Yi-Han Song

We present a clean, homogeneous, and AI-ready stellar dataset assembled from LAMOST DR11 low-resolution spectra, tailored for data-driven models. This dataset contains 111,417 high-quality spectra for 91,542 AFGK-type stars, with stellar labels such as T _eff , log g , [Fe/H], [ α /M], stellar mass, and absolute G -band magnitude. It reaches a metallicity of [Fe/H] ∼ −3.8 dex, significantly below the LAMOST pipeline’s lower limit of −2.5 dex, and covers T _eff ≈ 3800–13,000 K, log g ≈ 0.1–5.0 dex, [ α /M] from −0.4 to 0.6 dex, mass from 0.4 to 6.0 M _⊙ , and M _G from −4.0 to 8.0 mag. Self-consistency validation using neural networks confirms the reliability of stellar labels and shows only minor differences between the adopted and network-predicted parameters. By combining this dataset with the 5105 M-type stars from our previous work, we trained a neural network model and applied it to the complete set of LAMOST DR12 AFGKM-type spectra, obtaining stellar parameters for 9 million spectra. The model yields precise estimates that agree well with APOGEE and Gaia estimates, with uncertainties of about 55 K in T _eff , 0.10 dex in log g , 0.07 dex in [Fe/H], 0.02 dex in [ α /M], a relative error of about 4.2% in stellar mass, and 0.13 mag in M _G . Distances inferred from M _G show a relative error of 7.4%. This work delivers a public, AI-ready dataset and a value-added catalog for LAMOST DR12, extends investigations of very metal-poor stars toward lower metallicities, and supports direct, efficient estimation of stellar parameters, mass, and distance from large spectroscopic surveys.

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has collected tens of millions of spectra, providing an unprecedented resource for large-scale spectroscopic studies. Efficient retrieval techniques are therefore essential for exploring such massive datasets. Existing approaches often rely on predefined templates or manually labeled training samples, which can limit their applicability in large and diverse spectral archives. In this work, we present a general similarity-retrieval framework that combines self-supervised contrastive learning based on a convolutional neural network with Facebook AI Similarity Search (FAISS) for efficient large-scale spectral retrieval. The framework learns spectral representations directly from unlabeled data and enables flexible retrieval from user-defined wavelength regions based on feature similarity. We evaluate the framework on several stellar populations in LAMOST DR8. For late-type M8-star retrieval, 90.5% of the top 1000 retrieved spectra are later than M6. For M0–M5 giants, the mean retrieval accuracy across six subtypes reaches 94.8%. Using a C-H star spectrum as the query spectrum, 90.8% of the top 1000 retrieved candidates are classified as carbon stars by the LAMOST pipeline. Cross-matching with SIMBAD further confirms 255 C-H stars and 47 C-R stars among the retrieved candidates. These results demonstrate that the proposed framework can efficiently identify spectrally similar objects across large spectroscopic databases and can serve as a useful tool for searching for rare or spectrally distinctive stellar populations.

We present the first public data release of the DDO51 band from the Stellar Abundances and Galactic Evolution Survey (SAGES), based on Nanshan One-meter Wide-field Telescope observations obtained between 2023 September and 2024 January. This release initiates the DDO51-band component of the survey, covering ∼2500 deg ² of the northern sky and including more than 10 million sources. The DDO51 filter is centered near the Mg i b triplet and the adjacent MgH feature, offering sensitivity to stellar surface gravity. The data reduction pipeline incorporates an improved astrometric solution anchored to Gaia DR3 and a photometric calibration strategy tied to synthetic photometry from Gaia XP spectra. These procedures yield a point-source depth of ∼18.9 mag at a signal-to-noise ratio ∼10 and an internal photometric precision ≈6–7 mmag at the bright end. A preliminary color–color analysis using Gaia broadband photometry confirms the expected sensitivity of the DDO51 band to stellar surface gravity, demonstrating a clear photometric separation between dwarf and giant sequences for late-type stars. This dataset, when combined with existing SAGES photometry in other bands, provides a crucial tool for disentangling the substructures of the Milky Way. All data products from this release are available upon publication.

