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The accuracy of the estimated stellar atmospheric parameter decreasesevidently with the decreasing of spectral signal-to-noise ratio (SNR) and thereare a huge amount of this kind observations, especially in case of SNR$<$30.Therefore, it is helpful to improve the parameter estimation performance forthese spectra and this work studied the ($T_\texttt{eff}, \log~g$, [Fe/H])estimation problem for LAMOST DR8 low-resolution spectra with 20$\leq$SNR$<$30.We proposed a data-driven method based on machine learning techniques. Firstly,this scheme detected stellar atmospheric parameter-sensitive features fromspectra by the Least Absolute Shrinkage and Selection Operator (LASSO),rejected ineffective data components and irrelevant data. Secondly, aMulti-layer Perceptron (MLP) method was used to estimate stellar atmosphericparameters from the LASSO features. Finally, the performance of the LASSO-MLPwas evaluated by computing and analyzing the consistency between its estimationand the reference from the APOGEE (Apache Point Observatory Galactic EvolutionExperiment) high-resolution spectra. Experiments show that the Mean AbsoluteErrors (MAE) of $T_\texttt{eff}, \log~g$, [Fe/H] are reduced from the LASP(137.6 K, 0.195 dex, 0.091 dex) to LASSO-MLP (84.32 K, 0.137 dex, 0.063 dex),which indicate evident improvements on stellar atmospheric parameterestimation. In addition, this work estimated the stellar atmospheric parametersfor 1,162,760 low-resolution spectra with 20$\leq$SNR$<$30 from LAMOST DR8using LASSO-MLP, and released the estimation catalog, learned model,experimental code, trained model, training data and test data for scientificexploration and algorithm study.

Estimation of stellar atmospheric parameters from LAMOST DR8 low-resolution spectra with 20$\leq$SNR$<$30