model ( TDSM ): Framework and Its Application to 11 Million LAMOST Low-resolution Spectra

  • Lanya Mou1,2
  • Maosheng Xiang1,3
  • Haibo Yuan3
  • Tianmin Wu1,2
  • Meng Zhang1,4
4 affiliations
  1. 1Chinese Academy of Sciences
  2. 2University of Chinese Academy of Sciences
  3. 3Beijing Normal University
  4. 4Max-Planck-Institut für Astronomie
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Abstract

Deriving stellar astrophysical parameters from large-scale spectroscopic surveys requires accurate modeling of spectra across a wide wavelength coverage and a broad range of stellar parameter space, which remains a great challenge. In this work, we present the Total Differential Spectral Model ( TDSM ), a physically motivated neural network architecture that self-consistently emulates both the flux and its first-order derivatives of the ab initio stellar spectra. The method enables accurate and precise emulation of Kurucz synthetic spectra with severely blended features at R ≃ 2000 in a high-dimensional parameter space, achieving a typical pixel-wise flux precision of 0.05% in the wavelength range 3700 < λ < 9000 Å over the parameter ranges of 3000 < T eff < 45,000 K, − 1 < log g < 5 , and −4.0 < [Fe/H] < 0.5. The model is applied to LAMOST DR10 to derive stellar parameters for 11 million spectra spanning the full model parameter range. We conduct detailed refinement and calibration efforts to mitigate systematic errors, which are revealed to be significant and complex due to imperfections in the ab initio model atmosphere. The resultant catalog provides eight stellar labels— T eff , log g , v mic , [Fe/H], [ α /Fe], [C/Fe], [N/Fe], and [Mg/Fe]—for eight million unique stars (11 million spectra including repeat observations) across the full parameter space. This adds a valuable dataset for studies of Galactic structure and archaeology, stellar populations, and related areas of astrophysics.

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Cite

BibTeXRIS
BibTeX
@article{mou2026modeldriven,
  title = {{Model-driven Stellar Spectroscopy with the Total Differential Spectral Model ( TDSM ): Framework and Its Application to 11 Million LAMOST Low-resolution Spectra}},
  author = {Lanya Mou and Maosheng Xiang and Haibo Yuan and Tianmin Wu and Meng Zhang},
  journal = {The Astrophysical Journal Supplement Series},
  year = {2026},
  month = oct,
  doi = {10.3847/1538-4365/aeab87},
  url = {https://iopscience.iop.org/article/10.3847/1538-4365/aeab87},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Model-driven Stellar Spectroscopy with the Total Differential Spectral Model ( TDSM ): Framework and Its Application to 11 Million LAMOST Low-resolution Spectra
AU  - Lanya Mou
AU  - Maosheng Xiang
AU  - Haibo Yuan
AU  - Tianmin Wu
AU  - Meng Zhang
PY  - 2026
DA  - 2026/10/28
JO  - The Astrophysical Journal Supplement Series
DO  - 10.3847/1538-4365/aeab87
UR  - https://iopscience.iop.org/article/10.3847/1538-4365/aeab87
AB  - Deriving stellar astrophysical parameters from large-scale spectroscopic surveys requires accurate modeling of spectra across a wide wavelength coverage and a broad range of stellar parameter space, which remains a great challenge. In this work, we present the Total Differential Spectral Model ( TDSM ), a physically motivated neural network architecture that self-consistently emulates both the flux and its first-order derivatives of the ab initio stellar spectra. The method enables accurate and precise emulation of Kurucz synthetic spectra with severely blended features at R ≃ 2000 in a high-dimensional parameter space, achieving a typical pixel-wise flux precision of 0.05% in the wavelength range 3700 < λ < 9000 Å over the parameter ranges of 3000 < T eff < 45,000 K, − 1 < log g < 5 , and −4.0 < [Fe/H] < 0.5. The model is applied to LAMOST DR10 to derive stellar parameters for 11 million spectra spanning the full model parameter range. We conduct detailed refinement and calibration efforts to mitigate systematic errors, which are revealed to be significant and complex due to imperfections in the ab initio model atmosphere. The resultant catalog provides eight stellar labels— T eff , log g , v mic , [Fe/H], [ α /Fe], [C/Fe], [N/Fe], and [Mg/Fe]—for eight million unique stars (11 million spectra including repeat observations) across the full parameter space. This adds a valuable dataset for studies of Galactic structure and archaeology, stellar populations, and related areas of astrophysics.
SN  - 0067-0049
SN  - 1538-4365
ER  -