Reliable estimation of stellar surface gravity (log g ) for a large sample is crucial for evaluating stellar evolution models and understanding galactic structure; however, it is not easy to accomplish due to the difficulty in gathering a large spectroscopic data set. A photometric sky survey using a specific filter, on the other hand, can play a substantial role in the assessment of log g . The Stellar Abundances and Galactic Evolution Survey (SAGES) utilizes eight filters to provide accurate stellar parameters for ∼10 ⁷ stars, with its DDO51 intermediate-band filter specifically designed for robust log g determination. In this work, the observed SAGES u _SC and v _SAGES photometry and the synthetic photometry in the g , r , i , and DDO51 bands derived from Gaia XP spectra are employed to investigate the importance of the DDO51 filter in the determination of log g . We applied machine learning-based extinction correction and employed XGBoost models, trained on stellar parameters from Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), to predict log g using photometric data. By comparing the model-predicted log g with LAMOST values, we find that including the DDO51 filter improves the accuracy of log g estimates by 21.0% (from 0.224 to 0.177 dex) overall and by 26.5% (from 0.302 to 0.222 dex) for G- and K-type stars as compared to those obtained without DDO51. The DDO51 filter is also validated to be particularly effective for metal-poor stars ([Fe/H] < −1.0), where it significantly mitigates systematic biases. Our findings highlight the diagnostic power of the SAGES DDO51 filter, providing enhanced stellar characterization vital for future in-depth studies of the Milky Way.

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 Hα, this versatile machine learning approach is poised to play an important role in upcoming surveys featuring expanded filter sets.

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], logg, and Teff, based on a combination of medium-band and broad-band photometric observations. Our analysis employs data primarily sourced from the SAGE Survey , 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 logg, and 70 K for Teff. Furthermore, by incorporating 2MASS and WISE infrared photometric and GALEX 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 dataset for research on metal-poor stars, the structure of the Milky Way, and beyond. With the forthcoming release of additional bands from SAGE Survey such DDO51 and H-alpha, this versatile machine learning approach is poised to play an important role in upcoming surveys featuring expanded filter sets

The discrepancies between theoretical and observed spectra, and the systematic differences between various spectroscopic parameter estimates, complicate the determination of atmospheric parameters of M-type stars. In this work, we present an empirical sample of 5105 M-type star spectra with homogeneous atmospheric parameter labels through stellar-label transfer and sample cleaning. We addressed systematic discrepancies in spectroscopic parameter estimates by adopting recent results for Gaia EDR3 stars as a reference standard. Then, we used a density-based spatial clustering of applications with noise to remove unreliable samples in each subgrid of parameters. To confirm the reliability of the stellar labels, a five-layer neural network was utilized, randomly partitioning the samples into training and testing sets. The standard deviations between the predicted and actual values in the testing set are 14 K for T _eff, 0.06 dex for logg, and 0.05 dex for [M/H], respectively. In addition, we conducted an internal cross validation to enhance validation and obtained precisions of 11 K, 0.05 dex, and 0.05 dex for T _eff, logg, and [M/H], respectively. A grid of 1365 high-signal-to-noise ratio (S/N) spectra and their labels, selected from the empirical sample, was utilized in the stellar parameter pipeline for M-type stars of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), producing an almost seamless Kiel distribution diagram for LAMOST DR10 and DR11 data. The atmospheric parameters for M-type stars from LAMOST DR11 show improved precision compared to the data from DR9, with improvements (for spectra with S/N higher than 10) from 118 to 67 K in T _eff, 0.2 to 0.07 dex in logg, and 0.29 to 0.14 dex in [M/H].

The discrepancies between theoretical and observed spectra, and the systematic differences between various spectroscopic parameter estimates, complicate the determination of atmospheric parameters of M-type stars. In this work, we present an empirical sample of 5105 M-type star spectra with homogeneous atmospheric parameter labels through stellar-label transfer and sample cleaning. We addressed systematic discrepancies in spectroscopic parameter estimates by adopting recent results for Gaia EDR3 stars as a reference standard. Then, we used a density-based spatial clustering of applications with noise to remove unreliable samples in each subgrid of parameters. To confirm the reliability of the stellar labels, a 5-layer neural network was utilized, randomly partitioning the samples into training and testing sets. The standard deviations between the predicted and actual values in the testing set are 14 K for Teff , 0.06 dex for log g, and 0.05 dex for [M/H], respectively. In addition, we conducted an internal cross-validation to enhance validation and obtained precisions of 11 K, 0.05 dex, and 0.05 dex for Teff , log g, and [M/H], respectively. A grid of 1365 high Signal-to-Noise ratio (S/N) spectra and their labels, selected from the empirical sample, was utilized in the stellar parameter pipeline for M-Type stars (LASPM) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), producing an almost seamless Kiel distribution diagram for LAMOST DR10 and DR11 data. The atmospheric parameters for M-type stars from LAMOST DR11 show improved precision compared to the data from DR9, with improvements (for spectra with S/N higher than 10) from 118 to 67 K in Teff , 0.2 to 0.07 dex in log g, and 0.29 to 0.14 dex in [M/H].