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 (Teff, 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 Teff, 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.

Xiangru Li;Zhu Wang;Si Zeng;Caixiu Liao;Bing Du;Xiao Kong;Haining Li

School of Computer Science,South China Normal University,Guangzhou 510631,China;School of Mathematical Sciences,South China Normal University,Guangzhou 510631,China;Key Laboratory of Optical Astronomy,National Astronomical Observatories,Chinese Academy of Sciences,Beijing 100101,China;University of Chinese Academy of Sciences,Beijing 100049,China

天文和天体物理学研究

[1]Xiangru Li;Zhu Wang;Si Zeng;Caixiu Liao;Bing Du;Xiao Kong;Haining Li-.Estimation of Stellar Atmospheric Parameters from LAMOST DR8 Low-resolution Spectra with 20≤S/N<30)[J].天文和天体物理学研究,2022(06):204-214

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