The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has acquired tens of millions of low-resolution spectra of stars. This paper investigated the parameter estimation problem for these spectra. To this end, we proposed a deep learning model StarGRU network (StarGRUNet). This network was further applied to estimate the stellar atmospheric physical parameters and 13 elemental abundances from LAMOST low-resolution spectra. On the spectra with signal-to-noise ratios greater than or equal to $5$, the estimation precisions are $94$ K and $0.16$ dex on $T_\texttt{eff}$ and $\log \ g$ respectively, $0.07$ dex to $0.10$ dex on [C/H], [Mg/H], [Al/H], [Si/H], [Ca/H], [Ni/H] and [Fe/H], and $0.10$ dex to $0.16$ dex on [O/H], [S/H], [K/H], [Ti/H] and [Mn/H], and $0.18$ dex and $0.22$ dex on [N/H] and [Cr/H] respectively. The model shows advantages over available models and high consistency with high-resolution surveys. We released the estimated catalog computed from about 8.21 million low-resolution spectra in LAMOST DR8, code, trained model, and experimental data for astronomical science exploration and data processing algorithm research respectively.

/pdf/estimating-stellar-parameters-from-lamost-low-resolution-23gydv15.pdf

Estimating Stellar Parameters from LAMOST Low-resolution Spectra

Stellar parameters are estimated through spectra and are crucial in studying both stellar evolution and the history of the galaxy. To extract features from the spectra efficiently, we present ESNet (encoder selection network for spectra), a novel architecture that incorporates three essential modules: a feature encoder (FE), feature selection (FS), and feature mapping (FM). FE is responsible for extracting advanced spectral features through encoding. The role of FS, on the other hand, is to acquire compressed features by reducing the spectral dimension and eliminating redundant information. FM comes into play by fusing the advanced and compressed features, establishing a nonlinear mapping between spectra and stellar parameters. The stellar spectra used for training and testing are obtained through crossing LAMOST and SDSS. The experimental results demonstrate that for low signal-to-noise spectra (0–10), ESNet achieves excellent performance on the test set, with mean absolute error (MAE) values of 82 K for Teff (effective temperature), 0.20 dex for logg (logarithm of the gravity), and 0.10 dex for [Fe/H] (metallicity). The results indeed indicate that ESNet has an excellent ability to extract spectral features. Furthermore, this paper validates the consistency between ESNet predictions and the SDSS catalog. The experimental results prove that the model can be employed for the evaluation of stellar parameters.

ESNet: Estimating Stellar Parameters from LAMOST Low-Resolution Stellar Spectra

We present the discovery of 2MASS J05241392 − 0336543 (hereafter J0524 − 0336), a very metal-poor ([Fe/H]= − 2 . 43 ± 0 . 16), highly r -process-enhanced ([Eu/Fe]= +1 . 34 ± 0 . 10) Milky Way halo ﬁeld red giant star, with an ultra high Li abundance of A(Li)(3D,NLTE)= 5 . 62 ± 0 . 25 and [Li/Fe]= +7 . 00 ± 0 . 25, respectively. This makes J0524 − 0336 the most lithium-enhanced giant star discovered to date. We present a detailed analysis of the star’s atmospheric stellar parameters and chemical-abundance determinations. Additionally, we detect infrared excess and variable emission in the wings of the H α absorption line across multiple epochs, indicative of a potential enhanced mass-loss event with possible outﬂows. Our analysis reveals that J0524 − 0336 lies either between the bump and the tip of the Red Giant Branch (RGB), or on the early-Asymptotic Giant Branch (e-AGB). We investigate the possible sources of lithium enrichment in J0524 − 0336, including both internal and external sources. Based on current models and on the observational evidence we have collected, our study shows that J0524 − 0336 may be undergoing the so-called lithium ﬂash that is expected to occur in low-mass stars when they reach the RGB bump and/or the early-AGB.

