Yan-Xin Guo

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 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].

In Data Release 9 of LAMOST, we present measurements of v sin i for a total of 121,698 stars measured using the Medium Resolution Spectrograph (MRS) and 80,108 stars using the Low Resolution Spectrograph (LRS). These values were obtained through a χ ² minimization process, comparing LAMOST spectra with corresponding grids of synthetically broadened spectra. Due to the resolution and the spectral range of LAMOST, v sin i measurements are limited to stars with an effective temperature (T _eff) ranging from 5000 to 8500 K for MRS and 7000 to 9000 K for LRS. The detectable v sin i for MRS is set between 27 and 350 km s⁻¹, and for LRS between 110 and 350 km s⁻¹. This limitation is because the convolved reference spectra become less informative beyond 350 km s⁻¹. The intrinsic precision of v sin i, determined from multiepoch observations, is approximately ∼4.0 km s⁻¹ for MRS and ∼10.0 km s⁻¹ for LRS at a signal-to-noise ratio greater than 50. Our v sin i values show consistency with those from APOGEE17, displaying a scatter of 8.79 km s⁻¹. They are also in agreement with measurements from the Gaia DR3 and Sun et al. catalogs. An observed trend in LAMOST MRS data is the decrease in v sin i with a drop in T _eff, particularly transiting around 7000 K for dwarfs and 6500 K for giants, primarily observed in stars with near-solar abundances.

In Data Release 9 of LAMOST, we present measurements of v sin i for a total of 121,698 stars measured using the Medium Resolution Spectrograph (MRS) and 80,108 stars using the Low Resolution Spectrograph (LRS). These values were obtained through a chi^2 minimisation process, comparing LAMOST spectra with corresponding grids of synthetically broadened spectra. Due to the resolution and the spectral range of LAMOST, v sin i measurements are limited to stars with effective temperature (Teff) ranging from 5000 K to 8500 K for MRS and 7000 K to 9000 K for LRS. The detectable v sin i for MRS is set between 27 km/s and 350 km/s , and for LRS between 110 km/s and 350 km/s, This limitation is because the convolved reference spectra become less informative beyond 350 km/s. The intrinsic precisions of v sin i , determined from multi-epoch observations, is approximately 4.0 km/s for MRS and 10.0 km/s for LRS at signal-to-noise ratio (S/N) greater than 50. Our v sin i values show consistence with those from APOGEE17, displaying a scatter of 8.79 km/s. They are also in agreement with measurements from the Gaia DR3 and SUN catalogs. An observed trend in LAMOST MRS data is the decrease in v sin i with dropping Teff, particularly transiting around 7000 K for dwarfs and 6500 K for giants, primarily observed in stars with near-solar abundances.