LAUSR

The accuracy of the estimated stellar atmospheric parameter evidently decreases with the decreasing of spectral signal-to-noise ratio (S/N) and there are a huge amount of this kind observations, especially in case of S/N < 30. Therefore, it is helpful to improve the parameter estimation performance for these spectra and this work studied the (T eff , log g, [Fe/H]) estimation problem for LAMOST DR8 low-resolution spectra with 20 ≤ S/N < 30. We proposed a data-driven method based on machine learning techniques. First, this scheme detected stellar atmospheric parameter-sensitive features from spectra by the Least Absolute Shrinkage and Selection Operator (LASSO), rejected ineffective data components and irrelevant data. Second, a Multi-layer Perceptron (MLP) method was used to estimate stellar atmospheric parameters from the LASSO features. Finally, the performance of the LASSO-MLP was evaluated by computing and analyzing the consistency between its estimation and the reference from the Apache Point Observatory Galactic Evolution Experiment high-resolution spectra. Experiments show that the Mean Absolute Errors of T eff , log g, [Fe/H] are reduced from the LASP (137.6 K, 0.195, 0.091 dex) to LASSO-MLP (84.32 K, 0.137, 0.063 dex), which indicate evident improvements on stellar atmospheric parameter estimation. In addition, this work estimated the stellar atmospheric parameters for 1,162,760 low-resolution spectra with 20 ≤ S/N < 30 from LAMOST DR8 using LASSO-MLP, and released the estimation catalog, learned model, experimental code, trained model, training data and test data for scientific exploration and algorithm study.