Discovery of an Ultra Lithium-rich Metal-Poor Red Giant star

Abstract We present the discovery of 2MASS J05241392−0336543 (hereafter J0524−0336), a very metal-poor ([Fe/H] = −2.43 ± 0.16), highly r -process-enhanced ([Eu/Fe] = +1.34 ± 0.10) Milky Way halo field red giant star, with an ultrahigh Li abundance of A (Li, 3D, NLTE) = 6.15 ± 0.25 and [Li/Fe] = +7.64 ± 0.25, respectively. This makes J0524−0336 the most lithium-enhanced giant star discovered to date. We present a detailed analysis of the star’s atmospheric stellar parameters and chemical abundance determinations. Additionally, we detect indications of infrared excess, as well as observe variable emission in the wings of the H α absorption line across multiple epochs, indicative of a potential enhanced mass-loss event with possible outflows. Our analysis reveals that J0524−0336 lies either between the bump and the tip of the red giant branch (RGB), or on the early asymptotic giant branch (e-AGB). We investigate the possible sources of lithium enrichment in J0524−0336, including both internal and external sources. Based on current models and on the observational evidence we have collected, our study shows that J0524−0336 may be undergoing the so-called lithium flash that is expected to occur in low-mass stars when they reach the RGB bump and/or the e-AGB. 

Discovery of a Metal-poor Red Giant Star with the Highest Ultralithium Enhancement

Abstract Barium stars are peculiar stars with enhanced slow neutron capture process ( s -process) elements. Abundance analysis of them aids in better understanding the chemical evolution of the Milky Way. In this paper, we introduce a data-driven method named the memory-enhanced adaptive spectral network (MEASNet) to search for barium candidates in the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) low-resolution survey (LRS) and estimate the abundance of five s -process elements: Sr, Y, Ba, Ce, and Nd. MEASNet, trained using spectra from common stars in both LAMOST and the Galactic Archaeology with HERMES survey, showcases notable performance: for the classification task, precision = 98.22% and recall = 94.12%; in prediction, the mean absolute error for the seven elements range between 0.07 and 0.15 dex. After training, we apply the model to 4,083,003 stellar spectra from LAMOST DR10 LRS, successfully identifying 1,803,670 spectra of barium candidates ([Ba/Fe] ≥ 0.25 dex) along with their five s -process elemental abundances. The catalog enlarges the sample size, providing a wealth of data for further statistical analysis of the formation and evolution of barium stars. Meanwhile, this work highlights the potential value of MEASNet in star classification and abundance estimation, offering a strong reference for future data-driven models. 

MEASNet. I. A Model for Barium Star Identification and s-process Abundance Estimation from LAMOST DR10 Low-resolution Survey

A small fraction of giants possess photospheric lithium (Li) abundance higher than the value predicted by the standard stellar evolution models, and the detailed mechanisms of Li enhancement are complicated and lack a definite conclusion. In order to better understand the Li enhancement behaviors, a large and homogeneous Li-rich giant sample is needed. In this study, we designed a modified convolutional neural network model called Coord-DenseNet to determine the A(Li) of Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) low-resolution survey (LRS) giant spectra. The precision is good on the test set: MAE = 0.15 dex, and σ = 0.21 dex. We used this model to predict the Li abundance of more than 900,000 LAMOST DR8 LRS giant spectra and identified 7768 Li-rich giants with Li abundances ranging from 2.0 to 5.4 dex, accounting for about 1.02% of all giants. We compared the Li abundance estimated by our work with those derived from high-resolution spectra. We found that the consistency was good if the overall deviation of 0.27 dex between them was not considered. The analysis shows that the difference is mainly due to the high A(Li) from the medium-resolution spectra in the training set. This sample of Li-rich giants dramatically expands the existing sample size of Li-rich giants and provides us with more samples to further study the formation and evolution of Li-rich giants.

Li-rich Giants Identified from LAMOST DR8 Low-resolution Survey

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Li-rich Giants Identified from LAMOST DR8 Low-resolution Survey